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z?maiLmi?SQvVmg?rk_ji%SZM7cilKkC5mYtzycdSXMjcE=CeYTf(shngL9RiR)Oav z>tP32VRHbjXyIGt?TQkUw+^-CBLF`Ts?s2sPFbh^!<&XI`-BM*o#=V=oxB5$(v>R* z+S+6rf~}|FQHNLXGZdQ1?MU^?zIjIOX;%5)__pJmYO#S&~tkoihB3w-mi) zpl5@XveRUcR9Fe5$cb5_1X;}fPxKR+&W(ZQjH80M`NBou+lb*UxeSrT#uqb>Nod6X zGEr2&w6nJxduQ_EZJYOekc_6dDZQ;z<>j2_*8;!Rr!d=6GF^JofzPU~gt(vhD6W#6 zWncc91i*T@iXrwB`6{whwBzLL-DHPz4agxiUMA@1CT5gAvmsXLgmN5PPKLF|{0@DBh diff --git a/docs/_modules/index.html b/docs/_modules/index.html index 97c8bd93..895b27d7 100644 --- a/docs/_modules/index.html +++ b/docs/_modules/index.html @@ -1,9 +1,9 @@ - - + + @@ -12,18 +12,15 @@ - Overview: module code — jMetalPy 1.5.3 documentation - - - - - - - - - - - + Overview: module code — jMetalPy 1.7.0 documentation + + + + + + + + @@ -39,7 +36,8 @@

Navigation

 -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Overview: module code
    • @@ -50,7 +48,7 @@

    Navigation

    @@ -72,6 +70,8 @@

    Table Of Contents

  • Single-objective algorithms
  • Operators
  • Problems
    • +
  • Contributing
    • +
  • About
    • @@ -112,6 +112,7 @@

    All modules for which code is available

  • jmetal.algorithm.multiobjective.mocell
  • jmetal.algorithm.multiobjective.moead
  • jmetal.algorithm.multiobjective.nsgaii
    • +
  • jmetal.algorithm.multiobjective.nsgaiii
  • jmetal.algorithm.multiobjective.omopso
  • jmetal.algorithm.multiobjective.smpso
  • jmetal.algorithm.multiobjective.spea2
    • @@ -162,7 +163,8 @@

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  • jMetalPy 1.5.3 documentation »
    • +
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    • +
  • Overview: module code
    • - - - - - - - + jmetal.algorithm.multiobjective.gde3 — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Module code »
    • +
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  • Module code »
    • +
  • jmetal.algorithm.multiobjective.gde3
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -109,39 +106,43 @@

    Table Of Contents

    Source code for jmetal.algorithm.multiobjective.gde3

    -from typing import TypeVar, List
+from typing import List, TypeVar
 
 from jmetal.config import store
-from jmetal.core.algorithm import EvolutionaryAlgorithm, DynamicAlgorithm
-from jmetal.core.problem import Problem, DynamicProblem
+from jmetal.core.algorithm import DynamicAlgorithm, EvolutionaryAlgorithm
+from jmetal.core.problem import DynamicProblem, Problem
 from jmetal.core.solution import FloatSolution
-from jmetal.operator import DifferentialEvolutionCrossover, RankingAndCrowdingDistanceSelection
+from jmetal.operator.crossover import DifferentialEvolutionCrossover
+from jmetal.operator.selection import DifferentialEvolutionSelection
+
 from jmetal.operator.selection import DifferentialEvolutionSelection
 from jmetal.util.comparator import Comparator, DominanceComparator
 from jmetal.util.evaluator import Evaluator
 from jmetal.util.generator import Generator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-S = TypeVar('S')
+S = TypeVar("S")
 R = List[S]
 
 
-
    [docs]class GDE3(EvolutionaryAlgorithm[FloatSolution, FloatSolution]):
-
-    def __init__(self,
-                 problem: Problem,
-                 population_size: int,
-                 cr: float,
-                 f: float,
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 k: float = 0.5,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator,
-                 dominance_comparator: Comparator = store.default_comparator):
+
    
+[docs]
+class GDE3(EvolutionaryAlgorithm[FloatSolution, FloatSolution]):
+    def __init__(
+        self,
+        problem: Problem,
+        population_size: int,
+        cr: float,
+        f: float,
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        k: float = 0.5,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+        dominance_comparator: Comparator = store.default_comparator,
+    ):
         super(GDE3, self).__init__(
-            problem=problem,
-            population_size=population_size,
-            offspring_population_size=population_size)
+            problem=problem, population_size=population_size, offspring_population_size=population_size
+        )
         self.dominance_comparator = dominance_comparator
         self.selection_operator = DifferentialEvolutionSelection()
         self.crossover_operator = DifferentialEvolutionCrossover(cr, f, k)
@@ -152,7 +153,9 @@ 
    Source code for jmetal.algorithm.multiobjective.gde3
    self.termination_criterion = termination_criterion
         self.observable.register(termination_criterion)
 
-
    [docs]    def selection(self, population: List[FloatSolution]) -> List[FloatSolution]:
+
    
+[docs]
+    def selection(self, population: List[FloatSolution]) -> List[FloatSolution]:
         mating_pool = []
 
         for i in range(self.population_size):
@@ -162,20 +165,26 @@ 
    Source code for jmetal.algorithm.multiobjective.gde3
    return mating_pool
    
 
-
    [docs]    def reproduction(self, mating_pool: List[S]) -> List[S]:
+
+
    
+[docs]
+    def reproduction(self, mating_pool: List[S]) -> List[S]:
         offspring_population = []
         first_parent_index = 0
 
         for solution in self.solutions:
             self.crossover_operator.current_individual = solution
-            parents = mating_pool[first_parent_index:first_parent_index + 3]
+            parents = mating_pool[first_parent_index : first_parent_index + 3]
             first_parent_index += 3
 
             offspring_population.append(self.crossover_operator.execute(parents)[0])
 
         return offspring_population
    
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[FloatSolution]) -> List[List[FloatSolution]]:
+
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[FloatSolution]) -> List[List[FloatSolution]]:
         tmp_list = []
 
         for solution1, solution2 in zip(self.solutions, offspring_population):
@@ -194,62 +203,100 @@ 
    Source code for jmetal.algorithm.multiobjective.gde3
    self.population_size, dominance_comparator=self.dominance_comparator
         ).execute(join_population)
    
 
-
    [docs]    def create_initial_solutions(self) -> List[FloatSolution]:
+
+
    
+[docs]
+    def create_initial_solutions(self) -> List[FloatSolution]:
         return [self.population_generator.new(self.problem) for _ in range(self.population_size)]
    
 
-
    [docs]    def evaluate(self, solution_list: List[FloatSolution]) -> List[FloatSolution]:
+
+
    
+[docs]
+    def evaluate(self, solution_list: List[FloatSolution]) -> List[FloatSolution]:
         return self.population_evaluator.evaluate(solution_list, self.problem)
    
 
-
    [docs]    def stopping_condition_is_met(self) -> bool:
+
+
    
+[docs]
+    def stopping_condition_is_met(self) -> bool:
         return self.termination_criterion.is_met
    
 
-
    [docs]    def get_result(self) -> List[FloatSolution]:
+
+
    
+[docs]
+    def result(self) -> List[FloatSolution]:
         return self.solutions
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'GDE3'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "GDE3"
    
+
    
 
 
-
    [docs]class DynamicGDE3(GDE3, DynamicAlgorithm):
 
-    def __init__(self,
-                 problem: DynamicProblem,
-                 population_size: int,
-                 cr: float,
-                 f: float,
-                 termination_criterion: TerminationCriterion,
-                 k: float = 0.5,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator,
-                 dominance_comparator: Comparator = DominanceComparator()):
+
    
+[docs]
+class DynamicGDE3(GDE3, DynamicAlgorithm):
+    def __init__(
+        self,
+        problem: DynamicProblem,
+        population_size: int,
+        cr: float,
+        f: float,
+        termination_criterion: TerminationCriterion,
+        k: float = 0.5,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+        dominance_comparator: Comparator = DominanceComparator(),
+    ):
         super(DynamicGDE3, self).__init__(
-            problem, population_size, cr, f, termination_criterion, k,
-            population_generator, population_evaluator, dominance_comparator)
+            problem,
+            population_size,
+            cr,
+            f,
+            termination_criterion,
+            k,
+            population_generator,
+            population_evaluator,
+            dominance_comparator,
+        )
 
         self.completed_iterations = 0
 
-
    [docs]    def restart(self) -> None:
+
    
+[docs]
+    def restart(self) -> None:
         pass
    
 
-
    [docs]    def update_progress(self):
+
+
    
+[docs]
+    def update_progress(self):
         if self.problem.the_problem_has_changed():
             self.restart()
             self.problem.clear_changed()
 
-        observable_data = self.get_observable_data()
+        observable_data = self.observable_data()
         self.observable.notify_all(**observable_data)
 
         self.evaluations += self.offspring_population_size
    
 
-
    [docs]    def stopping_condition_is_met(self):
+
+
    
+[docs]
+    def stopping_condition_is_met(self):
         if self.termination_criterion.is_met:
-            observable_data = self.get_observable_data()
+            observable_data = self.observable_data()
             self.observable.notify_all(**observable_data)
 
             self.restart()
             self.init_progress()
 
-            self.completed_iterations += 1
    
+            self.completed_iterations += 1
    
+
    
+
    @@ -266,8 +313,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Module code »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Module code »
    • +
  • jmetal.algorithm.multiobjective.gde3
    • - - - - - - - + jmetal.algorithm.multiobjective.hype — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

    Navigation

  • modules |
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  • jMetalPy 1.5.3 documentation »
    • -
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    • +
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    • +
  • jmetal.algorithm.multiobjective.hype
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -109,39 +106,41 @@

    Table Of Contents

    Source code for jmetal.algorithm.multiobjective.hype

    -from typing import TypeVar, List
+from typing import List, TypeVar
 
 from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
 from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover
+from jmetal.core.operator import Crossover, Mutation
 from jmetal.core.problem import Problem
 from jmetal.core.solution import Solution
-from jmetal.operator import BinaryTournamentSelection
+from jmetal.operator.selection import BinaryTournamentSelection
 from jmetal.operator.selection import RankingAndFitnessSelection
-from jmetal.util.comparator import Comparator
-from jmetal.util.comparator import SolutionAttributeComparator
+from jmetal.util.comparator import Comparator, SolutionAttributeComparator
 from jmetal.util.evaluator import Evaluator
 from jmetal.util.generator import Generator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-S = TypeVar('S')
-R = TypeVar('R')
-
-
-
    [docs]class HYPE(GeneticAlgorithm[S, R]):
-
-    def __init__(self,
-                 problem: Problem,
-                 reference_point: Solution,
-                 population_size: int,
-                 offspring_population_size: int,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator,
-                 dominance_comparator: Comparator = store.default_comparator):
-        """ This is an implementation of the Hypervolume Estimation Algorithm for Multi-objective Optimization
+S = TypeVar("S")
+R = TypeVar("R")
+
+
+
    
+[docs]
+class HYPE(GeneticAlgorithm[S, R]):
+    def __init__(
+        self,
+        problem: Problem,
+        reference_point: Solution,
+        population_size: int,
+        offspring_population_size: int,
+        mutation: Mutation,
+        crossover: Crossover,
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+        dominance_comparator: Comparator = store.default_comparator,
+    ):
+        """This is an implementation of the Hypervolume Estimation Algorithm for Multi-objective Optimization
         proposed in:
 
         * J. Bader and E. Zitzler. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective
@@ -160,10 +159,11 @@ 
    Source code for jmetal.algorithm.multiobjective.hype
            """
 
         selection = BinaryTournamentSelection(
-            comparator=SolutionAttributeComparator(key='fitness', lowest_is_best=False))
-        self.ranking_fitness = RankingAndFitnessSelection(population_size,
-                                                          dominance_comparator=dominance_comparator,
-                                                          reference_point=reference_point)
+            comparator=SolutionAttributeComparator(key="fitness", lowest_is_best=False)
+        )
+        self.ranking_fitness = RankingAndFitnessSelection(
+            population_size, dominance_comparator=dominance_comparator, reference_point=reference_point
+        )
         self.reference_point = reference_point
         self.dominance_comparator = dominance_comparator
 
@@ -176,24 +176,38 @@ 
    Source code for jmetal.algorithm.multiobjective.hype
    selection=selection,
             termination_criterion=termination_criterion,
             population_evaluator=population_evaluator,
-            population_generator=population_generator
+            population_generator=population_generator,
         )
 
-
    [docs]    def evaluate(self, population: List[S]):
+
    
+[docs]
+    def evaluate(self, population: List[S]):
         population = self.population_evaluator.evaluate(population, self.problem)
-        population = self.ranking_fitness.compute_hypervol_fitness_values(population, self.reference_point,
-                                                                          len(population))
+        population = self.ranking_fitness.compute_hypervol_fitness_values(
+            population, self.reference_point, len(population)
+        )
         return population
    
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
+
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
         join_population = population + offspring_population
         return self.ranking_fitness.execute(join_population)
    
 
-
    [docs]    def get_result(self) -> R:
+
+
    
+[docs]
+    def result(self) -> R:
         return self.solutions
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'HYPE'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "HYPE"
    
+
    
+
    @@ -210,8 +224,9 @@

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  • jmetal.algorithm.multiobjective.hype
    • - - - - - - - + jmetal.algorithm.multiobjective.ibea — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.algorithm.multiobjective.ibea
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -109,38 +106,41 @@

    Table Of Contents

    Source code for jmetal.algorithm.multiobjective.ibea

    -from typing import TypeVar, List
+from typing import List, TypeVar
 
 import numpy as np
 
 from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
 from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover
+from jmetal.core.operator import Crossover, Mutation
 from jmetal.core.problem import Problem
 from jmetal.core.quality_indicator import EpsilonIndicator
-from jmetal.operator import BinaryTournamentSelection
+from jmetal.operator.selection import BinaryTournamentSelection
 from jmetal.util.comparator import SolutionAttributeComparator
 from jmetal.util.evaluator import Evaluator
 from jmetal.util.generator import Generator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-S = TypeVar('S')
-R = TypeVar('R')
-
+S = TypeVar("S")
+R = TypeVar("R")
 
-
    [docs]class IBEA(GeneticAlgorithm[S, R]):
 
-    def __init__(self,
-                 problem: Problem,
-                 population_size: int,
-                 offspring_population_size: int,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 kappa: float,
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator):
-        """  Epsilon IBEA implementation as described in
+
    
+[docs]
+class IBEA(GeneticAlgorithm[S, R]):
+    def __init__(
+        self,
+        problem: Problem,
+        population_size: int,
+        offspring_population_size: int,
+        mutation: Mutation,
+        crossover: Crossover,
+        kappa: float,
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+    ):
+        """Epsilon IBEA implementation as described in
 
         * Zitzler, Eckart, and Simon Künzli. "Indicator-based selection in multiobjective search."
         In International Conference on Parallel Problem Solving from Nature, pp. 832-842. Springer,
@@ -161,7 +161,8 @@ 
    Source code for jmetal.algorithm.multiobjective.ibea
            """
 
         selection = BinaryTournamentSelection(
-            comparator=SolutionAttributeComparator(key='fitness', lowest_is_best=False))
+            comparator=SolutionAttributeComparator(key="fitness", lowest_is_best=False)
+        )
         self.kappa = kappa
 
         super(IBEA, self).__init__(
@@ -173,48 +174,69 @@ 
    Source code for jmetal.algorithm.multiobjective.ibea
    selection=selection,
             termination_criterion=termination_criterion,
             population_evaluator=population_evaluator,
-            population_generator=population_generator
+            population_generator=population_generator,
         )
 
-
    [docs]    def compute_fitness_values(self, population: List[S], kappa: float) -> List[S]:
+
    
+[docs]
+    def compute_fitness_values(self, population: List[S], kappa: float) -> List[S]:
         for i in range(len(population)):
-            population[i].attributes['fitness'] = 0
+            population[i].attributes["fitness"] = 0
 
             for j in range(len(population)):
                 if j != i:
-                    population[i].attributes['fitness'] += -np.exp(
-                        -EpsilonIndicator([population[i]]).compute([population[j]]) / self.kappa)
+                    population[i].attributes["fitness"] += -np.exp(
+                        -EpsilonIndicator([population[i].objectives]).compute([population[j].objectives]) / self.kappa
+                    )
         return population
    
 
-
    [docs]    def create_initial_solutions(self) -> List[S]:
+
+
    
+[docs]
+    def create_initial_solutions(self) -> List[S]:
         population = [self.population_generator.new(self.problem) for _ in range(self.population_size)]
         population = self.compute_fitness_values(population, self.kappa)
 
         return population
    
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
+
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
         join_population = population + offspring_population
         join_population_size = len(join_population)
         join_population = self.compute_fitness_values(join_population, self.kappa)
 
         while join_population_size > self.population_size:
-            current_fitnesses = [individual.attributes['fitness'] for individual in join_population]
+            current_fitnesses = [individual.attributes["fitness"] for individual in join_population]
             index_worst = current_fitnesses.index(min(current_fitnesses))
 
             for i in range(join_population_size):
-                join_population[i].attributes['fitness'] += np.exp(
-                    - EpsilonIndicator([join_population[i]]).compute([join_population[index_worst]]) / self.kappa)
+                join_population[i].attributes["fitness"] += np.exp(
+                    -EpsilonIndicator([join_population[i].objectives]).compute(
+                        [join_population[index_worst].objectives]
+                    )
+                    / self.kappa
+                )
 
             join_population.pop(index_worst)
             join_population_size = join_population_size - 1
 
         return join_population
    
 
-
    [docs]    def get_result(self) -> R:
+
+
    
+[docs]
+    def result(self) -> R:
         return self.solutions
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Epsilon-IBEA'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Epsilon-IBEA"
    
+
    
+
    @@ -231,8 +253,9 @@

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  • jmetal.algorithm.multiobjective.ibea
    • - - - - - - - + jmetal.algorithm.multiobjective.mocell — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.algorithm.multiobjective.mocell
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -111,24 +108,24 @@

    Table Of Contents

    Source code for jmetal.algorithm.multiobjective.mocell

     import copy
 from functools import cmp_to_key
-from typing import TypeVar, List
+from typing import List, TypeVar
 
 from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
 from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover, Selection
+from jmetal.core.operator import Crossover, Mutation, Selection
 from jmetal.core.problem import Problem
-from jmetal.operator import BinaryTournamentSelection
+from jmetal.operator.selection import BinaryTournamentSelection
 from jmetal.util.archive import BoundedArchive
+from jmetal.util.comparator import Comparator, MultiComparator
 from jmetal.util.density_estimator import CrowdingDistance, DensityEstimator
 from jmetal.util.evaluator import Evaluator
 from jmetal.util.generator import Generator
 from jmetal.util.neighborhood import Neighborhood
 from jmetal.util.ranking import FastNonDominatedRanking, Ranking
-from jmetal.util.comparator import Comparator, MultiComparator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-S = TypeVar('S')
-R = TypeVar('R')
+S = TypeVar("S")
+R = TypeVar("R")
 
 """
 .. module:: MOCell
@@ -138,23 +135,26 @@ 
    Source code for jmetal.algorithm.multiobjective.mocell
    """
 
 
-
    [docs]class MOCell(GeneticAlgorithm[S, R]):
-
-    def __init__(self,
-                 problem: Problem,
-                 population_size: int,
-                 neighborhood: Neighborhood,
-                 archive: BoundedArchive,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 selection: Selection = BinaryTournamentSelection(
-                     MultiComparator([FastNonDominatedRanking.get_comparator(),
-                                      CrowdingDistance.get_comparator()])),
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator,
-                 dominance_comparator: Comparator = store.default_comparator):
-        """
+
    
+[docs]
+class MOCell(GeneticAlgorithm[S, R]):
+    def __init__(
+        self,
+        problem: Problem,
+        population_size: int,
+        neighborhood: Neighborhood,
+        archive: BoundedArchive,
+        mutation: Mutation,
+        crossover: Crossover,
+        selection: Selection = BinaryTournamentSelection(
+            MultiComparator([FastNonDominatedRanking.get_comparator(), CrowdingDistance.get_comparator()])
+        ),
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+        dominance_comparator: Comparator = store.default_comparator,
+    ):
+        """
         MOCEll implementation as described in:
 
         :param problem: The problem to solve.
@@ -172,7 +172,7 @@ 
    Source code for jmetal.algorithm.multiobjective.mocell
    selection=selection,
             termination_criterion=termination_criterion,
             population_evaluator=population_evaluator,
-            population_generator=population_generator
+            population_generator=population_generator,
         )
         self.dominance_comparator = dominance_comparator
         self.neighborhood = neighborhood
@@ -180,19 +180,26 @@ 
    Source code for jmetal.algorithm.multiobjective.mocell
    self.current_individual = 0
         self.current_neighbors = []
 
-        self.comparator = MultiComparator([FastNonDominatedRanking.get_comparator(),
-                                           CrowdingDistance.get_comparator()])
+        self.comparator = MultiComparator([FastNonDominatedRanking.get_comparator(), CrowdingDistance.get_comparator()])
 
-
    [docs]    def init_progress(self) -> None:
+
    
+[docs]
+    def init_progress(self) -> None:
         super().init_progress()
         for solution in self.solutions:
             self.archive.add(copy.copy(solution))
    
 
-
    [docs]    def update_progress(self) -> None:
+
+
    
+[docs]
+    def update_progress(self) -> None:
         super().update_progress()
         self.current_individual = (self.current_individual + 1) % self.population_size
    
 
-
    [docs]    def selection(self, population: List[S]):
+
+
    
+[docs]
+    def selection(self, population: List[S]):
         parents = []
 
         self.current_neighbors = self.neighborhood.get_neighbors(self.current_individual, population)
@@ -206,23 +213,29 @@ 
    Source code for jmetal.algorithm.multiobjective.mocell
    return parents
    
 
-
    [docs]    def reproduction(self, mating_population: List[S]) -> List[S]:
+
+
    
+[docs]
+    def reproduction(self, mating_population: List[S]) -> List[S]:
         number_of_parents_to_combine = self.crossover_operator.get_number_of_parents()
 
         if len(mating_population) % number_of_parents_to_combine != 0:
-            raise Exception('Wrong number of parents')
+            raise Exception("Wrong number of parents")
 
         offspring_population = self.crossover_operator.execute(mating_population)
-        self.mutation_operator.execute(offspring_population[0])
+        offspring_population[0] = self.mutation_operator.execute(offspring_population[0])
 
         return [offspring_population[0]]
    
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
+
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
         result = self.dominance_comparator.compare(population[self.current_individual], offspring_population[0])
 
         if result == 1:  # the offspring individual dominates the current one
             population[self.current_individual] = offspring_population[0]
-            self.archive.add(offspring_population[0])
+            self.archive.add(copy.deepcopy(offspring_population[0]))
         elif result == 0:  # the offspring and current individuals are non-dominated
             new_individual = offspring_population[0]
 
@@ -238,17 +251,25 @@ 
    Source code for jmetal.algorithm.multiobjective.mocell
    self.current_neighbors.sort(key=cmp_to_key(self.comparator.compare))
             worst_solution = self.current_neighbors[-1]
 
-            self.archive.add(new_individual)
+            self.archive.add(copy.deepcopy(new_individual))
             if worst_solution != new_individual:
                 population[self.current_individual] = new_individual
 
         return population
    
 
-
    [docs]    def get_result(self) -> R:
+
+
    
+[docs]
+    def result(self) -> R:
         return self.archive.solution_list
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'MOCell'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "MOCell"
    
+
    
+
    @@ -265,8 +286,9 @@

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  • jmetal.algorithm.multiobjective.mocell
    • - - - - - - - + jmetal.algorithm.multiobjective.moead — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.algorithm.multiobjective.moead
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -112,7 +109,7 @@

    Source code for jmetal.algorithm.multiobjective.moead

    import copy import random from math import ceil -from typing import TypeVar, List, Generator +from typing import Generator, List, TypeVar import numpy as np @@ -120,36 +117,46 @@

    Source code for jmetal.algorithm.multiobjective.moead

    from jmetal.config import store from jmetal.core.operator import Mutation from jmetal.core.problem import Problem -from jmetal.operator import DifferentialEvolutionCrossover, NaryRandomSolutionSelection -from jmetal.util.aggregative_function import AggregativeFunction -from jmetal.util.constraint_handling import feasibility_ratio, \ - overall_constraint_violation_degree, is_feasible +from jmetal.operator.crossover import DifferentialEvolutionCrossover +from jmetal.operator.selection import NaryRandomSolutionSelection +from jmetal.util.aggregation_function import AggregationFunction +from jmetal.util.constraint_handling import ( + feasibility_ratio, + is_feasible, + overall_constraint_violation_degree, +) from jmetal.util.density_estimator import CrowdingDistance from jmetal.util.evaluator import Evaluator from jmetal.util.neighborhood import WeightVectorNeighborhood from jmetal.util.ranking import FastNonDominatedRanking -from jmetal.util.termination_criterion import TerminationCriterion, StoppingByEvaluations +from jmetal.util.termination_criterion import ( + StoppingByEvaluations, + TerminationCriterion, +) -S = TypeVar('S') +S = TypeVar("S") R = List[S] -
    [docs]class MOEAD(GeneticAlgorithm): - - def __init__(self, - problem: Problem, - population_size: int, - mutation: Mutation, - crossover: DifferentialEvolutionCrossover, - aggregative_function: AggregativeFunction, - neighbourhood_selection_probability: float, - max_number_of_replaced_solutions: int, - neighbor_size: int, - weight_files_path: str, - termination_criterion: TerminationCriterion = store.default_termination_criteria, - population_generator: Generator = store.default_generator, - population_evaluator: Evaluator = store.default_evaluator): - """ +
    +[docs] +class MOEAD(GeneticAlgorithm): + def __init__( + self, + problem: Problem, + population_size: int, + mutation: Mutation, + crossover: DifferentialEvolutionCrossover, + aggregation_function: AggregationFunction, + neighbourhood_selection_probability: float, + max_number_of_replaced_solutions: int, + neighbor_size: int, + weight_files_path: str, + termination_criterion: TerminationCriterion = store.default_termination_criteria, + population_generator: Generator = store.default_generator, + population_evaluator: Evaluator = store.default_evaluator, + ): + """ :param max_number_of_replaced_solutions: (eta in Zhang & Li paper). :param neighbourhood_selection_probability: Probability of mating with a solution in the neighborhood rather than the entire population (Delta in Zhang & Li paper). @@ -163,36 +170,41 @@

    Source code for jmetal.algorithm.multiobjective.moead

    selection=NaryRandomSolutionSelection(2), population_evaluator=population_evaluator, population_generator=population_generator, - termination_criterion=termination_criterion + termination_criterion=termination_criterion, ) self.max_number_of_replaced_solutions = max_number_of_replaced_solutions - self.fitness_function = aggregative_function + self.fitness_function = aggregation_function self.neighbourhood = WeightVectorNeighborhood( number_of_weight_vectors=population_size, neighborhood_size=neighbor_size, - weight_vector_size=problem.number_of_objectives, - weights_path=weight_files_path + weight_vector_size=problem.number_of_objectives(), + weights_path=weight_files_path, ) self.neighbourhood_selection_probability = neighbourhood_selection_probability self.permutation = None self.current_subproblem = 0 self.neighbor_type = None -
    [docs] def init_progress(self) -> None: +
    +[docs] + def init_progress(self) -> None: self.evaluations = self.population_size for solution in self.solutions: self.fitness_function.update(solution.objectives) self.permutation = Permutation(self.population_size) - observable_data = self.get_observable_data() + observable_data = self.observable_data() self.observable.notify_all(**observable_data)
    -
    [docs] def selection(self, population: List[S]): + +
    +[docs] + def selection(self, population: List[S]): self.current_subproblem = self.permutation.get_next_value() self.neighbor_type = self.choose_neighbor_type() - if self.neighbor_type == 'NEIGHBOR': + if self.neighbor_type == "NEIGHBOR": neighbors = self.neighbourhood.get_neighbors(self.current_subproblem, population) mating_population = self.selection_operator.execute(neighbors) else: @@ -202,7 +214,10 @@

    Source code for jmetal.algorithm.multiobjective.moead

    return mating_population
    -
    [docs] def reproduction(self, mating_population: List[S]) -> List[S]: + +
    +[docs] + def reproduction(self, mating_population: List[S]) -> List[S]: self.crossover_operator.current_individual = self.solutions[self.current_subproblem] offspring_population = self.crossover_operator.execute(mating_population) @@ -210,7 +225,10 @@

    Source code for jmetal.algorithm.multiobjective.moead

    return offspring_population
    -
    [docs] def replacement(self, population: List[S], offspring_population: List[S]) -> List[S]: + +
    +[docs] + def replacement(self, population: List[S], offspring_population: List[S]) -> List[S]: new_solution = offspring_population[0] self.fitness_function.update(new_solution.objectives) @@ -219,7 +237,10 @@

    Source code for jmetal.algorithm.multiobjective.moead

    return new_population
    -
    [docs] def update_current_subproblem_neighborhood(self, new_solution, population): + +
    +[docs] + def update_current_subproblem_neighborhood(self, new_solution, population): permuted_neighbors_indexes = self.generate_permutation_of_neighbors(self.current_subproblem) replacements = 0 @@ -238,8 +259,11 @@

    Source code for jmetal.algorithm.multiobjective.moead

    return population
    -
    [docs] def generate_permutation_of_neighbors(self, subproblem_id): - if self.neighbor_type == 'NEIGHBOR': + +
    +[docs] + def generate_permutation_of_neighbors(self, subproblem_id): + if self.neighbor_type == "NEIGHBOR": neighbors = self.neighbourhood.get_neighborhood()[subproblem_id] permuted_array = copy.deepcopy(neighbors.tolist()) else: @@ -247,34 +271,64 @@

    Source code for jmetal.algorithm.multiobjective.moead

    return permuted_array
    -
    [docs] def choose_neighbor_type(self): + +
    +[docs] + def choose_neighbor_type(self): rnd = random.random() if rnd < self.neighbourhood_selection_probability: - neighbor_type = 'NEIGHBOR' + neighbor_type = "NEIGHBOR" else: - neighbor_type = 'POPULATION' + neighbor_type = "POPULATION" return neighbor_type
    -
    [docs] def get_name(self): - return 'MOEAD'
    -
    [docs] def get_result(self): - return self.solutions
    +
    +[docs] + def get_name(self): + return "MOEAD"
    + + +
    +[docs] + def result(self): + return self.solutions
    +
    + class MOEAD_DRA(MOEAD): - def __init__(self, problem, population_size, mutation, crossover, aggregative_function, - neighbourhood_selection_probability, max_number_of_replaced_solutions, neighbor_size, - weight_files_path, termination_criterion=store.default_termination_criteria, - population_generator=store.default_generator, population_evaluator=store.default_evaluator): - super(MOEAD_DRA, self).__init__(problem, population_size, mutation, crossover, aggregative_function, - neighbourhood_selection_probability, max_number_of_replaced_solutions, - neighbor_size, weight_files_path, - termination_criterion=termination_criterion, - population_generator=population_generator, - population_evaluator=population_evaluator) + def __init__( + self, + problem, + population_size, + mutation, + crossover, + aggregation_function, + neighbourhood_selection_probability, + max_number_of_replaced_solutions, + neighbor_size, + weight_files_path, + termination_criterion=store.default_termination_criteria, + population_generator=store.default_generator, + population_evaluator=store.default_evaluator, + ): + super(MOEAD_DRA, self).__init__( + problem, + population_size, + mutation, + crossover, + aggregation_function, + neighbourhood_selection_probability, + max_number_of_replaced_solutions, + neighbor_size, + weight_files_path, + termination_criterion=termination_criterion, + population_generator=population_generator, + population_evaluator=population_evaluator, + ) self.saved_values = [] self.utility = [1.0 for _ in range(population_size)] @@ -294,7 +348,7 @@

    Source code for jmetal.algorithm.multiobjective.moead

    self.order = self.__tour_selection(10) self.current_order_index = 0 - observable_data = self.get_observable_data() + observable_data = self.observable_data() self.observable.notify_all(**observable_data) def update_progress(self): @@ -316,7 +370,7 @@

    Source code for jmetal.algorithm.multiobjective.moead

    self.neighbor_type = self.choose_neighbor_type() - if self.neighbor_type == 'NEIGHBOR': + if self.neighbor_type == "NEIGHBOR": neighbors = self.neighbourhood.get_neighbors(self.current_subproblem, population) mating_population = self.selection_operator.execute(neighbors) else: @@ -327,7 +381,7 @@

    Source code for jmetal.algorithm.multiobjective.moead

    return mating_population def get_name(self): - return 'MOEAD-DRA' + return "MOEAD-DRA" def __utility_function(self): for i in range(len(self.solutions)): @@ -343,8 +397,8 @@

    Source code for jmetal.algorithm.multiobjective.moead

    self.saved_values[i] = copy.copy(self.solutions[i]) def __tour_selection(self, depth): - selected = [i for i in range(self.problem.number_of_objectives)] - candidate = [i for i in range(self.problem.number_of_objectives, self.population_size)] + selected = [i for i in range(self.problem.number_of_objectives())] + candidate = [i for i in range(self.problem.number_of_objectives(), self.population_size)] while len(selected) < int(self.population_size / 5.0): best_idd = int(random.random() * len(candidate)) @@ -362,20 +416,22 @@

    Source code for jmetal.algorithm.multiobjective.moead

    class MOEADIEpsilon(MOEAD): - def __init__(self, - problem: Problem, - population_size: int, - mutation: Mutation, - crossover: DifferentialEvolutionCrossover, - aggregative_function: AggregativeFunction, - neighbourhood_selection_probability: float, - max_number_of_replaced_solutions: int, - neighbor_size: int, - weight_files_path: str, - termination_criterion: TerminationCriterion = StoppingByEvaluations(300000), - population_generator: Generator = store.default_generator, - population_evaluator: Evaluator = store.default_evaluator): - """ + def __init__( + self, + problem: Problem, + population_size: int, + mutation: Mutation, + crossover: DifferentialEvolutionCrossover, + aggregation_function: AggregationFunction, + neighbourhood_selection_probability: float, + max_number_of_replaced_solutions: int, + neighbor_size: int, + weight_files_path: str, + termination_criterion: TerminationCriterion = StoppingByEvaluations(300000), + population_generator: Generator = store.default_generator, + population_evaluator: Evaluator = store.default_evaluator, + ): + """ :param max_number_of_replaced_solutions: (eta in Zhang & Li paper). :param neighbourhood_selection_probability: Probability of mating with a solution in the neighborhood rather than the entire population (Delta in Zhang & Li paper). @@ -385,14 +441,14 @@

    Source code for jmetal.algorithm.multiobjective.moead

    population_size=population_size, mutation=mutation, crossover=crossover, - aggregative_function=aggregative_function, + aggregation_function=aggregation_function, neighbourhood_selection_probability=neighbourhood_selection_probability, max_number_of_replaced_solutions=max_number_of_replaced_solutions, neighbor_size=neighbor_size, weight_files_path=weight_files_path, population_evaluator=population_evaluator, population_generator=population_generator, - termination_criterion=termination_criterion + termination_criterion=termination_criterion, ) self.constraints = [] self.epsilon_k = 0 @@ -409,8 +465,9 @@

    Source code for jmetal.algorithm.multiobjective.moead

    # for i in range(self.population_size): # self.constraints[i] = get_overall_constraint_violation_degree(self.permutation[i]) - self.constraints = [overall_constraint_violation_degree(self.solutions[i]) - for i in range(0, self.population_size)] + self.constraints = [ + overall_constraint_violation_degree(self.solutions[i]) for i in range(0, self.population_size) + ] sorted(self.constraints) self.epsilon_zero = abs(self.constraints[int(ceil(0.05 * self.population_size))]) @@ -489,20 +546,20 @@

    Source code for jmetal.algorithm.multiobjective.moead

    crowding_distance = CrowdingDistance() while len(first_rank_solutions) > self.population_size: crowding_distance.compute_density_estimator(first_rank_solutions) - first_rank_solutions = sorted(first_rank_solutions, key=lambda x: x.attributes['crowding_distance'], - reverse=True) + first_rank_solutions = sorted( + first_rank_solutions, key=lambda x: x.attributes["crowding_distance"], reverse=True + ) first_rank_solutions.pop() self.archive = [] for solution in first_rank_solutions: self.archive.append(copy.deepcopy(solution)) - def get_result(self): + def result(self): return self.archive class Permutation: - def __init__(self, length: int): self.counter = 0 self.length = length @@ -536,8 +593,9 @@

    Navigation

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    • -
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    • +
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    • +
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    • +
  • jmetal.algorithm.multiobjective.moead
    • - - - - - - - + jmetal.algorithm.multiobjective.nsgaii — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

    Navigation

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  • jmetal.algorithm.multiobjective.nsgaii
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -110,29 +107,32 @@

    Table Of Contents

    Source code for jmetal.algorithm.multiobjective.nsgaii

     import time
-from typing import TypeVar, List, Generator
+from typing import Generator, List, TypeVar
 
 try:
     import dask
-    from distributed import as_completed, Client
+    from distributed import Client, as_completed
 except ImportError:
     pass
 
 from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
 from jmetal.config import store
-from jmetal.core.algorithm import DynamicAlgorithm, Algorithm
-from jmetal.core.operator import Mutation, Crossover, Selection
-from jmetal.core.problem import Problem, DynamicProblem
-from jmetal.operator import BinaryTournamentSelection
+from jmetal.core.algorithm import Algorithm, DynamicAlgorithm
+from jmetal.core.operator import Crossover, Mutation, Selection
+from jmetal.core.problem import DynamicProblem, Problem
+from jmetal.operator.selection import BinaryTournamentSelection
+from jmetal.util.comparator import Comparator, DominanceComparator, MultiComparator
 from jmetal.util.density_estimator import CrowdingDistance
 from jmetal.util.evaluator import Evaluator
 from jmetal.util.ranking import FastNonDominatedRanking
-from jmetal.util.replacement import RankingAndDensityEstimatorReplacement, RemovalPolicyType
-from jmetal.util.comparator import DominanceComparator, Comparator, MultiComparator
+from jmetal.util.replacement import (
+    RankingAndDensityEstimatorReplacement,
+    RemovalPolicyType,
+)
 from jmetal.util.termination_criterion import TerminationCriterion
 
-S = TypeVar('S')
-R = TypeVar('R')
+S = TypeVar("S")
+R = TypeVar("R")
 
 """
 .. module:: NSGA-II
@@ -143,22 +143,25 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    """
 
 
-
    [docs]class NSGAII(GeneticAlgorithm[S, R]):
-
-    def __init__(self,
-                 problem: Problem,
-                 population_size: int,
-                 offspring_population_size: int,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 selection: Selection = BinaryTournamentSelection(
-                     MultiComparator([FastNonDominatedRanking.get_comparator(),
-                                      CrowdingDistance.get_comparator()])),
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator,
-                 dominance_comparator: Comparator = store.default_comparator):
-        """
+
    
+[docs]
+class NSGAII(GeneticAlgorithm[S, R]):
+    def __init__(
+        self,
+        problem: Problem,
+        population_size: int,
+        offspring_population_size: int,
+        mutation: Mutation,
+        crossover: Crossover,
+        selection: Selection = BinaryTournamentSelection(
+            MultiComparator([FastNonDominatedRanking.get_comparator(), CrowdingDistance.get_comparator()])
+        ),
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+        dominance_comparator: Comparator = store.default_comparator,
+    ):
+        """
         NSGA-II implementation as described in
 
         * K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist
@@ -175,7 +178,6 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
            :param population_size: Size of the population.
         :param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
         :param crossover: Crossover operator (see :py:mod:`jmetal.operator.crossover`).
-        :param selection: Selection operator (see :py:mod:`jmetal.operator.selection`).
         """
         super(NSGAII, self).__init__(
             problem=problem,
@@ -186,12 +188,14 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    selection=selection,
             termination_criterion=termination_criterion,
             population_evaluator=population_evaluator,
-            population_generator=population_generator
+            population_generator=population_generator,
         )
         self.dominance_comparator = dominance_comparator
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
-        """ This method joins the current and offspring populations to produce the population of the next generation
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
+        """This method joins the current and offspring populations to produce the population of the next generation
         by applying the ranking and crowding distance selection.
 
         :param population: Parent population.
@@ -206,28 +210,39 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    return solutions
    
 
-
    [docs]    def get_result(self) -> R:
+
+
    
+[docs]
+    def result(self) -> R:
         return self.solutions
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'NSGAII'
    
-
-
-
    [docs]class DynamicNSGAII(NSGAII[S, R], DynamicAlgorithm):
-
-    def __init__(self,
-                 problem: DynamicProblem[S],
-                 population_size: int,
-                 offspring_population_size: int,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 selection: Selection = BinaryTournamentSelection(
-                     MultiComparator([FastNonDominatedRanking.get_comparator(),
-                                      CrowdingDistance.get_comparator()])),
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator,
-                 dominance_comparator: DominanceComparator = DominanceComparator()):
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "NSGAII"
    
+
    
+
+
+
+
    
+[docs]
+class DynamicNSGAII(NSGAII[S, R], DynamicAlgorithm):
+    def __init__(
+        self,
+        problem: DynamicProblem[S],
+        population_size: int,
+        offspring_population_size: int,
+        mutation: Mutation,
+        crossover: Crossover,
+        selection: Selection = BinaryTournamentSelection(
+            MultiComparator([FastNonDominatedRanking.get_comparator(), CrowdingDistance.get_comparator()])
+        ),
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+        dominance_comparator: DominanceComparator = DominanceComparator(),
+    ):
         super(DynamicNSGAII, self).__init__(
             problem=problem,
             population_size=population_size,
@@ -238,50 +253,64 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    population_evaluator=population_evaluator,
             population_generator=population_generator,
             termination_criterion=termination_criterion,
-            dominance_comparator=dominance_comparator)
+            dominance_comparator=dominance_comparator,
+        )
         self.completed_iterations = 0
         self.start_computing_time = 0
         self.total_computing_time = 0
 
-
    [docs]    def restart(self):
+
    
+[docs]
+    def restart(self):
         self.solutions = self.evaluate(self.solutions)
    
 
-
    [docs]    def update_progress(self):
+
+
    
+[docs]
+    def update_progress(self):
         if self.problem.the_problem_has_changed():
             self.restart()
             self.problem.clear_changed()
 
-        observable_data = self.get_observable_data()
+        observable_data = self.observable_data()
         self.observable.notify_all(**observable_data)
 
         self.evaluations += self.offspring_population_size
    
 
-
    [docs]    def stopping_condition_is_met(self):
+
+
    
+[docs]
+    def stopping_condition_is_met(self):
         if self.termination_criterion.is_met:
-            observable_data = self.get_observable_data()
-            observable_data['TERMINATION_CRITERIA_IS_MET'] = True
+            observable_data = self.observable_data()
+            observable_data["TERMINATION_CRITERIA_IS_MET"] = True
             self.observable.notify_all(**observable_data)
 
             self.restart()
             self.init_progress()
 
-            self.completed_iterations += 1
    
+            self.completed_iterations += 1
    
+
    
 
 
-
    [docs]class DistributedNSGAII(Algorithm[S, R]):
 
-    def __init__(self,
-                 problem: Problem,
-                 population_size: int,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 number_of_cores: int,
-                 client,
-                 selection: Selection = BinaryTournamentSelection(
-                     MultiComparator([FastNonDominatedRanking.get_comparator(),
-                                      CrowdingDistance.get_comparator()])),
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 dominance_comparator: DominanceComparator = DominanceComparator()):
+
    
+[docs]
+class DistributedNSGAII(Algorithm[S, R]):
+    def __init__(
+        self,
+        problem: Problem,
+        population_size: int,
+        mutation: Mutation,
+        crossover: Crossover,
+        number_of_cores: int,
+        client,
+        selection: Selection = BinaryTournamentSelection(
+            MultiComparator([FastNonDominatedRanking.get_comparator(), CrowdingDistance.get_comparator()])
+        ),
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        dominance_comparator: DominanceComparator = DominanceComparator(),
+    ):
         super(DistributedNSGAII, self).__init__()
         self.problem = problem
         self.population_size = population_size
@@ -296,38 +325,63 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    self.number_of_cores = number_of_cores
         self.client = client
 
-
    [docs]    def create_initial_solutions(self) -> List[S]:
+
    
+[docs]
+    def create_initial_solutions(self) -> List[S]:
         return [self.problem.create_solution() for _ in range(self.number_of_cores)]
    
 
-
    [docs]    def evaluate(self, solutions: List[S]) -> List[S]:
+
+
    
+[docs]
+    def evaluate(self, solutions: List[S]) -> List[S]:
         return self.client.map(self.problem.evaluate, solutions)
    
 
-
    [docs]    def stopping_condition_is_met(self) -> bool:
+
+
    
+[docs]
+    def stopping_condition_is_met(self) -> bool:
         return self.termination_criterion.is_met
    
 
-
    [docs]    def get_observable_data(self) -> dict:
+
+
    
+[docs]
+    def observable_data(self) -> dict:
         ctime = time.time() - self.start_computing_time
 
-        return {'PROBLEM': self.problem,
-                'EVALUATIONS': self.evaluations,
-                'SOLUTIONS': self.get_result(),
-                'COMPUTING_TIME': ctime}
    
+        return {
+            "PROBLEM": self.problem,
+            "EVALUATIONS": self.evaluations,
+            "SOLUTIONS": self.result(),
+            "COMPUTING_TIME": ctime,
+        }
    
 
-
    [docs]    def init_progress(self) -> None:
+
+
    
+[docs]
+    def init_progress(self) -> None:
         self.evaluations = self.number_of_cores
 
-        observable_data = self.get_observable_data()
+        observable_data = self.observable_data()
         self.observable.notify_all(**observable_data)
    
 
-
    [docs]    def step(self) -> None:
+
+
    
+[docs]
+    def step(self) -> None:
         pass
    
 
-
    [docs]    def update_progress(self):
-        observable_data = self.get_observable_data()
+
+
    
+[docs]
+    def update_progress(self):
+        observable_data = self.observable_data()
         self.observable.notify_all(**observable_data)
    
 
-
    [docs]    def run(self):
-        """ Execute the algorithm. """
+
+
    
+[docs]
+    def run(self):
+        """Execute the algorithm."""
         self.start_computing_time = time.time()
 
         create_solution = dask.delayed(self.problem.create_solution)
@@ -384,8 +438,9 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    mating_population.append(solution)
 
                 # Reproduction and evaluation
-                new_task = self.client.submit(reproduction, mating_population, self.problem,
-                                              self.crossover_operator, self.mutation_operator)
+                new_task = self.client.submit(
+                    reproduction, mating_population, self.problem, self.crossover_operator, self.mutation_operator
+                )
                 task_pool.add(new_task)
 
                 # update progress
@@ -403,11 +458,19 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    for future, _ in task_pool:
             future.cancel()
    
 
-
    [docs]    def get_result(self) -> R:
+
+
    
+[docs]
+    def result(self) -> R:
         return self.solutions
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'dNSGA-II'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "dNSGA-II"
    
+
    
+
 
 
 def reproduction(mating_population: List[S], problem, crossover_operator, mutation_operator) -> S:
@@ -438,8 +501,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.algorithm.multiobjective.nsgaii
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
-    
+    jmetal.algorithm.multiobjective.nsgaiii — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -40,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
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+          
  • Module code »
    • 
+        
  • jmetal.algorithm.multiobjective.nsgaiii
    •  
       
     
    
     
    
@@ -52,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -74,8 +70,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,27 +106,27 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    -from abc import abstractmethod, ABC
-from typing import TypeVar, List
+from abc import ABC, abstractmethod
+from typing import List, TypeVar
 
 import numpy as np
-from numpy.linalg import LinAlgError
-from scipy import special
-
-from jmetal.algorithm.multiobjective.nsgaii import NSGAII
-from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover, Selection
-from jmetal.core.problem import Problem
-from jmetal.operator import BinaryTournamentSelection
-from jmetal.util.comparator import Comparator, MultiComparator
-from jmetal.util.density_estimator import CrowdingDistance
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.ranking import FastNonDominatedRanking
-from jmetal.util.termination_criterion import TerminationCriterion
-
-S = TypeVar('S')
-R = TypeVar('R')
+from numpy.linalg import LinAlgError
+from scipy import special
+
+from jmetal.algorithm.multiobjective.nsgaii import NSGAII
+from jmetal.config import store
+from jmetal.core.operator import Crossover, Mutation, Selection
+from jmetal.core.problem import Problem
+from jmetal.operator.selection import BinaryTournamentSelection
+from jmetal.util.comparator import Comparator, MultiComparator
+from jmetal.util.density_estimator import CrowdingDistance
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.ranking import FastNonDominatedRanking
+from jmetal.util.termination_criterion import TerminationCriterion
+
+S = TypeVar("S")
+R = TypeVar("R")
 
 """
 .. module:: NSGA-III
@@ -144,8 +138,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    class ReferenceDirectionFactory(ABC):
-
-    def __init__(self, n_dim: int, scaling=None) -> None:
+    def __init__(self, n_dim: int, scaling=None) -> None:
         self.n_dim = n_dim
         self.scaling = scaling
 
@@ -164,8 +157,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    class UniformReferenceDirectionFactory(ReferenceDirectionFactory):
-
-    def __init__(self, n_dim: int, scaling=None, n_points: int = None, n_partitions: int = None) -> None:
+    def __init__(self, n_dim: int, scaling=None, n_points: int = None, n_partitions: int = None) -> None:
         super().__init__(n_dim, scaling)
         if n_points is not None:
             self.n_partitions = self.get_partition_closest_to_points(n_points, n_dim)
@@ -191,8 +183,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    else:
             for i in range(beta + 1):
                 ref_dir[depth] = 1.0 * i / (1.0 * n_partitions)
-                self.__uniform_reference_directions(ref_dirs, np.copy(ref_dir), n_partitions, beta - i,
-                                                    depth + 1)
+                self.__uniform_reference_directions(ref_dirs, np.copy(ref_dir), n_partitions, beta - i, depth + 1)
 
     @staticmethod
     def get_partition_closest_to_points(n_points, n_dim):
@@ -214,7 +205,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    def get_extreme_points(F, n_objs, ideal_point, extreme_points=None):
-    """ Calculate the Achievement Scalarization Function which is used for the extreme point decomposition. """
+    """Calculate the Achievement Scalarization Function which is used for the extreme point decomposition."""
     asf = np.eye(n_objs)
     asf[asf == 0] = 1e6
 
@@ -236,7 +227,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    def get_nadir_point(extreme_points, ideal_point, worst_point, worst_of_front, worst_of_population):
-    """ Calculate the axis intersects for a set of individuals and its extremes (construct hyperplane). """
+    """Calculate the axis intersects for a set of individuals and its extremes (construct hyperplane)."""
     try:
         # find the intercepts using gaussian elimination
         M = extreme_points - ideal_point
@@ -295,7 +286,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    # add the selected individual to the survivors
             mask[next_ind] = False
-            pop[next_ind].attributes['is_closest'] = is_closest
+            pop[next_ind].attributes["is_closest"] = is_closest
             survivors.append(int(next_ind))
 
             # increase the corresponding niche count
@@ -305,7 +296,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    def associate_to_niches(F, niches, ideal_point, nadir_point, utopian_epsilon: float = 0.0):
-    """ Associate each solution to a reference point. """
+    """Associate each solution to a reference point."""
     utopian_point = ideal_point - utopian_epsilon
 
     denom = nadir_point - utopian_point
@@ -343,27 +334,30 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    return niche_count
 
 
-
    [docs]class NSGAIII(NSGAII):
-
-    def __init__(self,
-                 reference_directions,
-                 problem: Problem,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 population_size: int = None,
-                 selection: Selection = BinaryTournamentSelection(
-                     MultiComparator([FastNonDominatedRanking.get_comparator(),
-                                      CrowdingDistance.get_comparator()])),
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator,
-                 dominance_comparator: Comparator = store.default_comparator):
+
    
+[docs]
+class NSGAIII(NSGAII):
+    def __init__(
+        self,
+        reference_directions,
+        problem: Problem,
+        mutation: Mutation,
+        crossover: Crossover,
+        population_size: int = None,
+        selection: Selection = BinaryTournamentSelection(
+            MultiComparator([FastNonDominatedRanking.get_comparator(), CrowdingDistance.get_comparator()])
+        ),
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+        dominance_comparator: Comparator = store.default_comparator,
+    ):
         self.reference_directions = reference_directions.compute()
 
         if not population_size:
             population_size = len(self.reference_directions)
-        if self.reference_directions.shape[1] != problem.number_of_objectives:
-            raise Exception('Dimensionality of reference points must be equal to the number of objectives')
+        if self.reference_directions.shape[1] != problem.number_of_objectives():
+            raise Exception("Dimensionality of reference points must be equal to the number of objectives")
 
         super(NSGAIII, self).__init__(
             problem=problem,
@@ -375,15 +369,17 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    termination_criterion=termination_criterion,
             population_evaluator=population_evaluator,
             population_generator=population_generator,
-            dominance_comparator=dominance_comparator
+            dominance_comparator=dominance_comparator,
         )
 
         self.extreme_points = None
-        self.ideal_point = np.full(self.problem.number_of_objectives, np.inf)
-        self.worst_point = np.full(self.problem.number_of_objectives, -np.inf)
+        self.ideal_point = np.full(self.problem.number_of_objectives(), np.inf)
+        self.worst_point = np.full(self.problem.number_of_objectives(), -np.inf)
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[S]) -> List[S]:
-        """ Implements NSGA-III environmental selection based on reference points as described in:
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[S]) -> List[S]:
+        """Implements NSGA-III environmental selection based on reference points as described in:
 
         * Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization
           Algorithm Using Reference-Point-Based Nondominated Sorting Approach,
@@ -404,20 +400,24 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    fronts, non_dominated = ranking.ranked_sublists, ranking.get_subfront(0)
 
         # find the extreme points for normalization
-        self.extreme_points = get_extreme_points(F=np.array([s.objectives for s in non_dominated]),
-                                                 n_objs=self.problem.number_of_objectives,
-                                                 ideal_point=self.ideal_point,
-                                                 extreme_points=self.extreme_points)
+        self.extreme_points = get_extreme_points(
+            F=np.array([s.objectives for s in non_dominated]),
+            n_objs=self.problem.number_of_objectives(),
+            ideal_point=self.ideal_point,
+            extreme_points=self.extreme_points,
+        )
 
         # find the intercepts for normalization and do backup if gaussian elimination fails
         worst_of_population = np.max(F, axis=0)
         worst_of_front = np.max(np.array([s.objectives for s in non_dominated]), axis=0)
 
-        nadir_point = get_nadir_point(extreme_points=self.extreme_points,
-                                      ideal_point=self.ideal_point,
-                                      worst_point=self.worst_point,
-                                      worst_of_population=worst_of_population,
-                                      worst_of_front=worst_of_front)
+        nadir_point = get_nadir_point(
+            extreme_points=self.extreme_points,
+            ideal_point=self.ideal_point,
+            worst_point=self.worst_point,
+            worst_of_population=worst_of_population,
+            worst_of_front=worst_of_front,
+        )
 
         #  consider only the population until we come to the splitting front
         pop = np.concatenate(ranking.ranked_sublists)
@@ -432,10 +432,9 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    last_front = np.array(fronts[-1])
 
         # associate individuals to niches
-        niche_of_individuals, dist_to_niche = associate_to_niches(F=F,
-                                                                  niches=self.reference_directions,
-                                                                  ideal_point=self.ideal_point,
-                                                                  nadir_point=nadir_point)
+        niche_of_individuals, dist_to_niche = associate_to_niches(
+            F=F, niches=self.reference_directions, ideal_point=self.ideal_point, nadir_point=nadir_point
+        )
 
         # if we need to select individuals to survive
         if len(pop) > self.population_size:
@@ -447,30 +446,41 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaiii
    # if some individuals already survived
             else:
                 until_last_front = np.concatenate(fronts[:-1])
-                niche_count = compute_niche_count(len(self.reference_directions),
-                                                  niche_of_individuals[until_last_front])
+                niche_count = compute_niche_count(
+                    len(self.reference_directions), niche_of_individuals[until_last_front]
+                )
                 n_remaining = self.population_size - len(until_last_front)
 
-            S_idx = niching(pop=pop[last_front],
-                            n_remaining=n_remaining,
-                            niche_count=niche_count,
-                            niche_of_individuals=niche_of_individuals[last_front],
-                            dist_to_niche=dist_to_niche[last_front])
+            S_idx = niching(
+                pop=pop[last_front],
+                n_remaining=n_remaining,
+                niche_count=niche_count,
+                niche_of_individuals=niche_of_individuals[last_front],
+                dist_to_niche=dist_to_niche[last_front],
+            )
 
             survivors_idx = np.concatenate((until_last_front, last_front[S_idx].tolist()))
             pop = pop[survivors_idx]
 
         return list(pop)
    
 
-
    [docs]    def get_result(self):
-        """ Return only non dominated solutions."""
+
+
    
+[docs]
+    def result(self):
+        """Return only non dominated solutions."""
         ranking = FastNonDominatedRanking(self.dominance_comparator)
         ranking.compute_ranking(self.solutions, k=self.population_size)
 
         return ranking.get_subfront(0)
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'NSGAIII'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "NSGAIII"
    
+
    
+
 
    
 
           
    
@@ -487,8 +497,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
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    • 
+          
  • Module code »
    • 
+        
  • jmetal.algorithm.multiobjective.nsgaiii
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.algorithm.multiobjective.omopso — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.algorithm.multiobjective.omopso
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -111,8 +108,7 @@ 
    Table Of Contents
    
   
    Source code for jmetal.algorithm.multiobjective.omopso
     import random
 from copy import copy
-from math import sqrt
-from typing import TypeVar, List, Optional
+from typing import List, Optional, TypeVar
 
 import numpy
 
@@ -120,7 +116,7 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    from jmetal.core.algorithm import ParticleSwarmOptimization
 from jmetal.core.problem import FloatProblem
 from jmetal.core.solution import FloatSolution
-from jmetal.operator import UniformMutation
+from jmetal.operator.mutation import UniformMutation
 from jmetal.operator.mutation import NonUniformMutation
 from jmetal.util.archive import BoundedArchive, NonDominatedSolutionsArchive
 from jmetal.util.comparator import DominanceComparator, EpsilonDominanceComparator
@@ -128,7 +124,7 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    from jmetal.util.generator import Generator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-R = TypeVar('R')
+R = TypeVar("R")
 
 """
 .. module:: OMOPSO
@@ -139,19 +135,22 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    """
 
 
-
    [docs]class OMOPSO(ParticleSwarmOptimization):
-
-    def __init__(self,
-                 problem: FloatProblem,
-                 swarm_size: int,
-                 uniform_mutation: UniformMutation,
-                 non_uniform_mutation: NonUniformMutation,
-                 leaders: Optional[BoundedArchive],
-                 epsilon: float,
-                 termination_criterion: TerminationCriterion,
-                 swarm_generator: Generator = store.default_generator,
-                 swarm_evaluator: Evaluator = store.default_evaluator):
-        """ This class implements the OMOPSO algorithm as described in
+
    
+[docs]
+class OMOPSO(ParticleSwarmOptimization):
+    def __init__(
+        self,
+        problem: FloatProblem,
+        swarm_size: int,
+        uniform_mutation: UniformMutation,
+        non_uniform_mutation: NonUniformMutation,
+        leaders: Optional[BoundedArchive],
+        epsilon: float,
+        termination_criterion: TerminationCriterion,
+        swarm_generator: Generator = store.default_generator,
+        swarm_evaluator: Evaluator = store.default_evaluator,
+    ):
+        """This class implements the OMOPSO algorithm as described in
 
         todo Update this reference
         * SMPSO: A new PSO-based metaheuristic for multi-objective optimization
@@ -163,9 +162,7 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
            :param swarm_size: Size of the swarm.
         :param leaders: Archive for leaders.
         """
-        super(OMOPSO, self).__init__(
-            problem=problem,
-            swarm_size=swarm_size)
+        super(OMOPSO, self).__init__(problem=problem, swarm_size=swarm_size)
         self.swarm_generator = swarm_generator
         self.swarm_evaluator = swarm_evaluator
 
@@ -195,34 +192,54 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    self.dominance_comparator = DominanceComparator()
 
-        self.speed = numpy.zeros((self.swarm_size, self.problem.number_of_variables), dtype=float)
+        self.speed = numpy.zeros((self.swarm_size, self.problem.number_of_variables()), dtype=float)
 
-
    [docs]    def create_initial_solutions(self) -> List[FloatSolution]:
+
    
+[docs]
+    def create_initial_solutions(self) -> List[FloatSolution]:
         return [self.swarm_generator.new(self.problem) for _ in range(self.swarm_size)]
    
 
-
    [docs]    def evaluate(self, solution_list: List[FloatSolution]):
+
+
    
+[docs]
+    def evaluate(self, solution_list: List[FloatSolution]):
         return self.swarm_evaluator.evaluate(solution_list, self.problem)
    
 
-
    [docs]    def stopping_condition_is_met(self) -> bool:
+
+
    
+[docs]
+    def stopping_condition_is_met(self) -> bool:
         return self.termination_criterion.is_met
    
 
-
    [docs]    def initialize_global_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def initialize_global_best(self, swarm: List[FloatSolution]) -> None:
         for particle in swarm:
             if self.leaders.add(particle):
                 self.epsilon_archive.add(copy(particle))
    
 
-
    [docs]    def initialize_particle_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def initialize_particle_best(self, swarm: List[FloatSolution]) -> None:
         for particle in swarm:
-            particle.attributes['local_best'] = copy(particle)
    
+            particle.attributes["local_best"] = copy(particle)
    
 
-
    [docs]    def initialize_velocity(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def initialize_velocity(self, swarm: List[FloatSolution]) -> None:
         for i in range(self.swarm_size):
-            for j in range(self.problem.number_of_variables):
+            for j in range(self.problem.number_of_variables()):
                 self.speed[i][j] = 0.0
    
 
-
    [docs]    def update_velocity(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def update_velocity(self, swarm: List[FloatSolution]) -> None:
         for i in range(self.swarm_size):
-            best_particle = copy(swarm[i].attributes['local_best'])
+            best_particle = copy(swarm[i].attributes["local_best"])
             best_global = self.select_global_best()
 
             r1 = round(random.uniform(self.r1_min, self.r1_max), 1)
@@ -232,11 +249,16 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    w = round(random.uniform(self.weight_min, self.weight_max), 1)
 
             for var in range(swarm[i].number_of_variables):
-                self.speed[i][var] = w * self.speed[i][var] \
-                                     + (c1 * r1 * (best_particle.variables[var] - swarm[i].variables[var])) \
-                                     + (c2 * r2 * (best_global.variables[var] - swarm[i].variables[var]))
    
+                self.speed[i][var] = (
+                    w * self.speed[i][var]
+                    + (c1 * r1 * (best_particle.variables[var] - swarm[i].variables[var]))
+                    + (c2 * r2 * (best_global.variables[var] - swarm[i].variables[var]))
+                )
    
+
 
-
    [docs]    def update_position(self, swarm: List[FloatSolution]) -> None:
+
    
+[docs]
+    def update_position(self, swarm: List[FloatSolution]) -> None:
         for i in range(self.swarm_size):
             particle = swarm[i]
 
@@ -251,20 +273,27 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    particle.variables[j] = self.problem.upper_bound[j]
                     self.speed[i][j] *= self.change_velocity2
    
 
-
    [docs]    def update_global_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def update_global_best(self, swarm: List[FloatSolution]) -> None:
         for particle in swarm:
             if self.leaders.add(copy(particle)):
                 self.epsilon_archive.add(copy(particle))
    
 
-
    [docs]    def update_particle_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def update_particle_best(self, swarm: List[FloatSolution]) -> None:
         for i in range(self.swarm_size):
-            flag = self.dominance_comparator.compare(
-                swarm[i],
-                swarm[i].attributes['local_best'])
+            flag = self.dominance_comparator.compare(swarm[i], swarm[i].attributes["local_best"])
             if flag != 1:
-                swarm[i].attributes['local_best'] = copy(swarm[i])
    
+                swarm[i].attributes["local_best"] = copy(swarm[i])
    
+
 
-
    [docs]    def perturbation(self, swarm: List[FloatSolution]) -> None:
+
    
+[docs]
+    def perturbation(self, swarm: List[FloatSolution]) -> None:
         self.non_uniform_mutation.set_current_iteration(self.evaluations / self.swarm_size)
         for i in range(self.swarm_size):
             if (i % 3) == 0:
@@ -272,7 +301,10 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    else:
                 self.uniform_mutation.execute(swarm[i])
    
 
-
    [docs]    def select_global_best(self) -> FloatSolution:
+
+
    
+[docs]
+    def select_global_best(self) -> FloatSolution:
         leaders = self.leaders.solution_list
 
         if len(leaders) > 2:
@@ -287,28 +319,10 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    return best_global
    
 
-    def __velocity_constriction(self, value: float, delta_max: [], delta_min: [], variable_index: int) -> float:
-        result = value
-        if value > delta_max[variable_index]:
-            result = delta_max[variable_index]
-        if value < delta_min[variable_index]:
-            result = delta_min[variable_index]
-
-        return result
-
-    def __inertia_weight(self, wmax: float):
-        return wmax
-
-    def __constriction_coefficient(self, c1: float, c2: float) -> float:
-        rho = c1 + c2
-        if rho <= 4:
-            result = 1.0
-        else:
-            result = 2.0 / (2.0 - rho - sqrt(pow(rho, 2.0) - 4.0 * rho))
-
-        return result
 
-
    [docs]    def init_progress(self) -> None:
+
    
+[docs]
+    def init_progress(self) -> None:
         self.evaluations = self.swarm_size
         self.leaders.compute_density_estimator()
 
@@ -316,19 +330,30 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    self.initialize_particle_best(self.solutions)
         self.initialize_global_best(self.solutions)
    
 
-
    [docs]    def update_progress(self) -> None:
+
+
    
+[docs]
+    def update_progress(self) -> None:
         self.evaluations += self.swarm_size
         self.leaders.compute_density_estimator()
 
-        observable_data = self.get_observable_data()
-        observable_data['SOLUTIONS'] = self.epsilon_archive.solution_list
+        observable_data = self.observable_data()
+        observable_data["SOLUTIONS"] = self.epsilon_archive.solution_list
         self.observable.notify_all(**observable_data)
    
 
-
    [docs]    def get_result(self) -> List[FloatSolution]:
+
+
    
+[docs]
+    def result(self) -> List[FloatSolution]:
         return self.epsilon_archive.solution_list
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'OMOPSO'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "OMOPSO"
    
+
    
+
 
    
 
           
    
@@ -345,8 +370,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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-          
  • Module code »
    •  
+        
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+          
  • Module code »
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+        
  • jmetal.algorithm.multiobjective.omopso
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.algorithm.multiobjective.smpso — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
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    • 
-        
  • jMetalPy 1.5.3 documentation »
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  • Module code »
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+        
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+          
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+        
  • jmetal.algorithm.multiobjective.smpso
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -113,22 +110,22 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    import threading
 from copy import copy
 from math import sqrt
-from typing import TypeVar, List, Optional
+from typing import List, Optional, TypeVar
 
 import numpy
 
 from jmetal.config import store
-from jmetal.core.algorithm import ParticleSwarmOptimization, DynamicAlgorithm
+from jmetal.core.algorithm import DynamicAlgorithm, ParticleSwarmOptimization
 from jmetal.core.operator import Mutation
-from jmetal.core.problem import FloatProblem, DynamicProblem
+from jmetal.core.problem import DynamicProblem, FloatProblem
 from jmetal.core.solution import FloatSolution
-from jmetal.util.archive import BoundedArchive, ArchiveWithReferencePoint
-from jmetal.util.comparator import DominanceComparator
+from jmetal.util.archive import ArchiveWithReferencePoint, BoundedArchive
+from jmetal.util.comparator import DominanceComparator, Comparator
 from jmetal.util.evaluator import Evaluator
 from jmetal.util.generator import Generator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-R = TypeVar('R')
+R = TypeVar("R")
 
 """
 .. module:: SMPSO
@@ -139,17 +136,21 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    """
 
 
-
    [docs]class SMPSO(ParticleSwarmOptimization):
-
-    def __init__(self,
-                 problem: FloatProblem,
-                 swarm_size: int,
-                 mutation: Mutation,
-                 leaders: Optional[BoundedArchive],
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 swarm_generator: Generator = store.default_generator,
-                 swarm_evaluator: Evaluator = store.default_evaluator):
-        """ This class implements the SMPSO algorithm as described in
+
    
+[docs]
+class SMPSO(ParticleSwarmOptimization):
+    def __init__(
+        self,
+        problem: FloatProblem,
+        swarm_size: int,
+        mutation: Mutation,
+        leaders: Optional[BoundedArchive],
+        dominance_comparator: Comparator = DominanceComparator(),
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        swarm_generator: Generator = store.default_generator,
+        swarm_evaluator: Evaluator = store.default_evaluator,
+    ):
+        """This class implements the SMPSO algorithm as described in
 
         * SMPSO: A new PSO-based metaheuristic for multi-objective optimization
         * MCDM 2009. DOI: `<http://dx.doi.org/10.1109/MCDM.2009.4938830/>`_.
@@ -163,9 +164,7 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
            :param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
         :param leaders: Archive for leaders.
         """
-        super(SMPSO, self).__init__(
-            problem=problem,
-            swarm_size=swarm_size)
+        super(SMPSO, self).__init__(problem=problem, swarm_size=swarm_size)
         self.swarm_generator = swarm_generator
         self.swarm_evaluator = swarm_evaluator
         self.termination_criterion = termination_criterion
@@ -186,38 +185,60 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    self.change_velocity1 = -1
         self.change_velocity2 = -1
 
-        self.dominance_comparator = DominanceComparator()
+        self.dominance_comparator = dominance_comparator
 
-        self.speed = numpy.zeros((self.swarm_size, self.problem.number_of_variables), dtype=float)
-        self.delta_max, self.delta_min = numpy.empty(problem.number_of_variables), \
-                                         numpy.empty(problem.number_of_variables)
+        self.speed = numpy.zeros((self.swarm_size, self.problem.number_of_variables()), dtype=float)
+        self.delta_max, self.delta_min = (
+            numpy.empty(problem.number_of_variables()),
+            numpy.empty(problem.number_of_variables()),
+        )
 
-
    [docs]    def create_initial_solutions(self) -> List[FloatSolution]:
+
    
+[docs]
+    def create_initial_solutions(self) -> List[FloatSolution]:
         return [self.swarm_generator.new(self.problem) for _ in range(self.swarm_size)]
    
 
-
    [docs]    def evaluate(self, solution_list: List[FloatSolution]):
+
+
    
+[docs]
+    def evaluate(self, solution_list: List[FloatSolution]):
         return self.swarm_evaluator.evaluate(solution_list, self.problem)
    
 
-
    [docs]    def stopping_condition_is_met(self) -> bool:
+
+
    
+[docs]
+    def stopping_condition_is_met(self) -> bool:
         return self.termination_criterion.is_met
    
 
-
    [docs]    def initialize_global_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def initialize_global_best(self, swarm: List[FloatSolution]) -> None:
         for particle in swarm:
             self.leaders.add(copy(particle))
    
 
-
    [docs]    def initialize_particle_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def initialize_particle_best(self, swarm: List[FloatSolution]) -> None:
         for particle in swarm:
-            particle.attributes['local_best'] = copy(particle)
    
+            particle.attributes["local_best"] = copy(particle)
    
 
-
    [docs]    def initialize_velocity(self, swarm: List[FloatSolution]) -> None:
-        for i in range(self.problem.number_of_variables):
+
+
    
+[docs]
+    def initialize_velocity(self, swarm: List[FloatSolution]) -> None:
+        for i in range(self.problem.number_of_variables()):
             self.delta_max[i] = (self.problem.upper_bound[i] - self.problem.lower_bound[i]) / 2.0
 
         self.delta_min = -1.0 * self.delta_max
    
 
-
    [docs]    def update_velocity(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def update_velocity(self, swarm: List[FloatSolution]) -> None:
         for i in range(self.swarm_size):
-            best_particle = copy(swarm[i].attributes['local_best'])
+            best_particle = copy(swarm[i].attributes["local_best"])
             best_global = self.select_global_best()
 
             r1 = round(random.uniform(self.r1_min, self.r1_max), 1)
@@ -227,22 +248,27 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    wmax = self.max_weight
             wmin = self.min_weight
 
-            for var in range(swarm[i].number_of_variables):
-                self.speed[i][var] = \
-                    self.__velocity_constriction(
-                        self.__constriction_coefficient(c1, c2) *
-                        ((self.__inertia_weight(wmax)
-                          * self.speed[i][var])
-                         + (c1 * r1 * (best_particle.variables[var] - swarm[i].variables[var]))
-                         + (c2 * r2 * (best_global.variables[var] - swarm[i].variables[var]))
-                         ),
-                        self.delta_max, self.delta_min, var)
    
-
-
    [docs]    def update_position(self, swarm: List[FloatSolution]) -> None:
+            for var in range(len(swarm[i].variables)):
+                self.speed[i][var] = self.__velocity_constriction(
+                    self.__constriction_coefficient(c1, c2)
+                    * (
+                        (self.__inertia_weight(wmax) * self.speed[i][var])
+                        + (c1 * r1 * (best_particle.variables[var] - swarm[i].variables[var]))
+                        + (c2 * r2 * (best_global.variables[var] - swarm[i].variables[var]))
+                    ),
+                    self.delta_max,
+                    self.delta_min,
+                    var,
+                )
    
+
+
+
    
+[docs]
+    def update_position(self, swarm: List[FloatSolution]) -> None:
         for i in range(self.swarm_size):
             particle = swarm[i]
 
-            for j in range(particle.number_of_variables):
+            for j in range(len(particle.variables)):
                 particle.variables[j] += self.speed[i][j]
 
                 if particle.variables[j] < self.problem.lower_bound[j]:
@@ -253,24 +279,34 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    particle.variables[j] = self.problem.upper_bound[j]
                     self.speed[i][j] *= self.change_velocity2
    
 
-
    [docs]    def update_global_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def update_global_best(self, swarm: List[FloatSolution]) -> None:
         for particle in swarm:
             self.leaders.add(copy(particle))
    
 
-
    [docs]    def update_particle_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def update_particle_best(self, swarm: List[FloatSolution]) -> None:
         for i in range(self.swarm_size):
-            flag = self.dominance_comparator.compare(
-                swarm[i],
-                swarm[i].attributes['local_best'])
+            flag = self.dominance_comparator.compare(swarm[i], swarm[i].attributes["local_best"])
             if flag != 1:
-                swarm[i].attributes['local_best'] = copy(swarm[i])
    
+                swarm[i].attributes["local_best"] = copy(swarm[i])
    
 
-
    [docs]    def perturbation(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def perturbation(self, swarm: List[FloatSolution]) -> None:
         for i in range(self.swarm_size):
             if (i % 6) == 0:
                 self.mutation_operator.execute(swarm[i])
    
 
-
    [docs]    def select_global_best(self) -> FloatSolution:
+
+
    
+[docs]
+    def select_global_best(self) -> FloatSolution:
         leaders = self.leaders.solution_list
 
         if len(leaders) > 2:
@@ -285,6 +321,7 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    return best_global
    
 
+
     def __velocity_constriction(self, value: float, delta_max: [], delta_min: [], variable_index: int) -> float:
         result = value
         if value > delta_max[variable_index]:
@@ -306,7 +343,9 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    return result
 
-
    [docs]    def init_progress(self) -> None:
+
    
+[docs]
+    def init_progress(self) -> None:
         self.evaluations = self.swarm_size
         self.leaders.compute_density_estimator()
 
@@ -314,31 +353,45 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    self.initialize_particle_best(self.solutions)
         self.initialize_global_best(self.solutions)
    
 
-
    [docs]    def update_progress(self) -> None:
+
+
    
+[docs]
+    def update_progress(self) -> None:
         self.evaluations += self.swarm_size
         self.leaders.compute_density_estimator()
 
-        observable_data = self.get_observable_data()
-        observable_data['SOLUTIONS'] = self.leaders.solution_list
+        observable_data = self.observable_data()
+        observable_data["SOLUTIONS"] = self.leaders.solution_list
         self.observable.notify_all(**observable_data)
    
 
-
    [docs]    def get_result(self) -> List[FloatSolution]:
+
+
    
+[docs]
+    def result(self) -> List[FloatSolution]:
         return self.leaders.solution_list
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'SMPSO'
    
 
+
    
+[docs]
+    def get_name(self) -> str:
+        return "SMPSO"
    
+
    
 
-
    [docs]class DynamicSMPSO(SMPSO, DynamicAlgorithm):
 
-    def __init__(self,
-                 problem: DynamicProblem[FloatSolution],
-                 swarm_size: int,
-                 mutation: Mutation,
-                 leaders: BoundedArchive,
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 swarm_generator: Generator = store.default_generator,
-                 swarm_evaluator: Evaluator = store.default_evaluator):
+
+
    
+[docs]
+class DynamicSMPSO(SMPSO, DynamicAlgorithm):
+    def __init__(
+        self,
+        problem: DynamicProblem[FloatSolution],
+        swarm_size: int,
+        mutation: Mutation,
+        leaders: BoundedArchive,
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        swarm_generator: Generator = store.default_generator,
+        swarm_evaluator: Evaluator = store.default_evaluator,
+    ):
         super(DynamicSMPSO, self).__init__(
             problem=problem,
             swarm_size=swarm_size,
@@ -346,10 +399,13 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    leaders=leaders,
             termination_criterion=termination_criterion,
             swarm_generator=swarm_generator,
-            swarm_evaluator=swarm_evaluator)
+            swarm_evaluator=swarm_evaluator,
+        )
         self.completed_iterations = 0
 
-
    [docs]    def restart(self) -> None:
+
    
+[docs]
+    def restart(self) -> None:
         self.solutions = self.create_initial_solutions()
         self.solutions = self.evaluate(self.solutions)
 
@@ -361,40 +417,51 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    self.init_progress()
    
 
-
    [docs]    def update_progress(self):
+
+
    
+[docs]
+    def update_progress(self):
         if self.problem.the_problem_has_changed():
             self.restart()
             self.problem.clear_changed()
 
-        observable_data = self.get_observable_data()
+        observable_data = self.observable_data()
         self.observable.notify_all(**observable_data)
 
         self.evaluations += self.swarm_size
         self.leaders.compute_density_estimator()
    
 
-
    [docs]    def stopping_condition_is_met(self):
+
+
    
+[docs]
+    def stopping_condition_is_met(self):
         if self.termination_criterion.is_met:
-            observable_data = self.get_observable_data()
-            observable_data['termination_criterion_is_met'] = True
+            observable_data = self.observable_data()
+            observable_data["termination_criterion_is_met"] = True
             self.observable.notify_all(**observable_data)
 
             self.restart()
             self.init_progress()
-            self.completed_iterations += 1
    
+            self.completed_iterations += 1
    
+
    
 
 
-
    [docs]class SMPSORP(SMPSO):
 
-    def __init__(self,
-                 problem: FloatProblem,
-                 swarm_size: int,
-                 mutation: Mutation,
-                 reference_points: List[List[float]],
-                 leaders: List[ArchiveWithReferencePoint],
-                 termination_criterion: TerminationCriterion,
-                 swarm_generator: Generator = store.default_generator,
-                 swarm_evaluator: Evaluator = store.default_evaluator):
-        """ This class implements the SMPSORP algorithm.
+
    
+[docs]
+class SMPSORP(SMPSO):
+    def __init__(
+        self,
+        problem: FloatProblem,
+        swarm_size: int,
+        mutation: Mutation,
+        reference_points: List[List[float]],
+        leaders: List[ArchiveWithReferencePoint],
+        termination_criterion: TerminationCriterion,
+        swarm_generator: Generator = store.default_generator,
+        swarm_evaluator: Evaluator = store.default_evaluator,
+    ):
+        """This class implements the SMPSORP algorithm.
 
         :param problem: The problem to solve.
         :param swarm_size:
@@ -409,7 +476,8 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    leaders=None,
             swarm_generator=swarm_generator,
             swarm_evaluator=swarm_evaluator,
-            termination_criterion=termination_criterion)
+            termination_criterion=termination_criterion,
+        )
         self.leaders = leaders
         self.reference_points = reference_points
         self.lock = threading.Lock()
@@ -417,17 +485,25 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    thread = threading.Thread(target=_change_reference_point, args=(self,))
         thread.start()
 
-
    [docs]    def initialize_global_best(self, swarm: List[FloatSolution]) -> None:
+
    
+[docs]
+    def initialize_global_best(self, swarm: List[FloatSolution]) -> None:
         for particle in swarm:
             for leader in self.leaders:
                 leader.add(copy(particle))
    
 
-
    [docs]    def update_global_best(self, swarm: List[FloatSolution]) -> None:
+
+
    
+[docs]
+    def update_global_best(self, swarm: List[FloatSolution]) -> None:
         for particle in swarm:
             for leader in self.leaders:
                 leader.add(copy(particle))
    
 
-
    [docs]    def select_global_best(self) -> FloatSolution:
+
+
    
+[docs]
+    def select_global_best(self) -> FloatSolution:
         selected = False
         selected_swarm_index = 0
 
@@ -450,7 +526,10 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    return best_global
    
 
-
    [docs]    def init_progress(self) -> None:
+
+
    
+[docs]
+    def init_progress(self) -> None:
         self.evaluations = self.swarm_size
 
         for leader in self.leaders:
@@ -460,29 +539,41 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    self.initialize_particle_best(self.solutions)
         self.initialize_global_best(self.solutions)
    
 
-
    [docs]    def update_progress(self) -> None:
+
+
    
+[docs]
+    def update_progress(self) -> None:
         self.evaluations += self.swarm_size
 
         for leader in self.leaders:
             leader.filter()
             leader.compute_density_estimator()
 
-        observable_data = self.get_observable_data()
-        observable_data['REFERENCE_POINT'] = self.get_reference_point()
+        observable_data = self.observable_data()
+        observable_data["REFERENCE_POINT"] = self.get_reference_point()
         self.observable.notify_all(**observable_data)
    
 
-
    [docs]    def update_reference_point(self, new_reference_points: list):
+
+
    
+[docs]
+    def update_reference_point(self, new_reference_points: list):
         with self.lock:
             self.reference_points = new_reference_points
 
             for index, archive in enumerate(self.leaders):
                 archive.update_reference_point(new_reference_points[index])
    
 
-
    [docs]    def get_reference_point(self):
+
+
    
+[docs]
+    def get_reference_point(self):
         with self.lock:
             return self.reference_points
    
 
-
    [docs]    def get_result(self) -> List[FloatSolution]:
+
+
    
+[docs]
+    def result(self) -> List[FloatSolution]:
         result = []
 
         for leader in self.leaders:
@@ -491,24 +582,28 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    return result
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'SMPSO/RP'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "SMPSO/RP"
    
+
    
+
 
 
 def _change_reference_point(algorithm: SMPSORP):
-    """ Auxiliar function to read new reference points from the keyboard for the SMPSO/RP algorithm
-    """
+    """Auxiliar function to read new reference points from the keyboard for the SMPSO/RP algorithm"""
     number_of_reference_points = len(algorithm.reference_points)
     number_of_objectives = algorithm.problem.number_of_objectives
 
     while True:
-        print(f'Enter {number_of_reference_points}-points of dimension {number_of_objectives}: ')
+        print(f"Enter {number_of_reference_points}-points of dimension {number_of_objectives}: ")
         read = [float(x) for x in input().split()]
 
         # Update reference points
         reference_points = []
         for i in range(0, len(read), number_of_objectives):
-            reference_points.append(read[i:i + number_of_objectives])
+            reference_points.append(read[i : i + number_of_objectives])
 
         algorithm.update_reference_point(reference_points)
    @@ -527,8 +622,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Module code »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Module code »
    • +
  • jmetal.algorithm.multiobjective.smpso
    • - - - - - - - + jmetal.algorithm.multiobjective.spea2 — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Module code »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Module code »
    • +
  • jmetal.algorithm.multiobjective.spea2
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -109,23 +106,23 @@

    Table Of Contents

    Source code for jmetal.algorithm.multiobjective.spea2

    -from typing import TypeVar, List
+from typing import List, TypeVar
 
 from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
 from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover
+from jmetal.core.operator import Crossover, Mutation
 from jmetal.core.problem import Problem
-from jmetal.operator import BinaryTournamentSelection
+from jmetal.operator.selection import BinaryTournamentSelection
+from jmetal.util.comparator import Comparator, MultiComparator
 from jmetal.util.density_estimator import KNearestNeighborDensityEstimator
 from jmetal.util.evaluator import Evaluator
 from jmetal.util.generator import Generator
 from jmetal.util.ranking import StrengthRanking
 from jmetal.util.replacement import RankingAndDensityEstimatorReplacement, RemovalPolicyType
-from jmetal.util.comparator import Comparator, MultiComparator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-S = TypeVar('S')
-R = TypeVar('R')
+S = TypeVar("S")
+R = TypeVar("R")
 
 """
 .. module:: SPEA2
@@ -143,26 +140,30 @@ 
    Source code for jmetal.algorithm.multiobjective.spea2
    """
 
 
-
    [docs]class SPEA2(GeneticAlgorithm[S, R]):
-
-    def __init__(self,
-                 problem: Problem,
-                 population_size: int,
-                 offspring_population_size: int,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator,
-                 dominance_comparator: Comparator = store.default_comparator):
-        """
+
    
+[docs]
+class SPEA2(GeneticAlgorithm[S, R]):
+    def __init__(
+        self,
+        problem: Problem,
+        population_size: int,
+        offspring_population_size: int,
+        mutation: Mutation,
+        crossover: Crossover,
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+        dominance_comparator: Comparator = store.default_comparator,
+    ):
+        """
         :param problem: The problem to solve.
         :param population_size: Size of the population.
         :param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
         :param crossover: Crossover operator (see :py:mod:`jmetal.operator.crossover`).
         """
-        multi_comparator = MultiComparator([StrengthRanking.get_comparator(),
-                                            KNearestNeighborDensityEstimator.get_comparator()])
+        multi_comparator = MultiComparator(
+            [StrengthRanking.get_comparator(), KNearestNeighborDensityEstimator.get_comparator()]
+        )
         selection = BinaryTournamentSelection(comparator=multi_comparator)
 
         super(SPEA2, self).__init__(
@@ -174,12 +175,14 @@ 
    Source code for jmetal.algorithm.multiobjective.spea2
    selection=selection,
             termination_criterion=termination_criterion,
             population_evaluator=population_evaluator,
-            population_generator=population_generator
+            population_generator=population_generator,
         )
         self.dominance_comparator = dominance_comparator
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
-        """ This method joins the current and offspring populations to produce the population of the next generation
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
+        """This method joins the current and offspring populations to produce the population of the next generation
         by applying the ranking and crowding distance selection.
 
         :param population: Parent population.
@@ -194,11 +197,19 @@ 
    Source code for jmetal.algorithm.multiobjective.spea2
    return solutions
    
 
-
    [docs]    def get_result(self) -> R:
+
+
    
+[docs]
+    def result(self) -> R:
         return self.solutions
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'SPEA2'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "SPEA2"
    
+
    
+
    @@ -215,8 +226,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Module code »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Module code »
    • +
  • jmetal.algorithm.multiobjective.spea2
    • - - - - - - - + jmetal.algorithm.singleobjective.evolution_strategy — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Module code »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Module code »
    • +
  • jmetal.algorithm.singleobjective.evolution_strategy
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -110,17 +107,18 @@

    Table Of Contents

    Source code for jmetal.algorithm.singleobjective.evolution_strategy

     from copy import copy
-from typing import TypeVar, List
+from typing import List, TypeVar
 
 from jmetal.core.algorithm import EvolutionaryAlgorithm
 from jmetal.core.operator import Mutation
 from jmetal.core.problem import Problem
+from jmetal.util.constraint_handling import overall_constraint_violation_degree
 from jmetal.util.evaluator import Evaluator, SequentialEvaluator
 from jmetal.util.generator import Generator, RandomGenerator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-S = TypeVar('S')
-R = TypeVar('R')
+S = TypeVar("S")
+R = TypeVar("R")
 
 """
 .. module:: evolutionary_algorithm
@@ -131,21 +129,21 @@ 
    Source code for jmetal.algorithm.singleobjective.evolution_strategy
    """
 
 
-
    [docs]class EvolutionStrategy(EvolutionaryAlgorithm[S, R]):
-
-    def __init__(self,
-                 problem: Problem,
-                 mu: int,
-                 lambda_: int,
-                 elitist: bool,
-                 mutation: Mutation,
-                 termination_criterion: TerminationCriterion,
-                 population_generator: Generator = RandomGenerator(),
-                 population_evaluator: Evaluator = SequentialEvaluator()):
-        super(EvolutionStrategy, self).__init__(
-            problem=problem,
-            population_size=mu,
-            offspring_population_size=lambda_)
+
    
+[docs]
+class EvolutionStrategy(EvolutionaryAlgorithm[S, R]):
+    def __init__(
+        self,
+        problem: Problem,
+        mu: int,
+        lambda_: int,
+        elitist: bool,
+        mutation: Mutation,
+        termination_criterion: TerminationCriterion,
+        population_generator: Generator = RandomGenerator(),
+        population_evaluator: Evaluator = SequentialEvaluator(),
+    ):
+        super(EvolutionStrategy, self).__init__(problem=problem, population_size=mu, offspring_population_size=lambda_)
         self.mu = mu
         self.lambda_ = lambda_
         self.elitist = elitist
@@ -158,20 +156,33 @@ 
    Source code for jmetal.algorithm.singleobjective.evolution_strategy
    self.termination_criterion = termination_criterion
         self.observable.register(termination_criterion)
 
-
    [docs]    def create_initial_solutions(self) -> List[S]:
-        return [self.population_generator.new(self.problem)
-                for _ in range(self.population_size)]
    
+
    
+[docs]
+    def create_initial_solutions(self) -> List[S]:
+        return [self.population_generator.new(self.problem) for _ in range(self.population_size)]
    
+
 
-
    [docs]    def evaluate(self, solution_list: List[S]):
+
    
+[docs]
+    def evaluate(self, solution_list: List[S]):
         return self.population_evaluator.evaluate(solution_list, self.problem)
    
 
-
    [docs]    def stopping_condition_is_met(self) -> bool:
+
+
    
+[docs]
+    def stopping_condition_is_met(self) -> bool:
         return self.termination_criterion.is_met
    
 
-
    [docs]    def selection(self, population: List[S]) -> List[S]:
+
+
    
+[docs]
+    def selection(self, population: List[S]) -> List[S]:
         return population
    
 
-
    [docs]    def reproduction(self, population: List[S]) -> List[S]:
+
+
    
+[docs]
+    def reproduction(self, population: List[S]) -> List[S]:
         offspring_population = []
         for solution in population:
             for j in range(int(self.lambda_ / self.mu)):
@@ -180,7 +191,10 @@ 
    Source code for jmetal.algorithm.singleobjective.evolution_strategy
    return offspring_population
    
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[S]) -> List[S]:
+
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[S]) -> List[S]:
         population_pool = []
 
         if self.elitist:
@@ -189,7 +203,7 @@ 
    Source code for jmetal.algorithm.singleobjective.evolution_strategy
    else:
             population_pool.extend(offspring_population)
 
-        population_pool.sort(key=lambda s: s.objectives[0])
+        population_pool.sort(key=lambda s: (overall_constraint_violation_degree(s), s.objectives[0]))
 
         new_population = []
         for i in range(self.mu):
@@ -197,11 +211,19 @@ 
    Source code for jmetal.algorithm.singleobjective.evolution_strategy
    return new_population
    
 
-
    [docs]    def get_result(self) -> R:
+
+
    
+[docs]
+    def result(self) -> R:
         return self.solutions[0]
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Elitist evolution Strategy'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Elitist evolution Strategy"
    
+
    
+
    @@ -218,8 +240,9 @@

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  • jmetal.algorithm.singleobjective.evolution_strategy
    • - - - - - - - + jmetal.algorithm.singleobjective.genetic_algorithm — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.algorithm.singleobjective.genetic_algorithm
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -109,18 +106,21 @@

    Table Of Contents

    Source code for jmetal.algorithm.singleobjective.genetic_algorithm

    -from typing import TypeVar, List
+from functools import cmp_to_key
+from typing import List, TypeVar
 
 from jmetal.config import store
 from jmetal.core.algorithm import EvolutionaryAlgorithm
-from jmetal.core.operator import Mutation, Crossover, Selection
+from jmetal.core.operator import Crossover, Mutation, Selection
 from jmetal.core.problem import Problem
+from jmetal.operator.selection import BinaryTournamentSelection
+from jmetal.util.comparator import Comparator, ObjectiveComparator
 from jmetal.util.evaluator import Evaluator
 from jmetal.util.generator import Generator
 from jmetal.util.termination_criterion import TerminationCriterion
 
-S = TypeVar('S')
-R = TypeVar('R')
+S = TypeVar("S")
+R = TypeVar("R")
 
 """
 .. module:: genetic_algorithm
@@ -130,24 +130,29 @@ 
    Source code for jmetal.algorithm.singleobjective.genetic_algorithm
    """
 
 
-
    [docs]class GeneticAlgorithm(EvolutionaryAlgorithm[S, R]):
-
-    def __init__(self,
-                 problem: Problem,
-                 population_size: int,
-                 offspring_population_size: int,
-                 mutation: Mutation,
-                 crossover: Crossover,
-                 selection: Selection,
-                 termination_criterion: TerminationCriterion = store.default_termination_criteria,
-                 population_generator: Generator = store.default_generator,
-                 population_evaluator: Evaluator = store.default_evaluator):
+
    
+[docs]
+class GeneticAlgorithm(EvolutionaryAlgorithm[S, R]):
+    def __init__(
+        self,
+        problem: Problem,
+        population_size: int,
+        offspring_population_size: int,
+        mutation: Mutation,
+        crossover: Crossover,
+        selection: Selection = BinaryTournamentSelection(ObjectiveComparator(0)),
+        termination_criterion: TerminationCriterion = store.default_termination_criteria,
+        population_generator: Generator = store.default_generator,
+        population_evaluator: Evaluator = store.default_evaluator,
+            solution_comparator: Comparator = ObjectiveComparator(0)
+    ):
         super(GeneticAlgorithm, self).__init__(
-            problem=problem,
-            population_size=population_size,
-            offspring_population_size=offspring_population_size)
+            problem=problem, population_size=population_size, offspring_population_size=offspring_population_size
+        )
         self.mutation_operator = mutation
         self.crossover_operator = crossover
+        self.solution_comparator = solution_comparator
+
         self.selection_operator = selection
 
         self.population_generator = population_generator
@@ -156,37 +161,52 @@ 
    Source code for jmetal.algorithm.singleobjective.genetic_algorithm
    self.termination_criterion = termination_criterion
         self.observable.register(termination_criterion)
 
-        self.mating_pool_size = \
-            self.offspring_population_size * \
-            self.crossover_operator.get_number_of_parents() // self.crossover_operator.get_number_of_children()
+        self.mating_pool_size = (
+            self.offspring_population_size
+            * self.crossover_operator.get_number_of_parents()
+            // self.crossover_operator.get_number_of_children()
+        )
 
         if self.mating_pool_size < self.crossover_operator.get_number_of_children():
             self.mating_pool_size = self.crossover_operator.get_number_of_children()
 
-
    [docs]    def create_initial_solutions(self) -> List[S]:
-        return [self.population_generator.new(self.problem)
-                for _ in range(self.population_size)]
    
+
    
+[docs]
+    def create_initial_solutions(self) -> List[S]:
+        return [self.population_generator.new(self.problem) for _ in range(self.population_size)]
    
 
-
    [docs]    def evaluate(self, population: List[S]):
+
+
    
+[docs]
+    def evaluate(self, population: List[S]):
         return self.population_evaluator.evaluate(population, self.problem)
    
 
-
    [docs]    def stopping_condition_is_met(self) -> bool:
+
+
    
+[docs]
+    def stopping_condition_is_met(self) -> bool:
         return self.termination_criterion.is_met
    
 
-
    [docs]    def selection(self, population: List[S]):
+
+
    
+[docs]
+    def selection(self, population: List[S]):
         mating_population = []
 
-        for i in range(self.mating_pool_size):
+        for _ in range(self.mating_pool_size):
             solution = self.selection_operator.execute(population)
             mating_population.append(solution)
 
         return mating_population
    
 
-
    [docs]    def reproduction(self, mating_population: List[S]) -> List[S]:
+
+
    
+[docs]
+    def reproduction(self, mating_population: List[S]) -> List[S]:
         number_of_parents_to_combine = self.crossover_operator.get_number_of_parents()
 
         if len(mating_population) % number_of_parents_to_combine != 0:
-            raise Exception('Wrong number of parents')
+            raise Exception("Wrong number of parents")
 
         offspring_population = []
         for i in range(0, self.offspring_population_size, number_of_parents_to_combine):
@@ -204,18 +224,29 @@ 
    Source code for jmetal.algorithm.singleobjective.genetic_algorithm
    return offspring_population
    
 
-
    [docs]    def replacement(self, population: List[S], offspring_population: List[S]) -> List[S]:
+
+
    
+[docs]
+    def replacement(self, population: List[S], offspring_population: List[S]) -> List[S]:
         population.extend(offspring_population)
 
-        population.sort(key=lambda s: s.objectives[0])
+        population.sort(key=cmp_to_key(self.solution_comparator.compare))
+
+        return population[: self.population_size]
    
 
-        return population[:self.population_size]
    
 
-
    [docs]    def get_result(self) -> R:
+
    
+[docs]
+    def result(self) -> R:
         return self.solutions[0]
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Genetic algorithm'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Genetic algorithm"
    
+
    
+
    @@ -232,8 +263,9 @@

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  • jmetal.algorithm.singleobjective.genetic_algorithm
    • - - - - - - - + jmetal.algorithm.singleobjective.local_search — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.algorithm.singleobjective.local_search
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -113,7 +110,7 @@

    Source code for jmetal.algorithm.singleobjective.local_search

    import random import threading import time -from typing import TypeVar, List +from typing import List, TypeVar from jmetal.config import store from jmetal.core.algorithm import Algorithm @@ -123,8 +120,8 @@

    Source code for jmetal.algorithm.singleobjective.local_search

    from jmetal.util.comparator import Comparator from jmetal.util.termination_criterion import TerminationCriterion -S = TypeVar('S') -R = TypeVar('R') +S = TypeVar("S") +R = TypeVar("R") """ .. module:: local_search @@ -135,13 +132,16 @@

    Source code for jmetal.algorithm.singleobjective.local_search

    """ -
    [docs]class LocalSearch(Algorithm[S, R], threading.Thread): - - def __init__(self, - problem: Problem[S], - mutation: Mutation, - termination_criterion: TerminationCriterion = store.default_termination_criteria, - comparator: Comparator = store.default_comparator): +
    +[docs] +class LocalSearch(Algorithm[S, R], threading.Thread): + def __init__( + self, + problem: Problem[S], + mutation: Mutation, + termination_criterion: TerminationCriterion = store.default_termination_criteria, + comparator: Comparator = store.default_comparator, + ): super(LocalSearch, self).__init__() self.comparator = comparator self.problem = problem @@ -149,20 +149,34 @@

    Source code for jmetal.algorithm.singleobjective.local_search

    self.termination_criterion = termination_criterion self.observable.register(termination_criterion) -
    [docs] def create_initial_solutions(self) -> List[S]: +
    +[docs] + def create_initial_solutions(self) -> List[S]: self.solutions.append(self.problem.create_solution()) return self.solutions
    -
    [docs] def evaluate(self, solutions: List[S]) -> List[S]: + +
    +[docs] + def evaluate(self, solutions: List[S]) -> List[S]: return [self.problem.evaluate(solutions[0])]
    -
    [docs] def stopping_condition_is_met(self) -> bool: + +
    +[docs] + def stopping_condition_is_met(self) -> bool: return self.termination_criterion.is_met
    -
    [docs] def init_progress(self) -> None: + +
    +[docs] + def init_progress(self) -> None: self.evaluations = 0
    -
    [docs] def step(self) -> None: + +
    +[docs] + def step(self) -> None: mutated_solution = copy.deepcopy(self.solutions[0]) mutated_solution: Solution = self.mutation.execute(mutated_solution) mutated_solution = self.evaluate([mutated_solution])[0] @@ -176,22 +190,40 @@

    Source code for jmetal.algorithm.singleobjective.local_search

    if random.random() < 0.5: self.solutions[0] = mutated_solution
    -
    [docs] def update_progress(self) -> None: + +
    +[docs] + def update_progress(self) -> None: self.evaluations += 1 - observable_data = self.get_observable_data() + observable_data = self.observable_data() self.observable.notify_all(**observable_data)
    -
    [docs] def get_observable_data(self) -> dict: + +
    +[docs] + def observable_data(self) -> dict: ctime = time.time() - self.start_computing_time - return {'PROBLEM': self.problem, 'EVALUATIONS': self.evaluations, 'SOLUTIONS': self.get_result(), - 'COMPUTING_TIME': ctime}
    + return { + "PROBLEM": self.problem, + "EVALUATIONS": self.evaluations, + "SOLUTIONS": self.result(), + "COMPUTING_TIME": ctime, + }
    + -
    [docs] def get_result(self) -> R: +
    +[docs] + def result(self) -> R: return self.solutions[0]
    -
    [docs] def get_name(self) -> str: - return 'LS'
    + +
    +[docs] + def get_name(self) -> str: + return "LS"
    +
    +
    @@ -208,8 +240,9 @@

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  • jmetal.algorithm.singleobjective.local_search
    • - - - - - - - + jmetal.algorithm.singleobjective.simulated_annealing — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.algorithm.singleobjective.simulated_annealing
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -113,73 +110,95 @@

    Source code for jmetal.algorithm.singleobjective.simulated_annealing

    import random import threading import time -from typing import TypeVar, List +from typing import List, TypeVar import numpy +from jmetal.config import store from jmetal.core.algorithm import Algorithm from jmetal.core.operator import Mutation from jmetal.core.problem import Problem from jmetal.core.solution import Solution +from jmetal.util.generator import Generator from jmetal.util.termination_criterion import TerminationCriterion -S = TypeVar('S') -R = TypeVar('R') +S = TypeVar("S") +R = TypeVar("R") """ .. module:: simulated_annealing :platform: Unix, Windows - :synopsis: Implementation of Local search. + :synopsis: Implementation of Simulated Annealing. .. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es>, Antonio Benítez-Hidalgo <antonio.b@uma.es> """ -
    [docs]class SimulatedAnnealing(Algorithm[S, R], threading.Thread): - - def __init__(self, - problem: Problem[S], - mutation: Mutation, - termination_criterion: TerminationCriterion): +
    +[docs] +class SimulatedAnnealing(Algorithm[S, R], threading.Thread): + def __init__( + self, + problem: Problem[S], + mutation: Mutation, + termination_criterion: TerminationCriterion, + solution_generator: Generator = store.default_generator, + ): super(SimulatedAnnealing, self).__init__() self.problem = problem self.mutation = mutation self.termination_criterion = termination_criterion + self.solution_generator = solution_generator self.observable.register(termination_criterion) self.temperature = 1.0 self.minimum_temperature = 0.000001 self.alpha = 0.95 self.counter = 0 -
    [docs] def create_initial_solutions(self) -> List[S]: - self.solutions.append(self.problem.create_solution()) - return self.solutions
    +
    +[docs] + def create_initial_solutions(self) -> List[S]: + return [self.solution_generator.new(self.problem)]
    -
    [docs] def evaluate(self, solutions: List[S]) -> List[S]: + +
    +[docs] + def evaluate(self, solutions: List[S]) -> List[S]: return [self.problem.evaluate(solutions[0])]
    -
    [docs] def stopping_condition_is_met(self) -> bool: + +
    +[docs] + def stopping_condition_is_met(self) -> bool: return self.termination_criterion.is_met
    -
    [docs] def init_progress(self) -> None: + +
    +[docs] + def init_progress(self) -> None: self.evaluations = 0
    -
    [docs] def step(self) -> None: + +
    +[docs] + def step(self) -> None: mutated_solution = copy.deepcopy(self.solutions[0]) mutated_solution: Solution = self.mutation.execute(mutated_solution) mutated_solution = self.evaluate([mutated_solution])[0] acceptance_probability = self.compute_acceptance_probability( - self.solutions[0].objectives[0], - mutated_solution.objectives[0], - self.temperature) + self.solutions[0].objectives[0], mutated_solution.objectives[0], self.temperature + ) if acceptance_probability > random.random(): self.solutions[0] = mutated_solution self.temperature *= self.alpha
    -
    [docs] def compute_acceptance_probability(self, current: float, new: float, temperature: float) -> float: + +
    +[docs] + def compute_acceptance_probability(self, current: float, new: float, temperature: float) -> float: if new < current: return 1.0 else: @@ -187,22 +206,40 @@

    Source code for jmetal.algorithm.singleobjective.simulated_annealing

    value = (new - current) / t return numpy.exp(-1.0 * value)
    -
    [docs] def update_progress(self) -> None: + +
    +[docs] + def update_progress(self) -> None: self.evaluations += 1 - observable_data = self.get_observable_data() + observable_data = self.observable_data() self.observable.notify_all(**observable_data)
    -
    [docs] def get_observable_data(self) -> dict: + +
    +[docs] + def observable_data(self) -> dict: ctime = time.time() - self.start_computing_time - return {'PROBLEM': self.problem, 'EVALUATIONS': self.evaluations, 'SOLUTIONS': self.get_result(), - 'COMPUTING_TIME': ctime}
    + return { + "PROBLEM": self.problem, + "EVALUATIONS": self.evaluations, + "SOLUTIONS": self.result(), + "COMPUTING_TIME": ctime, + }
    + -
    [docs] def get_result(self) -> R: +
    +[docs] + def result(self) -> R: return self.solutions[0]
    -
    [docs] def get_name(self) -> str: - return 'Simulated Annealing'
    + +
    +[docs] + def get_name(self) -> str: + return "Simulated Annealing"
    +
    +
    @@ -219,8 +256,9 @@

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  • jmetal.algorithm.singleobjective.simulated_annealing
    • - - - - - - - + jmetal.core.observer — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.core.observer
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -109,7 +106,7 @@

    Table Of Contents

    Source code for jmetal.core.observer

    -from abc import abstractmethod, ABC
+from abc import ABC, abstractmethod
 
 """
 .. module:: Observable
@@ -120,33 +117,49 @@ 
    Source code for jmetal.core.observer
     """
 
 
-
    [docs]class Observer(ABC):
-
-
    [docs]    @abstractmethod
+
    
+[docs]
+class Observer(ABC):
+
    
+[docs]
+    @abstractmethod
     def update(self, *args, **kwargs):
-        """ Update method.
-        """
-        pass
    
+        """Update method."""
+        pass
    
+
    
 
 
-
    [docs]class Observable(ABC):
 
-
    [docs]    @abstractmethod
+
    
+[docs]
+class Observable(ABC):
+
    
+[docs]
+    @abstractmethod
     def register(self, observer):
         pass
    
 
-
    [docs]    @abstractmethod
+
+
    
+[docs]
+    @abstractmethod
     def deregister(self, observer):
         pass
    
 
-
    [docs]    @abstractmethod
+
+
    
+[docs]
+    @abstractmethod
     def deregister_all(self):
         pass
    
 
-
    [docs]    @abstractmethod
-    def notify_all(self, *args, **kwargs):
-        pass
    
 
+
    
+[docs]
+    @abstractmethod
+    def notify_all(self, *args, **kwargs):
+        pass
    
+
    
 
 
    
 
@@ -164,8 +177,9 @@ 
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  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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-          
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    •  
+        
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+          
  • Module code »
    • 
+        
  • jmetal.core.observer
    •
    - - - - - - - + jmetal.core.problem — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.core.problem
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -109,123 +106,209 @@

    Table Of Contents

    Source code for jmetal.core.problem

    -import logging
-import random
+import random
 from abc import ABC, abstractmethod
-from typing import Generic, TypeVar, List
+from typing import Generic, List, TypeVar
 
 from jmetal.core.observer import Observer
-from jmetal.core.solution import BinarySolution, FloatSolution, IntegerSolution, PermutationSolution
+from jmetal.core.solution import (
+    BinarySolution,
+    FloatSolution,
+    IntegerSolution,
+    PermutationSolution,
+)
+from jmetal.logger import get_logger
 
-LOGGER = logging.getLogger('jmetal')
+logger = get_logger(__name__)
 
-S = TypeVar('S')
+S = TypeVar("S")
 
 
-
    [docs]class Problem(Generic[S], ABC):
-    """ Class representing problems. """
+
    
+[docs]
+class Problem(Generic[S], ABC):
+    """Class representing problems."""
 
     MINIMIZE = -1
     MAXIMIZE = 1
 
     def __init__(self):
-        self.number_of_variables: int = 0
-        self.number_of_objectives: int = 0
-        self.number_of_constraints: int = 0
-
-        self.reference_front: List[S] = None
-
+        self.reference_front: List[S] = []
         self.directions: List[int] = []
         self.labels: List[str] = []
 
-
    [docs]    @abstractmethod
+
    
+[docs]
+    @abstractmethod
+    def number_of_variables(self) -> int:
+        pass
    
+
+
+
    
+[docs]
+    @abstractmethod
+    def number_of_objectives(self) -> int:
+        pass
    
+
+
+
    
+[docs]
+    @abstractmethod
+    def number_of_constraints(self) -> int:
+        pass
    
+
+
+
    
+[docs]
+    @abstractmethod
     def create_solution(self) -> S:
-        """ Creates a random_search solution to the problem.
+        """Creates a random_search solution to the problem.
 
-        :return: Solution. """
+        :return: Solution."""
         pass
    
 
-
    [docs]    @abstractmethod
+
+
    
+[docs]
+    @abstractmethod
     def evaluate(self, solution: S) -> S:
-        """ Evaluate a solution. For any new problem inheriting from :class:`Problem`, this method should be
-        replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.
+        """Evaluate a solution. For any new problem inheriting from :class:`Problem`, this method should be replaced.
+        Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.
 
-        :return: Evaluated solution. """
+        :return: Evaluated solution."""
         pass
    
 
-
    [docs]    @abstractmethod
-    def get_name(self) -> str:
-        pass
    
+
+
    
+[docs]
+    @abstractmethod
+    def name(self) -> str:
+        pass
    
+
    
 
 
-
    [docs]class DynamicProblem(Problem[S], Observer, ABC):
 
-
    [docs]    @abstractmethod
+
    
+[docs]
+class DynamicProblem(Problem[S], Observer, ABC):
+
    
+[docs]
+    @abstractmethod
     def the_problem_has_changed(self) -> bool:
         pass
    
 
-
    [docs]    @abstractmethod
+
+
    
+[docs]
+    @abstractmethod
     def clear_changed(self) -> None:
-        pass
    
+        pass
    
+
    
+
 
 
-
    [docs]class BinaryProblem(Problem[BinarySolution], ABC):
-    """ Class representing binary problems. """
+
    
+[docs]
+class BinaryProblem(Problem[BinarySolution], ABC):
+    """Class representing binary problems."""
 
     def __init__(self):
-        super(BinaryProblem, self).__init__()
    
+        super(BinaryProblem, self).__init__()
+
+        self.number_of_bits_per_variable = []
 
+
    
+[docs]
+    def number_of_bits_per_variable_list(self):
+        return self.number_of_bits_per_variable
    
+
+
+
    
+[docs]
+    def total_number_of_bits(self):
+        return sum(self.number_of_bits_per_variable)
    
+
    
 
-
    [docs]class FloatProblem(Problem[FloatSolution], ABC):
-    """ Class representing float problems. """
+
+
+
    
+[docs]
+class FloatProblem(Problem[FloatSolution], ABC):
+    """Class representing float problems."""
 
     def __init__(self):
         super(FloatProblem, self).__init__()
         self.lower_bound = []
         self.upper_bound = []
 
-
    [docs]    def create_solution(self) -> FloatSolution:
+
    
+[docs]
+    def number_of_variables(self) -> int:
+        return len(self.lower_bound)
    
+
+
+
    
+[docs]
+    def create_solution(self) -> FloatSolution:
         new_solution = FloatSolution(
-            self.lower_bound,
-            self.upper_bound,
-            self.number_of_objectives,
-            self.number_of_constraints)
-        new_solution.variables = \
-            [random.uniform(self.lower_bound[i]*1.0, self.upper_bound[i]*1.0) for i in range(self.number_of_variables)]
+            self.lower_bound, self.upper_bound, self.number_of_objectives(), self.number_of_constraints()
+        )
+        new_solution.variables = [
+            random.uniform(self.lower_bound[i] * 1.0, self.upper_bound[i] * 1.0)
+            for i in range(self.number_of_variables())
+        ]
+
+        return new_solution
    
+
    
 
-        return new_solution
    
 
 
-
    [docs]class IntegerProblem(Problem[IntegerSolution], ABC):
-    """ Class representing integer problems. """
+
    
+[docs]
+class IntegerProblem(Problem[IntegerSolution], ABC):
+    """Class representing integer problems."""
 
     def __init__(self):
         super(IntegerProblem, self).__init__()
-        self.lower_bound = None
-        self.upper_bound = None
+        self.lower_bound = []
+        self.upper_bound = []
+
+
    
+[docs]
+    def number_of_variables(self) -> int:
+        return len(self.lower_bound)
    
+
 
-
    [docs]    def create_solution(self) -> IntegerSolution:
+
    
+[docs]
+    def create_solution(self) -> IntegerSolution:
         new_solution = IntegerSolution(
-            self.lower_bound,
-            self.upper_bound,
-            self.number_of_objectives,
-            self.number_of_constraints)
-        new_solution.variables = \
-            [int(random.uniform(self.lower_bound[i]*1.0, self.upper_bound[i]*1.0))
-             for i in range(self.number_of_variables)]
+            self.lower_bound, self.upper_bound, self.number_of_objectives(), self.number_of_constraints()
+        )
+        new_solution.variables = [
+            round(random.uniform(self.lower_bound[i] * 1.0, self.upper_bound[i] * 1.0))
+            for i in range(self.number_of_variables())
+        ]
+
+        return new_solution
    
+
    
 
-        return new_solution
    
 
 
-
    [docs]class PermutationProblem(Problem[PermutationSolution], ABC):
-    """ Class representing permutation problems. """
+
    
+[docs]
+class PermutationProblem(Problem[PermutationSolution], ABC):
+    """Class representing permutation problems."""
 
     def __init__(self):
         super(PermutationProblem, self).__init__()
    
 
 
-
    [docs]class OnTheFlyFloatProblem(FloatProblem):
-    """ Class for defining float problems on the fly.
+
+
    
+[docs]
+class OnTheFlyFloatProblem(FloatProblem):
+    """ Class for defining float problems on the fly.
 
         Example:
 
@@ -251,45 +334,74 @@ 
    Source code for jmetal.core.problem
                 .add_constraint(c1)\
             .add_constraint(c2)
     """
+
     def __init__(self):
         super(OnTheFlyFloatProblem, self).__init__()
         self.functions = []
         self.constraints = []
-        self.name = None
+        self.problem_name = None
 
-
    [docs]    def set_name(self, name):
-        self.name = name
+
    
+[docs]
+    def set_name(self, name) -> "OnTheFlyFloatProblem":
+        self.problem_name = name
 
         return self
    
 
-
    [docs]    def add_function(self, function):
+
+
    
+[docs]
+    def add_function(self, function) -> "OnTheFlyFloatProblem":
         self.functions.append(function)
-        self.number_of_objectives += 1
 
         return self
    
 
-
    [docs]    def add_constraint(self, constraint):
+
+
    
+[docs]
+    def add_constraint(self, constraint) -> "OnTheFlyFloatProblem":
         self.constraints.append(constraint)
-        self.number_of_constraints += 1
 
         return self
    
 
-
    [docs]    def add_variable(self, lower_bound, upper_bound):
+
+
    
+[docs]
+    def add_variable(self, lower_bound, upper_bound) -> "OnTheFlyFloatProblem":
         self.lower_bound.append(lower_bound)
         self.upper_bound.append(upper_bound)
-        self.number_of_variables += 1
 
         return self
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution):
-        for i in range(self.number_of_objectives):
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.functions)
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return len(self.constraints)
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> None:
+        for i in range(self.number_of_objectives()):
             solution.objectives[i] = self.functions[i](solution.variables)
 
-        for i in range(self.number_of_constraints):
+        for i in range(self.number_of_constraints()):
             solution.constraints[i] = self.constraints[i](solution.variables)
    
 
-
    [docs]    def get_name(self) -> str:
-        return self.name
    
+
+
    
+[docs]
+    def name(self) -> str:
+        return self.problem_name
    
+
    
+
 
    
 
           
    
@@ -306,8 +418,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.core.problem
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.experiment — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.experiment
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -110,7 +107,6 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.lab.experiment
     import io
-import logging
 import os
 from concurrent.futures import ProcessPoolExecutor
 from pathlib import Path
@@ -120,13 +116,18 @@ 
    Source code for jmetal.lab.experiment
     import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from scipy.stats import mannwhitneyu
+from scipy.stats import mannwhitneyu, iqr, ks_2samp
 
 from jmetal.core.algorithm import Algorithm
 from jmetal.core.quality_indicator import QualityIndicator
-from jmetal.util.solution import print_function_values_to_file, print_variables_to_file, read_solutions
+from jmetal.logger import get_logger
+from jmetal.util.solution import (
+    print_function_values_to_file,
+    print_variables_to_file,
+    read_solutions,
+)
 
-LOGGER = logging.getLogger('jmetal')
+logger = get_logger(__name__)
 
 """
 .. module:: laboratory
@@ -137,33 +138,45 @@ 
    Source code for jmetal.lab.experiment
     """
 
 
-
    [docs]class Job:
-
+
    
+[docs]
+class Job:
     def __init__(self, algorithm: Algorithm, algorithm_tag: str, problem_tag: str, run: int):
         self.algorithm = algorithm
         self.algorithm_tag = algorithm_tag
         self.problem_tag = problem_tag
         self.run_tag = run
 
-
    [docs]    def execute(self, output_path: str = ''):
+
    
+[docs]
+    def execute(self, output_path: str = ""):
         self.algorithm.run()
 
         if output_path:
-            file_name = os.path.join(output_path, 'FUN.{}.tsv'.format(self.run_tag))
-            print_function_values_to_file(self.algorithm.get_result(), filename=file_name)
+            file_name = os.path.join(output_path, "FUN.{}.tsv".format(self.run_tag))
+            print_function_values_to_file(self.algorithm.result(), filename=file_name)
 
-            file_name = os.path.join(output_path, 'VAR.{}.tsv'.format(self.run_tag))
-            print_variables_to_file(self.algorithm.get_result(), filename=file_name)
+            file_name = os.path.join(output_path, "VAR.{}.tsv".format(self.run_tag))
+            print_variables_to_file(self.algorithm.result(), filename=file_name)
 
-            file_name = os.path.join(output_path, 'TIME.{}'.format(self.run_tag))
-            with open(file_name, 'w+') as of:
-                of.write(str(self.algorithm.total_computing_time))
    
+            file_name = os.path.join(output_path, "TIME.{}".format(self.run_tag))
+            with open(file_name, "w+") as of:
+                of.write(str(self.algorithm.total_computing_time))
    
 
 
-
    [docs]class Experiment:
+
    
+[docs]
+    def get_algorithm_data(self):
+        return self.algorithm.observable_data()
    
+
    
+
 
+
+
    
+[docs]
+class Experiment:
     def __init__(self, output_dir: str, jobs: List[Job], m_workers: int = 6):
-        """ Run an experiment to execute a list of jobs.
+        """Run an experiment to execute a list of jobs.
 
         :param output_dir: Base directory where each job will save its results.
         :param jobs: List of Jobs (from :py:mod:`jmetal.util.laboratory)`) to be executed.
@@ -172,17 +185,26 @@ 
    Source code for jmetal.lab.experiment
             self.jobs = jobs
         self.m_workers = m_workers
         self.output_dir = output_dir
+        self.job_data = []
 
-
    [docs]    def run(self) -> None:
+
    
+[docs]
+    def run(self) -> None:
         with ProcessPoolExecutor(max_workers=self.m_workers) as executor:
             for job in self.jobs:
                 output_path = os.path.join(self.output_dir, job.algorithm_tag, job.problem_tag)
-                executor.submit(job.execute(output_path))
    
+                executor.submit(job.execute(output_path))
+                self.job_data.append(job.get_algorithm_data())
    
+
    
+
 
 
-
    [docs]def generate_summary_from_experiment(input_dir: str, quality_indicators: List[QualityIndicator],
-                                     reference_fronts: str = ''):
-    """ Compute a list of quality indicators. The input data directory *must* met the following structure (this is generated
+
    
+[docs]
+def generate_summary_from_experiment(
+        input_dir: str, quality_indicators: List[QualityIndicator], reference_fronts: str = ""
+):
+    """Compute a list of quality indicators. The input data directory *must* met the following structure (this is generated
     automatically by the Experiment class):
 
     * <base_dir>
@@ -206,52 +228,57 @@ 
    Source code for jmetal.lab.experiment
         if not quality_indicators:
         quality_indicators = []
 
-    with open('QualityIndicatorSummary.csv', 'w+') as of:
-        of.write('Algorithm,Problem,ExecutionId,IndicatorName,IndicatorValue\n')
+    with open("QualityIndicatorSummary.csv", "w+") as of:
+        of.write("Algorithm,Problem,ExecutionId,IndicatorName,IndicatorValue\n")
 
     for dirname, _, filenames in os.walk(input_dir):
         for filename in filenames:
             try:
                 # Linux filesystem
-                algorithm, problem = dirname.split('/')[-2:]
+                algorithm, problem = dirname.split("/")[-2:]
             except ValueError:
                 # Windows filesystem
-                algorithm, problem = dirname.split('\\')[-2:]
+                algorithm, problem = dirname.split("\\")[-2:]
 
-            if 'TIME' in filename:
-                run_tag = [s for s in filename.split('.') if s.isdigit()].pop()
+            if "TIME" in filename:
+                run_tag = [s for s in filename.split(".") if s.isdigit()].pop()
 
-                with open(os.path.join(dirname, filename), 'r') as content_file:
+                with open(os.path.join(dirname, filename), "r") as content_file:
                     content = content_file.read()
 
-                with open('QualityIndicatorSummary.csv', 'a+') as of:
-                    of.write(','.join([algorithm, problem, run_tag, 'Time', str(content)]))
-                    of.write('\n')
+                with open("QualityIndicatorSummary.csv", "a+") as of:
+                    of.write(",".join([algorithm, problem, run_tag, "Time", str(content)]))
+                    of.write("\n")
 
-            if 'FUN' in filename:
+            if "FUN" in filename:
                 solutions = read_solutions(os.path.join(dirname, filename))
-                run_tag = [s for s in filename.split('.') if s.isdigit()].pop()
-
+                run_tag = [s for s in filename.split(".") if s.isdigit()].pop()
                 for indicator in quality_indicators:
-                    reference_front_file = os.path.join(reference_fronts, problem + '.pf')
+                    reference_front_file = os.path.join(reference_fronts, problem + ".pf")
 
                     # Add reference front if any
-                    if hasattr(indicator, 'reference_front'):
+                    if hasattr(indicator, "reference_front"):
                         if Path(reference_front_file).is_file():
-                            indicator.reference_front = read_solutions(reference_front_file)
+                            reference_front = []
+                            with open(reference_front_file) as file:
+                                for line in file:
+                                    reference_front.append([float(x) for x in line.split()])
+
+                            indicator.reference_front = reference_front
                         else:
-                            LOGGER.warning('Reference front not found at', reference_front_file)
+                            logger.warning("Reference front not found at", reference_front_file)
 
-                    result = indicator.compute(solutions)
+                    result = indicator.compute([solutions[i].objectives for i in range(len(solutions))])
 
                     # Save quality indicator value to file
-                    with open('QualityIndicatorSummary.csv', 'a+') as of:
-                        of.write(','.join([algorithm, problem, run_tag, indicator.get_name(), str(result)]))
-                        of.write('\n')
    
+                    with open("QualityIndicatorSummary.csv", "a+") as of:
+                        of.write(",".join([algorithm, problem, run_tag, indicator.get_short_name(), str(result)]))
+                        of.write("\n")
    
+
 
 
-def generate_boxplot(filename: str, output_dir: str = 'boxplot'):
-    """ Generate boxplot diagrams.
+def generate_boxplot(filename: str, output_dir: str = "boxplot"):
+    """Generate boxplot diagrams.
 
     :param filename: Input filename (summary).
     :param output_dir: Output path.
@@ -259,31 +286,32 @@ 
    Source code for jmetal.lab.experiment
         df = pd.read_csv(filename, skipinitialspace=True)
 
     if len(set(df.columns.tolist())) != 5:
-        raise Exception('Wrong number of columns')
+        raise Exception("Wrong number of columns")
 
     if Path(output_dir).is_dir():
-        LOGGER.warning('Directory {} exists. Removing contents.'.format(output_dir))
+        logger.warning("Directory {} exists. Removing contents.".format(output_dir))
         for file in os.listdir(output_dir):
-            os.remove('{0}/{1}'.format(output_dir, file))
+            os.remove("{0}/{1}".format(output_dir, file))
     else:
-        LOGGER.warning('Directory {} does not exist. Creating it.'.format(output_dir))
+        logger.warning("Directory {} does not exist. Creating it.".format(output_dir))
         Path(output_dir).mkdir(parents=True)
 
-    algorithms = pd.unique(df['Algorithm'])
-    problems = pd.unique(df['Problem'])
-    indicators = pd.unique(df['IndicatorName'])
+    algorithms = pd.unique(df["Algorithm"])
+    problems = pd.unique(df["Problem"])
+    indicators = pd.unique(df["IndicatorName"])
 
     # We consider the quality indicator indicator_name
 
     for indicator_name in indicators:
-        data = df[df['IndicatorName'] == indicator_name]
+        data = df[df["IndicatorName"] == indicator_name]
 
         for pr in problems:
             data_to_plot = []
 
             for alg in algorithms:
-                data_to_plot.append(data['IndicatorValue'][np.logical_and(
-                    data['Algorithm'] == alg, data['Problem'] == pr)])
+                data_to_plot.append(
+                    data["IndicatorValue"][np.logical_and(data["Algorithm"] == alg, data["Problem"] == pr)]
+                )
 
             # Create a figure instance
             fig = plt.figure(1, figsize=(9, 6))
@@ -295,13 +323,13 @@ 
    Source code for jmetal.lab.experiment
                 ax.set_xticklabels(algorithms)
             ax.tick_params(labelsize=20)
 
-            plt.savefig(os.path.join(output_dir, 'boxplot-{}-{}.png'.format(pr, indicator_name)), bbox_inches='tight')
-            plt.savefig(os.path.join(output_dir, 'boxplot-{}-{}.eps'.format(pr, indicator_name)), bbox_inches='tight')
+            plt.savefig(os.path.join(output_dir, "boxplot-{}-{}.png".format(pr, indicator_name)), bbox_inches="tight")
+            plt.savefig(os.path.join(output_dir, "boxplot-{}-{}.eps".format(pr, indicator_name)), bbox_inches="tight")
             plt.close(fig)
 
 
-def generate_latex_tables(filename: str, output_dir: str = 'latex/statistical'):
-    """ Computes a number of statistical values (mean, median, standard deviation, interquartile range).
+def generate_latex_tables(filename: str, output_dir: str = "latex/statistical"):
+    """Computes a number of statistical values (mean, median, standard deviation, interquartile range).
 
     :param filename: Input filename (summary).
     :param output_dir: Output path.
@@ -309,24 +337,24 @@ 
    Source code for jmetal.lab.experiment
         df = pd.read_csv(filename, skipinitialspace=True)
 
     if len(set(df.columns.tolist())) != 5:
-        raise Exception('Wrong number of columns')
+        raise Exception("Wrong number of columns")
 
     if Path(output_dir).is_dir():
-        LOGGER.warning('Directory {} exists. Removing contents.'.format(output_dir))
+        logger.warning("Directory {} exists. Removing contents.".format(output_dir))
         for file in os.listdir(output_dir):
-            os.remove('{0}/{1}'.format(output_dir, file))
+            os.remove("{0}/{1}".format(output_dir, file))
     else:
-        LOGGER.warning('Directory {} does not exist. Creating it.'.format(output_dir))
+        logger.warning("Directory {} does not exist. Creating it.".format(output_dir))
         Path(output_dir).mkdir(parents=True)
 
     # Generate median & iqr tables
     median, iqr = pd.DataFrame(), pd.DataFrame()
     mean, std = pd.DataFrame(), pd.DataFrame()
 
-    for algorithm_name, subset in df.groupby('Algorithm', sort=False):
-        subset = subset.drop('Algorithm', axis=1)
-        subset = subset.rename(columns={'IndicatorValue': algorithm_name})
-        subset = subset.set_index(['Problem', 'IndicatorName', 'ExecutionId'])
+    for algorithm_name, subset in df.groupby("Algorithm", sort=False):
+        subset = subset.drop("Algorithm", axis=1)
+        subset = subset.rename(columns={"IndicatorValue": algorithm_name})
+        subset = subset.set_index(["Problem", "IndicatorName", "ExecutionId"])
 
         # Compute Median and Interquartile range
         median_ = subset.groupby(level=[0, 1]).median()
@@ -343,83 +371,83 @@ 
    Source code for jmetal.lab.experiment
             std = pd.concat([std, std_], axis=1)
 
     # Generate mean & std tables
-    for indicator_name, subset in std.groupby('IndicatorName', sort=False):
-        subset = median.groupby('IndicatorName', sort=False).get_group(indicator_name)
+    for indicator_name, subset in std.groupby("IndicatorName", sort=False):
+        subset = median.groupby("IndicatorName", sort=False).get_group(indicator_name)
         subset.index = subset.index.droplevel(1)
-        subset.to_csv(os.path.join(output_dir, 'Median-{}.csv'.format(indicator_name)), sep='\t', encoding='utf-8')
+        subset.to_csv(os.path.join(output_dir, "Median-{}.csv".format(indicator_name)), sep="\t", encoding="utf-8")
 
-        subset = iqr.groupby('IndicatorName', sort=False).get_group(indicator_name)
+        subset = iqr.groupby("IndicatorName", sort=False).get_group(indicator_name)
         subset.index = subset.index.droplevel(1)
-        subset.to_csv(os.path.join(output_dir, 'IQR-{}.csv'.format(indicator_name)), sep='\t', encoding='utf-8')
+        subset.to_csv(os.path.join(output_dir, "IQR-{}.csv".format(indicator_name)), sep="\t", encoding="utf-8")
 
-        subset = mean.groupby('IndicatorName', sort=False).get_group(indicator_name)
+        subset = mean.groupby("IndicatorName", sort=False).get_group(indicator_name)
         subset.index = subset.index.droplevel(1)
-        subset.to_csv(os.path.join(output_dir, 'Mean-{}.csv'.format(indicator_name)), sep='\t', encoding='utf-8')
+        subset.to_csv(os.path.join(output_dir, "Mean-{}.csv".format(indicator_name)), sep="\t", encoding="utf-8")
 
-        subset = std.groupby('IndicatorName', sort=False).get_group(indicator_name)
+        subset = std.groupby("IndicatorName", sort=False).get_group(indicator_name)
         subset.index = subset.index.droplevel(1)
-        subset.to_csv(os.path.join(output_dir, 'Std-{}.csv'.format(indicator_name)), sep='\t', encoding='utf-8')
+        subset.to_csv(os.path.join(output_dir, "Std-{}.csv".format(indicator_name)), sep="\t", encoding="utf-8")
 
     # Generate LaTeX tables
-    for indicator_name in df.groupby('IndicatorName', sort=False).groups.keys():
+    for indicator_name in df.groupby("IndicatorName", sort=False).groups.keys():
         # Median & IQR
-        md = median.groupby('IndicatorName', sort=False).get_group(indicator_name)
+        md = median.groupby("IndicatorName", sort=False).get_group(indicator_name)
         md.index = md.index.droplevel(1)
 
-        i = iqr.groupby('IndicatorName', sort=False).get_group(indicator_name)
+        i = iqr.groupby("IndicatorName", sort=False).get_group(indicator_name)
         i.index = i.index.droplevel(1)
 
-        with open(os.path.join(output_dir, 'MedianIQR-{}.tex'.format(indicator_name)), 'w') as latex:
+        with open(os.path.join(output_dir, "MedianIQR-{}.tex".format(indicator_name)), "w") as latex:
             latex.write(
                 __averages_to_latex(
                     md,
                     i,
-                    caption='Median and Interquartile Range of the {} quality indicator.'.format(indicator_name),
+                    caption="Median and Interquartile Range of the {} quality indicator.".format(indicator_name),
                     minimization=check_minimization(indicator_name),
-                    label='table:{}'.format(indicator_name)
+                    label="table:{}".format(indicator_name),
                 )
             )
 
         # Mean & Std
-        mn = mean.groupby('IndicatorName', sort=False).get_group(indicator_name)
+        mn = mean.groupby("IndicatorName", sort=False).get_group(indicator_name)
         mn.index = mn.index.droplevel(1)
 
-        s = std.groupby('IndicatorName', sort=False).get_group(indicator_name)
+        s = std.groupby("IndicatorName", sort=False).get_group(indicator_name)
         s.index = s.index.droplevel(1)
 
-        with open(os.path.join(output_dir, 'MeanStd-{}.tex'.format(indicator_name)), 'w') as latex:
+        with open(os.path.join(output_dir, "MeanStd-{}.tex".format(indicator_name)), "w") as latex:
             latex.write(
                 __averages_to_latex(
                     mn,
                     s,
-                    caption='Mean and Standard Deviation of the {} quality indicator.'.format(indicator_name),
+                    caption="Mean and Standard Deviation of the {} quality indicator.".format(indicator_name),
                     minimization=check_minimization(indicator_name),
-                    label='table:{}'.format(indicator_name)
+                    label="table:{}".format(indicator_name),
                 )
             )
 
 
-def compute_wilcoxon(filename: str, output_dir: str = 'latex/wilcoxon'):
-    """
+def compute_wilcoxon(filename: str, output_dir: str = "latex/wilcoxon"):
+    """
     :param filename: Input filename (summary).
     :param output_dir: Output path.
     """
     df = pd.read_csv(filename, skipinitialspace=True)
 
     if len(set(df.columns.tolist())) != 5:
-        raise Exception('Wrong number of columns')
+        raise Exception("Wrong number of columns")
 
     if Path(output_dir).is_dir():
-        LOGGER.warning('Directory {} exists. Removing contents.'.format(output_dir))
+        logger.warning("Directory {} exists. Removing contents.".format(output_dir))
         for file in os.listdir(output_dir):
-            os.remove('{0}/{1}'.format(output_dir, file))
+            os.remove("{0}/{1}".format(output_dir, file))
     else:
-        LOGGER.warning('Directory {} does not exist. Creating it.'.format(output_dir))
+        logger.warning("Directory {} does not exist. Creating it.".format(output_dir))
         Path(output_dir).mkdir(parents=True)
 
-    algorithms = pd.unique(df['Algorithm'])
-    problems = pd.unique(df['Problem'])
-    indicators = pd.unique(df['IndicatorName'])
+    algorithms = pd.unique(df["Algorithm"])
+    problems = pd.unique(df["Problem"])
+    indicators = pd.unique(df["IndicatorName"])
 
     table = pd.DataFrame(index=algorithms[0:-1], columns=algorithms[1:])
 
@@ -431,10 +459,16 @@ 
    Source code for jmetal.lab.experiment
     
                 if i <= j:
                     for problem in problems:
-                        df1 = df[(df["Algorithm"] == row_algorithm) & (df["Problem"] == problem) & (
-                                df["IndicatorName"] == indicator_name)]
-                        df2 = df[(df["Algorithm"] == col_algorithm) & (df["Problem"] == problem) & (
-                                df["IndicatorName"] == indicator_name)]
+                        df1 = df[
+                            (df["Algorithm"] == row_algorithm)
+                            & (df["Problem"] == problem)
+                            & (df["IndicatorName"] == indicator_name)
+                            ]
+                        df2 = df[
+                            (df["Algorithm"] == col_algorithm)
+                            & (df["Problem"] == problem)
+                            & (df["IndicatorName"] == indicator_name)
+                            ]
 
                         data1 = df1["IndicatorValue"]
                         data2 = df2["IndicatorValue"]
@@ -447,49 +481,51 @@ 
    Source code for jmetal.lab.experiment
                             if p <= 0.05:
                             if check_minimization(indicator_name):
                                 if median1 <= median2:
-                                    line.append('+')
+                                    line.append("+")
                                 else:
-                                    line.append('o')
+                                    line.append("o")
                             else:
                                 if median1 >= median2:
-                                    line.append('+')
+                                    line.append("+")
                                 else:
-                                    line.append('o')
+                                    line.append("o")
                         else:
-                            line.append('-')
-                    wilcoxon.append(''.join(line))
+                            line.append("-")
+                    wilcoxon.append("".join(line))
 
-            if len(wilcoxon) < len(algorithms): wilcoxon = [''] * (len(algorithms) - len(wilcoxon) - 1) + wilcoxon
+            if len(wilcoxon) < len(algorithms):
+                wilcoxon = [""] * (len(algorithms) - len(wilcoxon) - 1) + wilcoxon
             table.loc[row_algorithm] = wilcoxon
 
-        table.to_csv(os.path.join(output_dir, 'Wilcoxon-{}.csv'.format(indicator_name)), sep='\t', encoding='utf-8')
+        table.to_csv(os.path.join(output_dir, "Wilcoxon-{}.csv".format(indicator_name)), sep="\t", encoding="utf-8")
 
-        with open(os.path.join(output_dir, 'Wilcoxon-{}.tex'.format(indicator_name)), 'w') as latex:
+        with open(os.path.join(output_dir, "Wilcoxon-{}.tex".format(indicator_name)), "w") as latex:
             latex.write(
                 __wilcoxon_to_latex(
                     table,
-                    caption='Wilcoxon values of the {} quality indicator ({}).'.format(indicator_name,
-                                                                                       ', '.join(problems)),
-                    label='table:{}'.format(indicator_name)
+                    caption="Wilcoxon values of the {} quality indicator ({}).".format(
+                        indicator_name, ", ".join(problems)
+                    ),
+                    label="table:{}".format(indicator_name),
                 )
             )
 
 
 def compute_mean_indicator(filename: str, indicator_name: str):
-    """ Compute the mean values of an indicator.
+    """Compute the mean values of an indicator.
     :param filename:
     :param indicator_name: Quality indicator name.
     """
     df = pd.read_csv(filename, skipinitialspace=True)
 
     if len(set(df.columns.tolist())) != 5:
-        raise Exception('Wrong number of columns')
+        raise Exception("Wrong number of columns")
 
-    algorithms = pd.unique(df['Algorithm'])
-    problems = pd.unique(df['Problem'])
+    algorithms = pd.unique(df["Algorithm"])
+    problems = pd.unique(df["Problem"])
 
     # We consider the quality indicator indicator_name
-    data = df[df['IndicatorName'] == indicator_name]
+    data = df[df["IndicatorName"] == indicator_name]
 
     # Compute for each pair algorithm/problem the average of IndicatorValue
     average_values = np.zeros((problems.size, algorithms.size))
@@ -497,8 +533,9 @@ 
    Source code for jmetal.lab.experiment
         for alg in algorithms:
         i = 0
         for pr in problems:
-            average_values[i, j] = data['IndicatorValue'][np.logical_and(
-                data['Algorithm'] == alg, data['Problem'] == pr)].mean()
+            average_values[i, j] = data["IndicatorValue"][
+                np.logical_and(data["Algorithm"] == alg, data["Problem"] == pr)
+            ].mean()
             i += 1
         j += 1
 
@@ -509,9 +546,170 @@ 
    Source code for jmetal.lab.experiment
         return df
 
 
-def __averages_to_latex(central_tendency: pd.DataFrame, dispersion: pd.DataFrame,
-                        caption: str, label: str, minimization=True, alignment: str = 'c'):
-    """ Convert a pandas DataFrame to a LaTeX tabular. Prints labels in bold and does use math mode.
+
    
+[docs]
+def generate_median_and_wilcoxon_latex_tables(filename: str, output_dir: str = "latex/meansAndWilcoxon"):
+    """Generate Latex tables including medians and IQRs. Additionally, the last algorithm is considered as the reference
+        algorithm, and the cells include a symbol indicating whether the differences with the reference algorithm
+        are significant or not according to the Wilcoxon rank sum test.
+
+    :param filename: Input filename (summary).
+    :param output_dir: Output path.
+    """
+    data = pd.read_csv(filename, skipinitialspace=True)
+
+    if len(set(data.columns.tolist())) != 5:
+        raise Exception("Wrong number of columns")
+
+    if Path(output_dir).is_dir():
+        logger.warning("Directory {} exists. Removing contents.".format(output_dir))
+        for file in os.listdir(output_dir):
+            os.remove("{0}/{1}".format(output_dir, file))
+    else:
+        logger.warning("Directory {} does not exist. Creating it.".format(output_dir))
+        Path(output_dir).mkdir(parents=True)
+
+    algorithms = pd.unique(data["Algorithm"])
+    problems = pd.unique(data["Problem"])
+    indicators = pd.unique(data["IndicatorName"])
+
+    control_algorithm = algorithms[-1]
+
+    # Compute medians and IQRs
+    medians = data.groupby(["Algorithm", "Problem", "IndicatorName"])["IndicatorValue"].median()
+    iqrs = data.groupby(["Algorithm", "Problem", "IndicatorName"])["IndicatorValue"].apply(lambda x: iqr(x))
+
+    # Create data frame to store the Wilcoxon test results
+    wilcoxon_data = pd.DataFrame(columns=["Indicator", "Algorithm", "Problem", "PValue", "Median", "TestResult"])
+
+    for indicator in indicators:
+        for algorithm in algorithms:
+            for problem in problems:
+                algorithm_data = data[(data["Problem"] == problem) & (data["Algorithm"] == algorithm) & (
+                        data["IndicatorName"] == indicator)]
+                ref_data = data[(data["Problem"] == problem) & (data["Algorithm"] == control_algorithm) & (
+                        data["IndicatorName"] == indicator)]
+                stat, p_value = mannwhitneyu(algorithm_data["IndicatorValue"], ref_data["IndicatorValue"])
+
+                test_result = ""
+                if p_value <= 0.05:
+                    if check_minimization(indicator):
+                        if medians[algorithm][problem][indicator] <= medians[control_algorithm][problem][indicator]:
+                            test_result = '+'
+                        else:
+                            test_result = '-'
+                    else:
+                        if medians[algorithm][problem][indicator] >= medians[control_algorithm][problem][indicator]:
+                            test_result = '+'
+                        else:
+                            test_result = '-'
+                else:
+                    test_result = '='
+
+                new_row = {'Indicator': indicator, 'Algorithm': algorithm, "Problem": problem,
+                           "PValue": p_value,
+                           "Median": medians[algorithm][problem][indicator],
+                           "IQR": iqrs[algorithm][problem][indicator],
+                           "TestResult": test_result
+                           }
+                wilcoxon_data = wilcoxon_data._append(new_row, ignore_index=True)
+
+    # Generate LaTeX tables
+    caption = "Median and interquartile range (IQR) of the results of the {} quality indicator. " + \
+              "Cells with dark and light gray background highlights, respectively, the best and second best indicator values. " + \
+              "The algorithm in the last column is the reference " + \
+              "algorithm, and the symbols $+$, $-$ and $\\approx$ indicate that the differences with the reference " + \
+              "algorithm are significantly better, worse, or there is no difference according to the Wilcoxon rank " + \
+              "sum test (confidence level: 95\%)."
+    for indicator_name in indicators:
+        with open(os.path.join(output_dir, "MedianIQRWilcoxon-{}.tex".format(indicator_name)), "w") as latex:
+            latex.write(
+                __median_wilcoxon_to_latex(
+                    indicator_name,
+                    wilcoxon_data,
+                    caption=caption.format(indicator_name),
+                    label="table:{}".format(indicator_name),
+                )
+            )
    
+
+
+
+
    
+[docs]
+def generate_kolmogorov_smirnov_latex_tables(filename: str, output_dir: str = "latex/KolmogorovSmirnov"):
+    """Generate Latex tables with the results of the Kolmogorov-Smirnov test. The last algorithm is considered as
+        the reference algorithm, and the cells include a symbol with the p-value < 0.05.
+
+    :param filename: Input filename (summary).
+    :param output_dir: Output path.
+    """
+    data = pd.read_csv(filename, skipinitialspace=True)
+
+    if len(set(data.columns.tolist())) != 5:
+        raise Exception("Wrong number of columns")
+
+    if Path(output_dir).is_dir():
+        logger.warning("Directory {} exists. Removing contents.".format(output_dir))
+        for file in os.listdir(output_dir):
+            os.remove("{0}/{1}".format(output_dir, file))
+    else:
+        logger.warning("Directory {} does not exist. Creating it.".format(output_dir))
+        Path(output_dir).mkdir(parents=True)
+
+    algorithms = pd.unique(data["Algorithm"])
+    problems = pd.unique(data["Problem"])
+    indicators = pd.unique(data["IndicatorName"])
+
+    control_algorithm = algorithms[-1]
+
+    # Create data frame to store the Kolmogorov Smirnov test results
+    test_data = pd.DataFrame(columns=["Indicator", "Algorithm", "Problem", "PValue", "TestResult"])
+
+    for indicator in indicators:
+        for algorithm in algorithms:
+            for problem in problems:
+                algorithm_data = data[(data["Problem"] == problem) & (data["Algorithm"] == algorithm) & (
+                        data["IndicatorName"] == indicator)]
+                ref_data = data[(data["Problem"] == problem) & (data["Algorithm"] == control_algorithm) & (
+                        data["IndicatorName"] == indicator)]
+                stat, p_value = ks_2samp(algorithm_data["IndicatorValue"], ref_data["IndicatorValue"])
+
+                test_result = stat
+
+                new_row = {'Indicator': indicator, 'Algorithm': algorithm, "Problem": problem,
+                           "PValue": p_value,
+                           "TestResult": test_result
+                           }
+                test_data = test_data._append(new_row, ignore_index=True)
+
+    # Generate LaTeX tables
+    caption = "Kolmogorov-Smirnov Test of the {} quality indicator. " \
+              "The algorithm in the last column is the reference " + \
+              "algorithm and each cell contain the p-value obtained when applying the test with the reference " \
+              "algorithm. Cells with gray background highlight p-values less than 0.05 (i.e., the null hypothesis" \
+              " -- the two distributions are identical -- is rejected)."
+    for indicator_name in indicators:
+        with open(os.path.join(output_dir, "KolmogorovSmirnov-{}.tex".format(indicator_name)), "w") as latex:
+            latex.write(
+                __kolmogorov_smirnov_to_latex(
+                    indicator_name,
+                    test_data,
+                    caption=caption.format(indicator_name),
+                    label="table:{}".format(indicator_name),
+                )
+            )
    
+
+
+
+def __averages_to_latex(
+        central_tendency: pd.DataFrame,
+        dispersion: pd.DataFrame,
+        caption: str,
+        label: str,
+        minimization=True,
+        alignment: str = "c",
+):
+    """Convert a pandas DataFrame to a LaTeX tabular. Prints labels in bold and does use math mode.
 
     :param caption: LaTeX table caption.
     :param label: LaTeX table label.
@@ -520,40 +718,40 @@ 
    Source code for jmetal.lab.experiment
         num_columns, num_rows = central_tendency.shape[1], central_tendency.shape[0]
     output = io.StringIO()
 
-    col_format = '{}|{}'.format(alignment, alignment * num_columns)
-    column_labels = ['\\textbf{{{0}}}'.format(label.replace('_', '\\_')) for label in central_tendency.columns]
+    col_format = "{}|{}".format(alignment, alignment * num_columns)
+    column_labels = ["\\textbf{{{0}}}".format(label.replace("_", "\\_")) for label in central_tendency.columns]
 
     # Write header
-    output.write('\\documentclass{article}\n')
+    output.write("\\documentclass{article}\n")
 
-    output.write('\\usepackage[utf8]{inputenc}\n')
-    output.write('\\usepackage{tabularx}\n')
-    output.write('\\usepackage{colortbl}\n')
-    output.write('\\usepackage[table*]{xcolor}\n')
+    output.write("\\usepackage[utf8]{inputenc}\n")
+    output.write("\\usepackage{tabularx}\n")
+    output.write("\\usepackage{colortbl}\n")
+    output.write("\\usepackage[table*]{xcolor}\n")
 
-    output.write('\\xdefinecolor{gray95}{gray}{0.65}\n')
-    output.write('\\xdefinecolor{gray25}{gray}{0.8}\n')
+    output.write("\\xdefinecolor{gray95}{gray}{0.65}\n")
+    output.write("\\xdefinecolor{gray25}{gray}{0.8}\n")
 
-    output.write('\\title{Median and IQR}\n')
-    output.write('\\author{}\n')
+    output.write("\\title{Median and IQR}\n")
+    output.write("\\author{}\n")
 
-    output.write('\\begin{document}\n')
-    output.write('\\maketitle\n')
+    output.write("\\begin{document}\n")
+    output.write("\\maketitle\n")
 
-    output.write('\\section{Table}\n')
+    output.write("\\section{Table}\n")
 
-    output.write('\\begin{table}[!htp]\n')
-    output.write('  \\caption{{{}}}\n'.format(caption))
-    output.write('  \\label{{{}}}\n'.format(label))
-    output.write('  \\centering\n')
-    output.write('  \\begin{scriptsize}\n')
-    output.write('  \\begin{tabular}{%s}\n' % col_format)
-    output.write('      & {} \\\\\\hline\n'.format(' & '.join(column_labels)))
+    output.write("\\begin{table}[!htp]\n")
+    output.write("  \\caption{{{}}}\n".format(caption))
+    output.write("  \\label{{{}}}\n".format(label))
+    output.write("  \\centering\n")
+    output.write("  \\begin{scriptsize}\n")
+    output.write("  \\begin{tabular}{%s}\n" % col_format)
+    output.write("      & {} \\\\\\hline\n".format(" & ".join(column_labels)))
 
     # Write data lines
     for i in range(num_rows):
-        central_values = [v for v in central_tendency.ix[i]]
-        dispersion_values = [v for v in dispersion.ix[i]]
+        central_values = [v for v in central_tendency.iloc[i]]
+        dispersion_values = [v for v in dispersion.iloc[i]]
 
         # Sort mean/median values (the lower the better if minimization)
         # Note that mean/median values could be the same: in that case, sort by Std/IQR (the lower the better)
@@ -567,29 +765,32 @@ 
    Source code for jmetal.lab.experiment
                 second_best, best = sorted_values[-1][2], sorted_values[-2][2]
 
         # Compose cell
-        values = ['{:.2e}_{{{:.2e}}}'.format(central_values[i], dispersion_values[i]) for i in
-                  range(len(central_values))]
+        values = [
+            "{:.2e}_{{{:.2e}}}".format(central_values[i], dispersion_values[i]) for i in range(len(central_values))
+        ]
 
         # Highlight values
-        values[best] = '\\cellcolor{gray25} ' + values[best]
-        values[second_best] = '\\cellcolor{gray95} ' + values[second_best]
+        values[best] = "\\cellcolor{gray25} " + values[best]
+        values[second_best] = "\\cellcolor{gray95} " + values[second_best]
 
-        output.write('      \\textbf{{{0}}} & ${1}$ \\\\\n'.format(
-            central_tendency.index[i], ' $ & $ '.join([str(val) for val in values]))
+        output.write(
+            "      \\textbf{{{0}}} & ${1}$ \\\\\n".format(
+                central_tendency.index[i], " $ & $ ".join([str(val) for val in values])
+            )
         )
 
     # Write footer
-    output.write('  \\end{tabular}\n')
-    output.write('  \\end{scriptsize}\n')
-    output.write('\\end{table}\n')
+    output.write("  \\end{tabular}\n")
+    output.write("  \\end{scriptsize}\n")
+    output.write("\\end{table}\n")
 
-    output.write('\\end{document}')
+    output.write("\\end{document}")
 
     return output.getvalue()
 
 
-def __wilcoxon_to_latex(df: pd.DataFrame, caption: str, label: str, minimization=True, alignment: str = 'c'):
-    """ Convert a pandas DataFrame to a LaTeX tabular. Prints labels in bold and does use math mode.
+def __wilcoxon_to_latex(df: pd.DataFrame, caption: str, label: str, minimization=True, alignment: str = "c"):
+    """Convert a pandas DataFrame to a LaTeX tabular. Prints labels in bold and does use math mode.
 
     :param df: Pandas dataframe.
     :param caption: LaTeX table caption.
@@ -599,58 +800,242 @@ 
    Source code for jmetal.lab.experiment
         num_columns, num_rows = df.shape[1], df.shape[0]
     output = io.StringIO()
 
-    col_format = '{}|{}'.format(alignment, alignment * num_columns)
-    column_labels = ['\\textbf{{{0}}}'.format(label.replace('_', '\\_')) for label in df.columns]
+    col_format = "{}|{}".format(alignment, alignment * num_columns)
+    column_labels = ["\\textbf{{{0}}}".format(label.replace("_", "\\_")) for label in df.columns]
 
     # Write header
-    output.write('\\documentclass{article}\n')
+    output.write("\\documentclass{article}\n")
 
-    output.write('\\usepackage[utf8]{inputenc}\n')
-    output.write('\\usepackage{tabularx}\n')
-    output.write('\\usepackage{amssymb}\n')
-    output.write('\\usepackage{amsmath}\n')
+    output.write("\\usepackage[utf8]{inputenc}\n")
+    output.write("\\usepackage{tabularx}\n")
+    output.write("\\usepackage{amssymb}\n")
+    output.write("\\usepackage{amsmath}\n")
 
-    output.write('\\title{Wilcoxon - Mann-Whitney rank sum test}\n')
-    output.write('\\author{}\n')
+    output.write("\\title{Wilcoxon - Mann-Whitney rank sum test}\n")
+    output.write("\\author{}\n")
 
-    output.write('\\begin{document}\n')
-    output.write('\\maketitle\n')
+    output.write("\\begin{document}\n")
+    output.write("\\maketitle\n")
 
-    output.write('\\section{Table}\n')
+    output.write("\\section{Table}\n")
 
-    output.write('\\begin{table}[!htp]\n')
-    output.write('  \\caption{{{}}}\n'.format(caption))
-    output.write('  \\label{{{}}}\n'.format(label))
-    output.write('  \\centering\n')
-    output.write('  \\begin{scriptsize}\n')
-    output.write('  \\begin{tabular}{%s}\n' % col_format)
-    output.write('      & {} \\\\\\hline\n'.format(' & '.join(column_labels)))
+    output.write("\\begin{table}[!htp]\n")
+    output.write("  \\caption{{{}}}\n".format(caption))
+    output.write("  \\label{{{}}}\n".format(label))
+    output.write("  \\centering\n")
+    output.write("  \\begin{scriptsize}\n")
+    output.write("  \\begin{tabular}{%s}\n" % col_format)
+    output.write("      & {} \\\\\\hline\n".format(" & ".join(column_labels)))
 
-    symbolo = '\\triangledown\ '
-    symbolplus = '\\blacktriangle\ '
+    symbolo = "\\triangledown\ "
+    symbolplus = "\\blacktriangle\ "
 
     if not minimization:
         symbolo, symbolplus = symbolplus, symbolo
 
     # Write data lines
     for i in range(num_rows):
-        values = [val.replace('-', '\\text{--}\ ').replace('o', symbolo).replace('+', symbolplus) for val in df.ix[i]]
-        output.write('      \\textbf{{{0}}} & ${1}$ \\\\\n'.format(
-            df.index[i], ' $ & $ '.join([str(val) for val in values]))
+        values = [val.replace("-", "\\text{--}\ ").replace("o", symbolo).replace("+", symbolplus) for val in df.iloc[i]]
+        output.write(
+            "      \\textbf{{{0}}} & ${1}$ \\\\\n".format(df.index[i], " $ & $ ".join([str(val) for val in values]))
+        )
+
+    # Write footer
+    output.write("  \\end{tabular}\n")
+    output.write("  \\end{scriptsize}\n")
+    output.write("\\end{table}\n")
+
+    output.write("\\end{document}")
+
+    return output.getvalue()
+
+
+def __median_wilcoxon_to_latex(
+        indicator_name: str,
+        wilcoxon_data: pd.DataFrame,
+        caption: str,
+        label):
+    indicator_data = wilcoxon_data[wilcoxon_data["Indicator"] == indicator_name]
+
+    problems = pd.unique(indicator_data["Problem"])
+    algorithms = pd.unique(indicator_data["Algorithm"])
+
+    num_columns = len(algorithms)
+    columns = algorithms
+
+    alignment = "c"
+    col_format = "{}|{}".format(alignment, alignment * num_columns)
+    column_labels = ["\\textbf{{{0}}}".format(label.replace("_", "\\_")) for label in columns]
+
+    output = io.StringIO()
+
+    output.write("\\documentclass{article}\n")
+
+    output.write("\\usepackage[utf8]{inputenc}\n")
+    output.write("\\usepackage{tabularx}\n")
+    output.write("\\usepackage{colortbl}\n")
+    output.write("\\usepackage[table*]{xcolor}\n")
+
+    output.write("\\xdefinecolor{gray95}{gray}{0.65}\n")
+    output.write("\\xdefinecolor{gray25}{gray}{0.8}\n")
+
+    output.write("\\title{Median and Wilcoxon}\n")
+    output.write("\\author{}\n")
+
+    output.write("\\begin{document}\n")
+    output.write("\\maketitle\n")
+
+    output.write("\\section{Table}\n")
+
+    output.write("\\begin{table}[!htp]\n")
+    output.write("  \\caption{{{}}}\n".format(caption))
+    output.write("  \\label{{{}}}\n".format(label))
+    output.write("  \\centering\n")
+    output.write("  \\begin{tiny}\n")
+    output.write("  \\begin{tabular}{%s}\n" % col_format)
+    output.write("      & {} \\\\\\hline\n".format(" & ".join(column_labels)))
+
+    # Counts the number of times that an algorithm performs better, worse or equal than the reference algorithm
+    counters = {}
+    for algorithm in algorithms:
+        counters[algorithm] = [0, 0, 0]  # best, equal, worse
+
+    for problem in problems:
+        values = []
+
+        for algorithm in algorithms:
+            row = indicator_data[(indicator_data["Problem"] == problem) & (indicator_data["Algorithm"] == algorithm)]
+            value = "{:.2e}({:.2e})".format(row["Median"].tolist()[0], row["IQR"].tolist()[0])
+
+            # Include the symbol according to the Wilcoxon rank sum test with the reference algorithm
+            if algorithm != algorithms[-1]:
+                if row["TestResult"].tolist()[0] == "-":
+                    value = "{{{}-}}".format(value)
+                    counters[algorithm][2] = counters[algorithm][2] + 1
+                elif row["TestResult"].tolist()[0] == "+":
+                    value = "{{{}+}}".format(value)
+                    counters[algorithm][0] = counters[algorithm][0] + 1
+                else:
+                    value = "{{{}\\approx}}".format(value)
+                    counters[algorithm][1] = counters[algorithm][1] + 1
+            values.append(value)
+
+        # Find the best and second best values
+        medians = indicator_data[(indicator_data["Problem"] == problem)]["Median"]
+        iqrs = indicator_data[(indicator_data["Problem"] == problem)]["IQR"]
+        pairs = list(zip(medians, iqrs))
+        indexes = sorted(range(len(pairs)), key=lambda x: pairs[x])
+
+        if check_minimization(indicator_name):
+            best = indexes[0]
+            second_best = indexes[1]
+        else:
+            best = indexes[-1]
+            second_best = indexes[-2]
+
+        values[best] = "\\cellcolor{gray95} " + values[best]
+        values[second_best] = "\\cellcolor{gray25} " + values[second_best]
+
+        output.write(
+            "\\textbf{{{0}}} & ${1}$ \\\\\n".format(problem, " $ & $ ".join(
+                [str(val).replace("e-", "e\makebox[0.1cm]{-}").replace("e+", "e\makebox[0.1cm]{+}") for val in values])
+                                                    )
         )
 
+    # Select all but the last counter
+    counter_summary = []
+    for algorithm in algorithms[:-1]:
+        counter_summary.append(counters[algorithm])
+
+    output.write("  \\hline\n")
+    output.write(
+        "\\textbf{{{0}}} & ${1}$ \\\\\n".format("$+/\\approx/-$", " $ & $ ".join(
+            [str(val[0]) + "/" + str(val[1]) + "/" + str(val[2]) for val in counter_summary])))
+
+    # Write footer
+    output.write("  \\end{tabular}\n")
+    output.write("  \\end{tiny}\n")
+    output.write("\\end{table}\n")
+
+    output.write("\\end{document}")
+
+    return output.getvalue()
+
+
+def __kolmogorov_smirnov_to_latex(indicator_name: str, test_data: pd.DataFrame, caption: str, label: str):
+    indicator_data = test_data[test_data["Indicator"] == indicator_name]
+
+    problems = pd.unique(indicator_data["Problem"])
+    algorithms = pd.unique(indicator_data["Algorithm"])
+
+    num_columns = len(algorithms)
+    columns = algorithms
+
+    alignment = "c"
+    col_format = "{}|{}".format(alignment, alignment * num_columns)
+    column_labels = ["\\textbf{{{0}}}".format(label.replace("_", "\\_")) for label in columns]
+
+    output = io.StringIO()
+
+    output.write("\\documentclass{article}\n")
+
+    output.write("\\usepackage[utf8]{inputenc}\n")
+    output.write("\\usepackage{tabularx}\n")
+    output.write("\\usepackage{colortbl}\n")
+    output.write("\\usepackage[table*]{xcolor}\n")
+
+    output.write("\\xdefinecolor{gray95}{gray}{0.65}\n")
+    output.write("\\xdefinecolor{gray25}{gray}{0.8}\n")
+
+    output.write("\\title{Kolmogorov-Smirnov Test}\n")
+    output.write("\\author{}\n")
+
+    output.write("\\begin{document}\n")
+    output.write("\\maketitle\n")
+
+    output.write("\\section{Table}\n")
+
+    output.write("\\begin{table}[!htp]\n")
+    output.write("  \\caption{{{}}}\n".format(caption))
+    output.write("  \\label{{{}}}\n".format(label))
+    output.write("  \\centering\n")
+    output.write("  \\begin{tiny}\n")
+    output.write("  \\begin{tabular}{%s}\n" % col_format)
+    output.write("      & {} \\\\\\hline\n".format(" & ".join(column_labels)))
+
+    for problem in problems:
+        values = []
+
+        for algorithm in algorithms[:-1]:
+            row = indicator_data[(indicator_data["Problem"] == problem) & (indicator_data["Algorithm"] == algorithm)]
+            value = "{:.2e}".format(row["PValue"].tolist()[0])
+
+            if (row["PValue"].tolist()[0] < 0.05):
+                value = "\\cellcolor{gray25} " + value
+
+            values.append(value)
+        values.append("-")
+
+        output.write(
+            "\\textbf{{{0}}} & ${1}$ \\\\\n".format(problem, " $ & $ ".join(
+                [str(val).replace("e-", "e\makebox[0.1cm]{-}").replace("e+", "e\makebox[0.1cm]{+}") for val in values])
+                                                    )
+        )
+
+    output.write("  \\hline\n")
+
     # Write footer
-    output.write('  \\end{tabular}\n')
-    output.write('  \\end{scriptsize}\n')
-    output.write('\\end{table}\n')
+    output.write("  \\end{tabular}\n")
+    output.write("  \\end{tiny}\n")
+    output.write("\\end{table}\n")
 
-    output.write('\\end{document}')
+    output.write("\\end{document}")
 
     return output.getvalue()
 
 
 def check_minimization(indicator) -> bool:
-    if indicator == 'HV':
+    if indicator == "HV":
         return False
     else:
         return True
@@ -670,8 +1055,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.experiment
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.statistical_test.apv_procedures — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.statistical_test.apv_procedures
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -113,8 +110,10 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    import pandas as pd
 
 
-
    [docs]def bonferroni_dunn(p_values, control):
-    """
+
    
+[docs]
+def bonferroni_dunn(p_values, control):
+    """
     Bonferroni-Dunn's procedure for the adjusted p-value computation.
 
     Parameters:
@@ -132,14 +131,12 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    algorithms = p_values.columns
         p_values = p_values.values
     elif type(p_values) == np.ndarray:
-        algorithms = np.array(
-            ['Alg%d' % alg for alg in range(p_values.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(p_values.shape[1])])
 
     if type(control) == str:
         control = int(np.where(algorithms == control)[0])
     if control is None:
-        raise ValueError(
-            'Initialization ERROR. Incorrect value for control.')
+        raise ValueError("Initialization ERROR. Incorrect value for control.")
 
     k = p_values.shape[1]
 
@@ -149,14 +146,16 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    APVs = np.zeros((k - 1, 1))
     comparison = []
     for i in range(k - 1):
-        comparison.append(algorithms[control] +
-                          ' vs ' + algorithms[argsorted_pvals[i]])
+        comparison.append(algorithms[control] + " vs " + algorithms[argsorted_pvals[i]])
         APVs[i, 0] = np.min([(k - 1) * p_values[0, argsorted_pvals[i]], 1])
-    return pd.DataFrame(data=APVs, index=comparison, columns=['Bonferroni'])
    
+    return pd.DataFrame(data=APVs, index=comparison, columns=["Bonferroni"])
    
+
 
 
-
    [docs]def holland(p_values, control):
-    """
+
    
+[docs]
+def holland(p_values, control):
+    """
     Holland's procedure for the adjusted p-value computation.
 
     Parameters:
@@ -174,14 +173,12 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    algorithms = p_values.columns
         p_values = p_values.values
     elif type(p_values) == np.ndarray:
-        algorithms = np.array(
-            ['Alg%d' % alg for alg in range(p_values.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(p_values.shape[1])])
 
     if type(control) == str:
         control = int(np.where(algorithms == control)[0])
     if control is None:
-        raise ValueError(
-            'Initialization ERROR. Incorrect value for control.')
+        raise ValueError("Initialization ERROR. Incorrect value for control.")
 
     # --------------------------------------------------------------------------
     # ------------------------------- Procedure --------------------------------
@@ -194,16 +191,18 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    APVs = np.zeros((k - 1, 1))
     comparison = []
     for i in range(k - 1):
-        comparison.append(algorithms[control] +
-                          ' vs ' + algorithms[argsorted_pvals[i]])
+        comparison.append(algorithms[control] + " vs " + algorithms[argsorted_pvals[i]])
         aux = k - 1 - np.arange(i + 1)
-        v = np.max(1 - (1 - p_values[0, argsorted_pvals[:(i + 1)]]) ** aux)
+        v = np.max(1 - (1 - p_values[0, argsorted_pvals[: (i + 1)]]) ** aux)
         APVs[i, 0] = np.min([v, 1])
-    return pd.DataFrame(data=APVs, index=comparison, columns=['Holland'])
    
+    return pd.DataFrame(data=APVs, index=comparison, columns=["Holland"])
    
+
 
 
-
    [docs]def finner(p_values, control):
-    """
+
    
+[docs]
+def finner(p_values, control):
+    """
     Finner's procedure for the adjusted p-value computation.
 
     Parameters:
@@ -221,14 +220,12 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    algorithms = p_values.columns
         p_values = p_values.values
     elif type(p_values) == np.ndarray:
-        algorithms = np.array(
-            ['Alg%d' % alg for alg in range(p_values.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(p_values.shape[1])])
 
     if type(control) == str:
         control = int(np.where(algorithms == control)[0])
     if control is None:
-        raise ValueError(
-            'Initialization ERROR. Incorrect value for control.')
+        raise ValueError("Initialization ERROR. Incorrect value for control.")
 
     k = p_values.shape[1]
 
@@ -238,16 +235,18 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    APVs = np.zeros((k - 1, 1))
     comparison = []
     for i in range(k - 1):
-        comparison.append(algorithms[control] +
-                          ' vs ' + algorithms[argsorted_pvals[i]])
+        comparison.append(algorithms[control] + " vs " + algorithms[argsorted_pvals[i]])
         aux = float(k - 1) / (np.arange(i + 1) + 1)
-        v = np.max(1 - (1 - p_values[0, argsorted_pvals[:(i + 1)]]) ** aux)
+        v = np.max(1 - (1 - p_values[0, argsorted_pvals[: (i + 1)]]) ** aux)
         APVs[i, 0] = np.min([v, 1])
-    return pd.DataFrame(data=APVs, index=comparison, columns=['Finner'])
    
+    return pd.DataFrame(data=APVs, index=comparison, columns=["Finner"])
    
 
 
-
    [docs]def hochberg(p_values, control):
-    """
+
+
    
+[docs]
+def hochberg(p_values, control):
+    """
     Hochberg's procedure for the adjusted p-value computation.
 
     Parameters:
@@ -265,14 +264,12 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    algorithms = p_values.columns
         p_values = p_values.values
     elif type(p_values) == np.ndarray:
-        algorithms = np.array(
-            ['Alg%d' % alg for alg in range(p_values.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(p_values.shape[1])])
 
     if type(control) == str:
         control = int(np.where(algorithms == control)[0])
     if control is None:
-        raise ValueError(
-            'Initialization ERROR. Incorrect value for control.')
+        raise ValueError("Initialization ERROR. Incorrect value for control.")
 
     k = p_values.shape[1]
 
@@ -282,16 +279,18 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    APVs = np.zeros((k - 1, 1))
     comparison = []
     for i in range(k - 1):
-        comparison.append(algorithms[control] +
-                          ' vs ' + algorithms[argsorted_pvals[i]])
+        comparison.append(algorithms[control] + " vs " + algorithms[argsorted_pvals[i]])
         aux = np.arange(k, i, -1).astype(np.uint8)
         v = np.max(p_values[0, argsorted_pvals[aux - 1]] * (k - aux))
         APVs[i, 0] = np.min([v, 1])
-    return pd.DataFrame(data=APVs, index=comparison, columns=['Hochberg'])
    
+    return pd.DataFrame(data=APVs, index=comparison, columns=["Hochberg"])
    
+
 
 
-
    [docs]def li(p_values, control):
-    """
+
    
+[docs]
+def li(p_values, control):
+    """
     Li's procedure for the adjusted p-value computation.
 
     Parameters:
@@ -311,14 +310,12 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    algorithms = p_values.columns
         p_values = p_values.values
     elif type(p_values) == np.ndarray:
-        algorithms = np.array(
-            ['Alg%d' % alg for alg in range(p_values.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(p_values.shape[1])])
 
     if type(control) == str:
         control = int(np.where(algorithms == control)[0])
     if control is None:
-        raise ValueError(
-            'Initialization ERROR. Incorrect value for control.')
+        raise ValueError("Initialization ERROR. Incorrect value for control.")
 
     k = p_values.shape[1]
 
@@ -328,15 +325,22 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    APVs = np.zeros((k - 1, 1))
     comparison = []
     for i in range(k - 1):
-        comparison.append(algorithms[control] +
-                          ' vs ' + algorithms[argsorted_pvals[i]])
-        APVs[i, 0] = np.min([p_values[0, argsorted_pvals[-2]], p_values[0, argsorted_pvals[i]] / (
-                p_values[0, argsorted_pvals[i]] + 1 - p_values[0, argsorted_pvals[-2]])])
-    return pd.DataFrame(data=APVs, index=comparison, columns=['Li'])
    
-
-
-
    [docs]def holm(p_values, control=None):
-    """
+        comparison.append(algorithms[control] + " vs " + algorithms[argsorted_pvals[i]])
+        APVs[i, 0] = np.min(
+            [
+                p_values[0, argsorted_pvals[-2]],
+                p_values[0, argsorted_pvals[i]]
+                / (p_values[0, argsorted_pvals[i]] + 1 - p_values[0, argsorted_pvals[-2]]),
+            ]
+        )
+    return pd.DataFrame(data=APVs, index=comparison, columns=["Li"])
    
+
+
+
+
    
+[docs]
+def holm(p_values, control=None):
+    """
     Holm's procedure for the adjusted p-value computation.
 
     Parameters:
@@ -356,8 +360,7 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    algorithms = p_values.columns
         p_values = p_values.values
     elif type(p_values) == np.ndarray:
-        algorithms = np.array(
-            ['Alg%d' % alg for alg in range(p_values.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(p_values.shape[1])])
 
     if type(control) == str:
         control = int(np.where(algorithms == control)[0])
@@ -372,9 +375,8 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    comparison = []
         for i in range(k - 1):
             aux = k - 1 - np.arange(i + 1)
-            comparison.append(
-                algorithms[control] + ' vs ' + algorithms[argsorted_pvals[i]])
-            v = np.max(aux * p_values[0, argsorted_pvals[:(i + 1)]])
+            comparison.append(algorithms[control] + " vs " + algorithms[argsorted_pvals[i]])
+            v = np.max(aux * p_values[0, argsorted_pvals[: (i + 1)]])
             APVs[i, 0] = np.min([v, 1])
 
     elif control is None:
@@ -392,14 +394,17 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    for i in range(m):
             row = pairs_index[0][pairs_sorted[i]]
             col = pairs_index[1][pairs_sorted[i]]
-            comparison.append(algorithms[row] + ' vs ' + algorithms[col])
-            v = np.max(aux[:i + 1])
+            comparison.append(algorithms[row] + " vs " + algorithms[col])
+            v = np.max(aux[: i + 1])
             APVs[i, 0] = np.min([v, 1])
-    return pd.DataFrame(data=APVs, index=comparison, columns=['Holm'])
    
+    return pd.DataFrame(data=APVs, index=comparison, columns=["Holm"])
    
+
 
 
-
    [docs]def shaffer(p_values):
-    """
+
    
+[docs]
+def shaffer(p_values):
+    """
     Shaffer's procedure for adjusted p_value ccmputation.
 
     Parameters:
@@ -412,7 +417,7 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
        """
 
     def S(k):
-        """
+        """
         Computes the set of possible numbers of true hoypotheses.
 
         Parameters:
@@ -431,8 +436,7 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    TrueHset = [0]
         if k > 1:
             for j in np.arange(k, 0, -1, dtype=int):
-                TrueHset = list(set(TrueHset) | set(
-                    [binomial(j, 2) + x for x in S(k - j)]))
+                TrueHset = list(set(TrueHset) | set([binomial(j, 2) + x for x in S(k - j)]))
         return TrueHset
 
     # Initial Checking
@@ -440,15 +444,12 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    algorithms = p_values.columns
         p_values = p_values.values
     elif type(p_values) == np.ndarray:
-        algorithms = np.array(
-            ['Alg%d' % alg for alg in range(p_values.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(p_values.shape[1])])
 
     if p_values.ndim != 2:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions.')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions.")
     elif p_values.shape[0] != p_values.shape[1]:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions.')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions.")
 
     # define parameters
     k = p_values.shape[0]
@@ -467,19 +468,22 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    # Adjust p-values
     APVs = np.zeros((m, 1))
-    aux = (pairs_pvals[pairs_sorted] * t)
+    aux = pairs_pvals[pairs_sorted] * t
     comparison = []
     for i in range(m):
         row = pairs_index[0][pairs_sorted[i]]
         col = pairs_index[1][pairs_sorted[i]]
-        comparison.append(algorithms[row] + ' vs ' + algorithms[col])
-        v = np.max(aux[:i + 1])
+        comparison.append(algorithms[row] + " vs " + algorithms[col])
+        v = np.max(aux[: i + 1])
         APVs[i, 0] = np.min([v, 1])
-    return pd.DataFrame(data=APVs, index=comparison, columns=['Shaffer'])
    
+    return pd.DataFrame(data=APVs, index=comparison, columns=["Shaffer"])
    
 
 
-
    [docs]def nemenyi(p_values):
-    """
+
+
    
+[docs]
+def nemenyi(p_values):
+    """
     Nemenyi's procedure for adjusted p_value computation.
 
     Parameters:
@@ -496,15 +500,12 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    algorithms = p_values.columns
         p_values = p_values.values
     elif type(p_values) == np.ndarray:
-        algorithms = np.array(
-            ['Alg%d' % alg for alg in range(p_values.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(p_values.shape[1])])
 
     if p_values.ndim != 2:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions.')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions.")
     elif p_values.shape[0] != p_values.shape[1]:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions.')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions.")
 
     # define parameters
     k = p_values.shape[0]
@@ -521,9 +522,10 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
    for i in range(m):
         row = pairs_index[0][pairs_sorted[i]]
         col = pairs_index[1][pairs_sorted[i]]
-        comparison.append(algorithms[row] + ' vs ' + algorithms[col])
+        comparison.append(algorithms[row] + " vs " + algorithms[col])
         APVs[i, 0] = np.min([pairs_pvals[pairs_sorted[i]] * m, 1])
-    return pd.DataFrame(data=APVs, index=comparison, columns=['Nemenyi'])
    
+    return pd.DataFrame(data=APVs, index=comparison, columns=["Nemenyi"])
    
+
 
    
 
           
    
@@ -540,8 +542,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.statistical_test.apv_procedures
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.statistical_test.bayesian — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.statistical_test.bayesian
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -113,9 +110,12 @@ 
    Source code for jmetal.lab.statistical_test.bayesian
    import pandas as pd
 
 
-
    [docs]def bayesian_sign_test(data, rope_limits=[-0.01, 0.01], prior_strength=0.5, prior_place='rope', sample_size=50000,
-                       return_sample=False):
-    """ Bayesian version of the sign test.
+
    
+[docs]
+def bayesian_sign_test(
+    data, rope_limits=[-0.01, 0.01], prior_strength=0.5, prior_place="rope", sample_size=50000, return_sample=False
+):
+    """Bayesian version of the sign test.
 
     :param data: An (n x 2) array or DataFrame contaning the results. In data, each column represents an algorithm and, and each row a problem.
     :param rope_limits: array_like. Default [-0.01, 0.01]. Limits of the practical equivalence.
@@ -138,16 +138,13 @@ 
    Source code for jmetal.lab.statistical_test.bayesian
    sample1, sample2 = data[:, 0], data[:, 1]
         n = data.shape[0]
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of dimensions for axis 1')
+        raise ValueError("Initialization ERROR. Incorrect number of dimensions for axis 1")
 
     if prior_strength <= 0:
-        raise ValueError(
-            'Initialization ERROR. prior_strength mustb be a positive float')
+        raise ValueError("Initialization ERROR. prior_strength mustb be a positive float")
 
-    if prior_place not in ['left', 'rope', 'right']:
-        raise ValueError(
-            'Initialization ERROR. Incorrect value fro prior_place')
+    if prior_place not in ["left", "rope", "right"]:
+        raise ValueError("Initialization ERROR. Incorrect value fro prior_place")
 
     # Compute the differences
     Z = sample1 - sample2
@@ -164,7 +161,7 @@ 
    Source code for jmetal.lab.statistical_test.bayesian
    # Parameters of the Dirichlet distribution
     alpha = np.array([Nleft, Nequiv, Nright], dtype=float) + 1e-6
-    alpha[['left', 'rope', 'right'].index(prior_place)] += prior_strength
+    alpha[["left", "rope", "right"].index(prior_place)] += prior_strength
     # Simulate dirichlet process
     Dprocess = np.random.dirichlet(alpha, sample_size)
 
@@ -180,10 +177,13 @@ 
    Source code for jmetal.lab.statistical_test.bayesian
    return np.array([win_left, win_rope, win_rifht]) / float(sample_size)
    
 
 
-
    [docs]def bayesian_signed_rank_test(data, rope_limits=[-0.01, 0.01], prior_strength=1.0, prior_place='rope',
-                              sample_size=10000,
-                              return_sample=False):
-    """ Bayesian version of the signed rank test.
+
+
    
+[docs]
+def bayesian_signed_rank_test(
+    data, rope_limits=[-0.01, 0.01], prior_strength=1.0, prior_place="rope", sample_size=10000, return_sample=False
+):
+    """Bayesian version of the signed rank test.
 
     :param data: An (n x 2) array or DataFrame contaning the results. In data, each column represents an algorithm and, and each row a problem.
     :param rope_limits: array_like. Default [-0.01, 0.01]. Limits of the practical equivalence.
@@ -209,21 +209,17 @@ 
    Source code for jmetal.lab.statistical_test.bayesian
    sample1, sample2 = data[:, 0], data[:, 1]
         n = data.shape[0]
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of dimensions for axis 1')
+        raise ValueError("Initialization ERROR. Incorrect number of dimensions for axis 1")
 
     if prior_strength <= 0:
-        raise ValueError(
-            'Initialization ERROR. prior_strength must be a positive float')
+        raise ValueError("Initialization ERROR. prior_strength must be a positive float")
 
-    if prior_place not in ['left', 'rope', 'right']:
-        raise ValueError(
-            'Initialization ERROR. Incorrect value for prior_place')
+    if prior_place not in ["left", "rope", "right"]:
+        raise ValueError("Initialization ERROR. Incorrect value for prior_place")
 
     # Compute the differences
     Z = sample1 - sample2
-    Z0 = [-float('Inf'), 0.0, float('Inf')][['left',
-                                             'rope', 'right'].index(prior_place)]
+    Z0 = [-float("Inf"), 0.0, float("Inf")][["left", "rope", "right"].index(prior_place)]
     Z = np.concatenate(([Z0], Z), axis=None)
 
     # compute the the probabilities that the mean difference of accuracy is in
@@ -253,6 +249,7 @@ 
    Source code for jmetal.lab.statistical_test.bayesian
    return np.array([win_left, win_rope, win_rifht]) / float(sample_size), Dprocess
     else:
         return np.array([win_left, win_rope, win_rifht]) / float(sample_size)
    
+
 
    
 
           
    
@@ -269,8 +266,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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+        
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+          
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+        
  • jmetal.lab.statistical_test.bayesian
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.statistical_test.critical_distance — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
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  • jMetalPy 1.5.3 documentation »
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+          
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  • jmetal.lab.statistical_test.critical_distance
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -117,8 +114,10 @@ 
    Source code for jmetal.lab.statistical_test.critical_distance
    from jmetal.lab.statistical_test.functions import ranks
 
 
-
    [docs]def NemenyiCD(alpha: float, num_alg, num_dataset):
-    """ Computes Nemenyi's critical difference:
+
    
+[docs]
+def NemenyiCD(alpha: float, num_alg, num_dataset):
+    """Computes Nemenyi's critical difference:
     * CD = q_alpha * sqrt(num_alg*(num_alg + 1)/(6*num_prob))
     where q_alpha is the critical value, of the Studentized range statistic divided by sqrt(2).
     :param alpha: {0.1, 0.999}. Significance level.
@@ -135,8 +134,17 @@ 
    Source code for jmetal.lab.statistical_test.critical_distance
    return cd
    
 
 
-
    [docs]def CDplot(results, alpha: float = 0.05, higher_is_better: bool=False, alg_names: list = None, output_filename: str = 'cdplot.eps'):
-    """ CDgraph plots the critical difference graph show in Janez Demsar's 2006 work:
+
+
    
+[docs]
+def CDplot(
+    results,
+    alpha: float = 0.05,
+    higher_is_better: bool = False,
+    alg_names: list = None,
+    output_filename: str = "cdplot.eps",
+):
+    """CDgraph plots the critical difference graph show in Janez Demsar's 2006 work:
     * Statistical Comparisons of Classifiers over Multiple Data Sets.
     :param results: A 2-D array containing results from each algorithm. Each row of 'results' represents an algorithm, and each column a dataset.
     :param alpha: {0.1, 0.999}. Significance level for the critical difference.
@@ -144,15 +152,14 @@ 
    Source code for jmetal.lab.statistical_test.critical_distance
        """
 
     def _join_alg(avranks, num_alg, cd):
-        """
+        """
         join_alg returns the set of non significant methods
         """
 
         # get all pairs
         sets = (-1) * np.ones((num_alg, 2))
         for i in range(num_alg):
-            elements = np.where(np.logical_and(
-                avranks - avranks[i] > 0, avranks - avranks[i] < cd))[0]
+            elements = np.where(np.logical_and(avranks - avranks[i] > 0, avranks - avranks[i] < cd))[0]
             if elements.size > 0:
                 sets[i, :] = [avranks[i], avranks[elements[-1]]]
         sets = np.delete(sets, np.where(sets[:, 0] < 0)[0], axis=0)
@@ -170,14 +177,12 @@ 
    Source code for jmetal.lab.statistical_test.critical_distance
    alg_names = results.index
         results = results.values
     elif type(results) == np.ndarray and alg_names is None:
-        alg_names = np.array(
-            ['Alg%d' % alg for alg in range(results.shape[1])])
+        alg_names = np.array(["Alg%d" % alg for alg in range(results.shape[1])])
 
     if results.ndim == 2:
         num_alg, num_dataset = results.shape
     else:
-        raise ValueError(
-            'Initialization ERROR: In CDplot(...) results must be 2-D array')
+        raise ValueError("Initialization ERROR: In CDplot(...) results must be 2-D array")
 
     # Get the critical difference
     cd = NemenyiCD(alpha, num_alg, num_dataset)
@@ -201,9 +206,9 @@ 
    Source code for jmetal.lab.statistical_test.critical_distance
    highest = np.ceil(np.max(avranks)).astype(np.uint8)  # highest shown rank
     lowest = np.floor(np.min(avranks)).astype(np.uint8)  # lowest shown rank
     width = 6  # default figure width (in inches)
-    height = (0.575 * (rows + 1))  # figure height
+    height = 0.575 * (rows + 1)  # figure height
 
-    """
+    """
                         FIGURE
       (1,0)
         +-----+---------------------------+-------+
@@ -229,60 +234,79 @@ 
    Source code for jmetal.lab.statistical_test.critical_distance
    lline = sright - sleft
 
     # Initialize figure
-    fig = plt.figure(figsize=(width, height), facecolor='white')
+    fig = plt.figure(figsize=(width, height), facecolor="white")
     ax = fig.add_axes([0, 0, 1, 1])
     ax.set_xlim(0, 1)
     ax.set_ylim(0, 1)
     ax.set_axis_off()
 
     # Main horizontal axis
-    ax.hlines(stop, sleft, sright, color='black', linewidth=0.7)
+    ax.hlines(stop, sleft, sright, color="black", linewidth=0.7)
     for xi in range(highest - lowest + 1):
         # Plot mayor ticks
-        ax.vlines(x=sleft + (lline * xi) / (highest - lowest),
-                  ymin=stop, ymax=stop + 0.05, color='black', linewidth=0.7)
+        ax.vlines(
+            x=sleft + (lline * xi) / (highest - lowest), ymin=stop, ymax=stop + 0.05, color="black", linewidth=0.7
+        )
         # Mayor ticks labels
-        ax.text(x=sleft + (lline * xi) / (highest - lowest),
-                y=stop + 0.06,
-                s=str(lowest + xi), ha='center', va='bottom')
+        ax.text(
+            x=sleft + (lline * xi) / (highest - lowest), y=stop + 0.06, s=str(lowest + xi), ha="center", va="bottom"
+        )
         # Minor ticks
         if xi < highest - lowest:
-            ax.vlines(x=sleft + (lline * (xi + 0.5)) / (highest - lowest),
-                      ymin=stop, ymax=stop + 0.025, color='black', linewidth=0.7)
+            ax.vlines(
+                x=sleft + (lline * (xi + 0.5)) / (highest - lowest),
+                ymin=stop,
+                ymax=stop + 0.025,
+                color="black",
+                linewidth=0.7,
+            )
 
     # Plot lines/names for left models
     vspace = 0.5 * (stop - sbottom) / (spoint + 1)
     for i in range(spoint):
-        ax.vlines(x=sleft + (lline * (leftalg[i] - lowest)) / (highest - lowest),
-                  ymin=sbottom + (spoint - 1 - i) * vspace, ymax=stop, color='black', linewidth=0.7)
-        ax.hlines(y=sbottom + (spoint - 1 - i) * vspace, xmin=sleft,
-                  xmax=sleft +
-                       (lline * (leftalg[i] - lowest)) / (highest - lowest),
-                  color='black', linewidth=0.7)
-        ax.text(x=sleft - 0.01, y=sbottom + (spoint - 1 - i) * vspace,
-                s=alg_names[indices][i], ha='right', va='center')
+        ax.vlines(
+            x=sleft + (lline * (leftalg[i] - lowest)) / (highest - lowest),
+            ymin=sbottom + (spoint - 1 - i) * vspace,
+            ymax=stop,
+            color="black",
+            linewidth=0.7,
+        )
+        ax.hlines(
+            y=sbottom + (spoint - 1 - i) * vspace,
+            xmin=sleft,
+            xmax=sleft + (lline * (leftalg[i] - lowest)) / (highest - lowest),
+            color="black",
+            linewidth=0.7,
+        )
+        ax.text(x=sleft - 0.01, y=sbottom + (spoint - 1 - i) * vspace, s=alg_names[indices][i], ha="right", va="center")
 
     # Plot lines/names for right models
     vspace = 0.5 * (stop - sbottom) / (num_alg - spoint + 1)
     for i in range(num_alg - spoint):
-        ax.vlines(x=sleft + (lline * (rightalg[i] - lowest)) / (highest - lowest),
-                  ymin=sbottom + i * vspace, ymax=stop, color='black', linewidth=0.7)
-        ax.hlines(y=sbottom + i * vspace,
-                  xmin=sleft +
-                       (lline * (rightalg[i] - lowest)) / (highest - lowest),
-                  xmax=sright, color='black', linewidth=0.7)
-        ax.text(x=sright + 0.01, y=sbottom + i * vspace,
-                s=alg_names[indices][spoint + i], ha='left', va='center')
+        ax.vlines(
+            x=sleft + (lline * (rightalg[i] - lowest)) / (highest - lowest),
+            ymin=sbottom + i * vspace,
+            ymax=stop,
+            color="black",
+            linewidth=0.7,
+        )
+        ax.hlines(
+            y=sbottom + i * vspace,
+            xmin=sleft + (lline * (rightalg[i] - lowest)) / (highest - lowest),
+            xmax=sright,
+            color="black",
+            linewidth=0.7,
+        )
+        ax.text(x=sright + 0.01, y=sbottom + i * vspace, s=alg_names[indices][spoint + i], ha="left", va="center")
 
     # Plot critical difference rule
     if sleft + (cd * lline) / (highest - lowest) <= sright:
-        ax.hlines(y=stop + 0.2, xmin=sleft, xmax=sleft +
-                                                 (cd * lline) / (highest - lowest), linewidth=1.5)
-        ax.text(x=sleft + 0.5 * (cd * lline) /
-                  (highest - lowest), y=stop + 0.21, s='CD=%.3f' % cd, ha='center', va='bottom')
+        ax.hlines(y=stop + 0.2, xmin=sleft, xmax=sleft + (cd * lline) / (highest - lowest), linewidth=1.5)
+        ax.text(
+            x=sleft + 0.5 * (cd * lline) / (highest - lowest), y=stop + 0.21, s="CD=%.3f" % cd, ha="center", va="bottom"
+        )
     else:
-        ax.text(x=(sleft + sright) / 2, y=stop + 0.2, s='CD=%.3f' %
-                                                        cd, ha='center', va='bottom')
+        ax.text(x=(sleft + sright) / 2, y=stop + 0.2, s="CD=%.3f" % cd, ha="center", va="bottom")
 
     # Get pair of non-significant methods
     nonsig = _join_alg(avranks, num_alg, cd)
@@ -291,32 +315,35 @@ 
    Source code for jmetal.lab.statistical_test.critical_distance
    left_lines = np.reshape(nonsig[0, :], (1, 2))
             right_lines = np.reshape(nonsig[1, :], (1, 2))
         else:
-            left_lines = nonsig[:np.round(
-                nonsig.shape[0] / 2.0).astype(np.uint8), :]
-            right_lines = nonsig[np.round(
-                nonsig.shape[0] / 2.0).astype(np.uint8):, :]
+            left_lines = nonsig[: np.round(nonsig.shape[0] / 2.0).astype(np.uint8), :]
+            right_lines = nonsig[np.round(nonsig.shape[0] / 2.0).astype(np.uint8) :, :]
     else:
         left_lines = np.reshape(nonsig, (1, nonsig.shape[0]))
 
     # plot from the left
     vspace = 0.5 * (stop - sbottom) / (left_lines.shape[0] + 1)
     for i in range(left_lines.shape[0]):
-        ax.hlines(y=stop - (i + 1) * vspace,
-                  xmin=sleft + lline * (left_lines[i, 0] -
-                                        lowest - 0.025) / (highest - lowest),
-                  xmax=sleft + lline * (left_lines[i, 1] - lowest + 0.025) / (highest - lowest), linewidth=2)
+        ax.hlines(
+            y=stop - (i + 1) * vspace,
+            xmin=sleft + lline * (left_lines[i, 0] - lowest - 0.025) / (highest - lowest),
+            xmax=sleft + lline * (left_lines[i, 1] - lowest + 0.025) / (highest - lowest),
+            linewidth=2,
+        )
 
     # plot from the rigth
     if nonsig.ndim == 2:
         vspace = 0.5 * (stop - sbottom) / (left_lines.shape[0])
         for i in range(right_lines.shape[0]):
-            ax.hlines(y=stop - (i + 1) * vspace,
-                      xmin=sleft + lline * (right_lines[i, 0] -
-                                            lowest - 0.025) / (highest - lowest),
-                      xmax=sleft + lline * (right_lines[i, 1] - lowest + 0.025) / (highest - lowest), linewidth=2)
-
-    plt.savefig(output_filename, bbox_inches='tight')
+            ax.hlines(
+                y=stop - (i + 1) * vspace,
+                xmin=sleft + lline * (right_lines[i, 0] - lowest - 0.025) / (highest - lowest),
+                xmax=sleft + lline * (right_lines[i, 1] - lowest + 0.025) / (highest - lowest),
+                linewidth=2,
+            )
+
+    plt.savefig(output_filename, bbox_inches="tight")
     plt.show()
    
+
 
    
 
           
    
@@ -333,8 +360,9 @@ 
    Navigation
    
         
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           modules |
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  • jMetalPy 1.5.3 documentation »
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+        
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+          
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+        
  • jmetal.lab.statistical_test.critical_distance
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.statistical_test.functions — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
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  • jMetalPy 1.5.3 documentation »
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    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -109,13 +106,15 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.lab.statistical_test.functions
    -from scipy.stats import chi2, f, binom, norm
+from scipy.stats import binom, chi2, f, norm
 
 from jmetal.lab.statistical_test.apv_procedures import *
 
 
-
    [docs]def ranks(data: np.array, descending=False):
-    """ Computes the rank of the elements in data.
+
    
+[docs]
+def ranks(data: np.array, descending=False):
+    """Computes the rank of the elements in data.
 
     :param data: 2-D matrix
     :param descending: boolean (default False). If true, rank is sorted in descending order.
@@ -128,23 +127,30 @@ 
    Source code for jmetal.lab.statistical_test.functions
    ranks = np.ones(data.shape)
         for i in range(data.shape[0]):
             values, indices, rep = np.unique(
-                (-1) ** s * np.sort((-1) ** s * data[i, :]), return_index=True, return_counts=True, )
+                (-1) ** s * np.sort((-1) ** s * data[i, :]),
+                return_index=True,
+                return_counts=True,
+            )
             for j in range(data.shape[1]):
-                ranks[i, j] += indices[values == data[i, j]] + \
-                               0.5 * (rep[values == data[i, j]] - 1)
+                ranks[i, j] += indices[values == data[i, j]] + 0.5 * (rep[values == data[i, j]] - 1)
         return ranks
     elif data.ndim == 1:
         ranks = np.ones((data.size,))
         values, indices, rep = np.unique(
-            (-1) ** s * np.sort((-1) ** s * data), return_index=True, return_counts=True, )
+            (-1) ** s * np.sort((-1) ** s * data),
+            return_index=True,
+            return_counts=True,
+        )
         for i in range(data.size):
-            ranks[i] += indices[values == data[i]] + \
-                        0.5 * (rep[values == data[i]] - 1)
+            ranks[i] += indices[values == data[i]] + 0.5 * (rep[values == data[i]] - 1)
         return ranks
    
 
 
-
    [docs]def sign_test(data):
-    """ Given the results drawn from two algorithms/methods X and Y, the sign test analyses if
+
+
    
+[docs]
+def sign_test(data):
+    """Given the results drawn from two algorithms/methods X and Y, the sign test analyses if
     there is a difference between X and Y.
 
     .. note:: Null Hypothesis: Pr(X<Y)= 0.5
@@ -161,8 +167,7 @@ 
    Source code for jmetal.lab.statistical_test.functions
    X, Y = data[:, 0], data[:, 1]
         n_perf = data.shape[0]
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of dimensions for axis 1')
+        raise ValueError("Initialization ERROR. Incorrect number of dimensions for axis 1")
 
     # Compute the differences
     Z = X - Y
@@ -178,12 +183,16 @@ 
    Source code for jmetal.lab.statistical_test.functions
    p_value = 2 * min([p_value_minus, p_value_plus])
 
-    return pd.DataFrame(data=np.array([Wminus, Wplus, p_value]), index=['Num X<Y', 'Num X>Y', 'p-value'],
-                        columns=['Results'])
    
+    return pd.DataFrame(
+        data=np.array([Wminus, Wplus, p_value]), index=["Num X<Y", "Num X>Y", "p-value"], columns=["Results"]
+    )
    
+
 
 
-
    [docs]def friedman_test(data):
-    """ Friedman ranking test.
+
    
+[docs]
+def friedman_test(data):
+    """Friedman ranking test.
 
     ..note:: Null Hypothesis: In a set of k (>=2) treaments (or tested algorithms), all the treatments are equivalent, so their average ranks should be equal.
 
@@ -199,11 +208,9 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if data.ndim == 2:
         n_samples, k = data.shape
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions")
     if k < 2:
-        raise ValueError(
-            'Initialization Error. Incorrect number of dimensions for axis 1.')
+        raise ValueError("Initialization Error. Incorrect number of dimensions for axis 1.")
 
     # Compute ranks.
     datarank = ranks(data)
@@ -212,18 +219,21 @@ 
    Source code for jmetal.lab.statistical_test.functions
    avranks = np.mean(datarank, axis=0)
 
     # Get Friedman statistics
-    friedman_stat = (12.0 * n_samples) / (k * (k + 1.0)) * \
-                    (np.sum(avranks ** 2) - (k * (k + 1) ** 2) / 4.0)
+    friedman_stat = (12.0 * n_samples) / (k * (k + 1.0)) * (np.sum(avranks**2) - (k * (k + 1) ** 2) / 4.0)
 
     # Compute p-value
-    p_value = (1.0 - chi2.cdf(friedman_stat, df=(k - 1)))
+    p_value = 1.0 - chi2.cdf(friedman_stat, df=(k - 1))
 
-    return pd.DataFrame(data=np.array([friedman_stat, p_value]), index=['Friedman-statistic', 'p-value'],
-                        columns=['Results'])
    
+    return pd.DataFrame(
+        data=np.array([friedman_stat, p_value]), index=["Friedman-statistic", "p-value"], columns=["Results"]
+    )
    
 
 
-
    [docs]def friedman_aligned_rank_test(data):
-    """ Method of aligned ranks for the Friedman test.
+
+
    
+[docs]
+def friedman_aligned_rank_test(data):
+    """Method of aligned ranks for the Friedman test.
 
     ..note:: Null Hypothesis: In a set of k (>=2) treaments (or tested algorithms), all the treatments are equivalent, so their average ranks should be equal.
 
@@ -239,11 +249,9 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if data.ndim == 2:
         n_samples, k = data.shape
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions")
     if k < 2:
-        raise ValueError(
-            'Initialization Error. Incorrect number of dimensions for axis 1.')
+        raise ValueError("Initialization Error. Incorrect number of dimensions for axis 1.")
 
     # Compute the average value achieved by all algorithms in each problem
     control = np.mean(data, axis=1)
@@ -251,27 +259,31 @@ 
    Source code for jmetal.lab.statistical_test.functions
    diff = [data[:, j] - control for j in range(data.shape[1])]
     # rank diff
     alignedRanks = ranks(np.ravel(diff))
-    alignedRanks = np.reshape(alignedRanks, newshape=(n_samples, k), order='F')
+    alignedRanks = np.reshape(alignedRanks, newshape=(n_samples, k), order="F")
 
     # Compute statistic
     Rhat_i = np.sum(alignedRanks, axis=1)
     Rhat_j = np.sum(alignedRanks, axis=0)
-    si, sj = np.sum(Rhat_i ** 2), np.sum(Rhat_j ** 2)
+    si, sj = np.sum(Rhat_i**2), np.sum(Rhat_j**2)
 
-    A = sj - (k * n_samples ** 2 / 4.0) * (k * n_samples + 1) ** 2
-    B1 = (k * n_samples * (k * n_samples + 1) * (2 * k * n_samples + 1) / 6.0)
+    A = sj - (k * n_samples**2 / 4.0) * (k * n_samples + 1) ** 2
+    B1 = k * n_samples * (k * n_samples + 1) * (2 * k * n_samples + 1) / 6.0
     B2 = si / float(k)
 
     alignedRanks_stat = ((k - 1) * A) / (B1 - B2)
 
     p_value = 1 - chi2.cdf(alignedRanks_stat, df=k - 1)
 
-    return pd.DataFrame(data=np.array([alignedRanks_stat, p_value]), index=['Aligned Rank stat', 'p-value'],
-                        columns=['Results'])
    
+    return pd.DataFrame(
+        data=np.array([alignedRanks_stat, p_value]), index=["Aligned Rank stat", "p-value"], columns=["Results"]
+    )
    
+
 
 
-
    [docs]def quade_test(data):
-    """ Quade test.
+
    
+[docs]
+def quade_test(data):
+    """Quade test.
 
     ..note:: Null Hypothesis: In a set of k (>=2) treaments (or tested algorithms), all the treatments are equivalent, so their average ranks should be equal.
 
@@ -287,11 +299,9 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if data.ndim == 2:
         n_samples, k = data.shape
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions")
     if k < 2:
-        raise ValueError(
-            'Initialization Error. Incorrect number of dimensions for axis 1.')
+        raise ValueError("Initialization Error. Incorrect number of dimensions for axis 1.")
 
     # Compute ranks.
     datarank = ranks(data)
@@ -308,8 +318,8 @@ 
    Source code for jmetal.lab.statistical_test.functions
    Salg = np.sum(S_stat, axis=0)
 
     # Compute Fq (Quade Test statistic) and associated p_value
-    A = np.sum(S_stat ** 2)
-    B = np.sum(Salg ** 2) / float(n_samples)
+    A = np.sum(S_stat**2)
+    B = np.sum(Salg**2) / float(n_samples)
 
     if A == B:
         Fq = np.Inf
@@ -318,11 +328,14 @@ 
    Source code for jmetal.lab.statistical_test.functions
    Fq = (n_samples - 1.0) * B / (A - B)
         p_value = 1 - f.cdf(Fq, k - 1, (k - 1) * (n_samples - 1))
 
-    return pd.DataFrame(data=np.array([Fq, p_value]), index=['Quade Test statistic', 'p-value'], columns=['Results'])
    
+    return pd.DataFrame(data=np.array([Fq, p_value]), index=["Quade Test statistic", "p-value"], columns=["Results"])
    
 
 
-
    [docs]def friedman_ph_test(data, control=None, apv_procedure=None):
-    """ Friedman post-hoc test.
+
+
    
+[docs]
+def friedman_ph_test(data, control=None, apv_procedure=None):
+    """Friedman post-hoc test.
 
     :param data: An (n x 2) array or DataFrame contaning the results. In data, each column represents an algorithm and, and each row a problem.
     :param control: optional int or string. Default None. Index or Name of the control algorithm. If control = None all FriedmanPosHocTest considers all possible comparisons among algorithms.
@@ -344,7 +357,7 @@ 
    Source code for jmetal.lab.statistical_test.functions
    algorithms = data.columns
         data = data.values
     elif type(data) == np.ndarray:
-        algorithms = np.array(['Alg%d' % alg for alg in range(data.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(data.shape[1])])
 
     if control is None:
         index = algorithms
@@ -356,24 +369,29 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if data.ndim == 2:
         n_samples, k = data.shape
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions.')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions.")
     if k < 2:
-        raise ValueError(
-            'Initialization Error. Incorrect number of dimensions for axis 1.')
+        raise ValueError("Initialization Error. Incorrect number of dimensions for axis 1.")
 
     if control is not None:
         if type(control) == int and control >= data.shape[1]:
-            raise ValueError('Initialization ERROR. control is out of bounds')
+            raise ValueError("Initialization ERROR. control is out of bounds")
         if type(control) == str and control not in algorithms:
-            raise ValueError(
-                'Initialization ERROR. %s is not a column name of data' % control)
+            raise ValueError("Initialization ERROR. %s is not a column name of data" % control)
 
     if apv_procedure is not None:
-        if apv_procedure not in ['Bonferroni', 'Holm', 'Hochberg', 'Hommel', 'Holland', 'Finner', 'Li', 'Shaffer',
-                                 'Nemenyi']:
-            raise ValueError(
-                'Initialization ERROR. Incorrect value for APVprocedure.')
+        if apv_procedure not in [
+            "Bonferroni",
+            "Holm",
+            "Hochberg",
+            "Hommel",
+            "Holland",
+            "Finner",
+            "Li",
+            "Shaffer",
+            "Nemenyi",
+        ]:
+            raise ValueError("Initialization ERROR. Incorrect value for APVprocedure.")
 
     # Compute ranks.
     datarank = ranks(data)
@@ -405,28 +423,31 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if apv_procedure is None:
         return zvalues_df, pvalues_df
     else:
-        if apv_procedure == 'Bonferroni':
+        if apv_procedure == "Bonferroni":
             ap_vs_df = bonferroni_dunn(pvalues_df, control=control)
-        elif apv_procedure == 'Holm':
+        elif apv_procedure == "Holm":
             ap_vs_df = holm(pvalues_df, control=control)
-        elif apv_procedure == 'Hochberg':
+        elif apv_procedure == "Hochberg":
             ap_vs_df = hochberg(pvalues_df, control=control)
-        elif apv_procedure == 'Holland':
+        elif apv_procedure == "Holland":
             ap_vs_df = holland(pvalues_df, control=control)
-        elif apv_procedure == 'Finner':
+        elif apv_procedure == "Finner":
             ap_vs_df = finner(pvalues_df, control=control)
-        elif apv_procedure == 'Li':
+        elif apv_procedure == "Li":
             ap_vs_df = li(pvalues_df, control=control)
-        elif apv_procedure == 'Shaffer':
+        elif apv_procedure == "Shaffer":
             ap_vs_df = shaffer(pvalues_df)
-        elif apv_procedure == 'Nemenyi':
+        elif apv_procedure == "Nemenyi":
             ap_vs_df = nemenyi(pvalues_df)
 
         return zvalues_df, pvalues_df, ap_vs_df
    
 
 
-
    [docs]def friedman_aligned_ph_test(data, control=None, apv_procedure=None):
-    """ Friedman Aligned Ranks post-hoc test.
+
+
    
+[docs]
+def friedman_aligned_ph_test(data, control=None, apv_procedure=None):
+    """Friedman Aligned Ranks post-hoc test.
 
     :param data: An (n x 2) array or DataFrame contaning the results. In data, each column represents an algorithm and, and each row a problem.
     :param control: optional int or string. Default None. Index or Name of the control algorithm. If control = None all FriedmanPosHocTest considers all possible comparisons among algorithms.
@@ -448,7 +469,7 @@ 
    Source code for jmetal.lab.statistical_test.functions
    algorithms = data.columns
         data = data.values
     elif type(data) == np.ndarray:
-        algorithms = np.array(['Alg%d' % alg for alg in range(data.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(data.shape[1])])
 
     if control is None:
         index = algorithms
@@ -460,18 +481,15 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if data.ndim == 2:
         n_samples, k = data.shape
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions.')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions.")
     if k < 2:
-        raise ValueError(
-            'Initialization Error. Incorrect number of dimensions for axis 1.')
+        raise ValueError("Initialization Error. Incorrect number of dimensions for axis 1.")
 
     if control is not None:
         if type(control) == int and control >= data.shape[1]:
-            raise ValueError('Initialization ERROR. control is out of bounds')
+            raise ValueError("Initialization ERROR. control is out of bounds")
         if type(control) == str and control not in algorithms:
-            raise ValueError(
-                'Initialization ERROR. %s is not a column name of data' % control)
+            raise ValueError("Initialization ERROR. %s is not a column name of data" % control)
 
     # Compute the average value achieved by all algorithms in each problem
     problemmean = np.mean(data, axis=1)
@@ -510,28 +528,31 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if apv_procedure is None:
         return zvalues_df, pvalues_df
     else:
-        if apv_procedure == 'Bonferroni':
+        if apv_procedure == "Bonferroni":
             ap_vs_df = bonferroni_dunn(pvalues_df, control=control)
-        elif apv_procedure == 'Holm':
+        elif apv_procedure == "Holm":
             ap_vs_df = holm(pvalues_df, control=control)
-        elif apv_procedure == 'Hochberg':
+        elif apv_procedure == "Hochberg":
             ap_vs_df = hochberg(pvalues_df, control=control)
-        elif apv_procedure == 'Holland':
+        elif apv_procedure == "Holland":
             ap_vs_df = holland(pvalues_df, control=control)
-        elif apv_procedure == 'Finner':
+        elif apv_procedure == "Finner":
             ap_vs_df = finner(pvalues_df, control=control)
-        elif apv_procedure == 'Li':
+        elif apv_procedure == "Li":
             ap_vs_df = li(pvalues_df, control=control)
-        elif apv_procedure == 'Shaffer':
+        elif apv_procedure == "Shaffer":
             ap_vs_df = shaffer(pvalues_df)
-        elif apv_procedure == 'Nemenyi':
+        elif apv_procedure == "Nemenyi":
             ap_vs_df = nemenyi(pvalues_df)
 
         return zvalues_df, pvalues_df, ap_vs_df
    
 
 
-
    [docs]def quade_ph_test(data, control=None, apv_procedure=None):
-    """ Quade post-hoc test.
+
+
    
+[docs]
+def quade_ph_test(data, control=None, apv_procedure=None):
+    """Quade post-hoc test.
 
     :param data: An (n x 2) array or DataFrame contaning the results. In data, each column represents an algorithm and, and each row a problem.
     :param control: optional int or string. Default None. Index or Name of the control algorithm. If control = None all FriedmanPosHocTest considers all possible comparisons among algorithms.
@@ -553,7 +574,7 @@ 
    Source code for jmetal.lab.statistical_test.functions
    algorithms = data.columns
         data = data.values
     elif type(data) == np.ndarray:
-        algorithms = np.array(['Alg%d' % alg for alg in range(data.shape[1])])
+        algorithms = np.array(["Alg%d" % alg for alg in range(data.shape[1])])
 
     if control is None:
         index = algorithms
@@ -565,18 +586,15 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if data.ndim == 2:
         n_samples, k = data.shape
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of array dimensions.')
+        raise ValueError("Initialization ERROR. Incorrect number of array dimensions.")
     if k < 2:
-        raise ValueError(
-            'Initialization Error. Incorrect number of dimensions for axis 1.')
+        raise ValueError("Initialization Error. Incorrect number of dimensions for axis 1.")
 
     if control is not None:
         if type(control) == int and control >= data.shape[1]:
-            raise ValueError('Initialization ERROR. control is out of bounds')
+            raise ValueError("Initialization ERROR. control is out of bounds")
         if type(control) == str and control not in algorithms:
-            raise ValueError(
-                'Initialization ERROR. %s is not a column name of data' % control)
+            raise ValueError("Initialization ERROR. %s is not a column name of data" % control)
 
     # Compute ranks.
     datarank = ranks(data)
@@ -591,8 +609,7 @@ 
    Source code for jmetal.lab.statistical_test.functions
    W[i, :] = problemRank[i] * datarank[i, :]
     avranks = 2 * np.sum(W, axis=0) / (n_samples * (n_samples + 1))
     # Compute test statistics
-    aux = 1.0 / np.sqrt(k * (k + 1) * (2 * n_samples + 1) * (k - 1) /
-                        (18.0 * n_samples * (n_samples + 1)))
+    aux = 1.0 / np.sqrt(k * (k + 1) * (2 * n_samples + 1) * (k - 1) / (18.0 * n_samples * (n_samples + 1)))
     if control is None:
         z = np.zeros((k, k))
         for i in range(k):
@@ -615,24 +632,25 @@ 
    Source code for jmetal.lab.statistical_test.functions
    if apv_procedure is None:
         return zvalues_df, pvalues_df
     else:
-        if apv_procedure == 'Bonferroni':
+        if apv_procedure == "Bonferroni":
             ap_vs_df = bonferroni_dunn(pvalues_df, control=control)
-        elif apv_procedure == 'Holm':
+        elif apv_procedure == "Holm":
             ap_vs_df = holm(pvalues_df, control=control)
-        elif apv_procedure == 'Hochberg':
+        elif apv_procedure == "Hochberg":
             ap_vs_df = hochberg(pvalues_df, control=control)
-        elif apv_procedure == 'Holland':
+        elif apv_procedure == "Holland":
             ap_vs_df = holland(pvalues_df, control=control)
-        elif apv_procedure == 'Finner':
+        elif apv_procedure == "Finner":
             ap_vs_df = finner(pvalues_df, control=control)
-        elif apv_procedure == 'Li':
+        elif apv_procedure == "Li":
             ap_vs_df = li(pvalues_df, control=control)
-        elif apv_procedure == 'Shaffer':
+        elif apv_procedure == "Shaffer":
             ap_vs_df = shaffer(pvalues_df)
-        elif apv_procedure == 'Nemenyi':
+        elif apv_procedure == "Nemenyi":
             ap_vs_df = nemenyi(pvalues_df)
 
         return zvalues_df, pvalues_df, ap_vs_df
    
+
 
    
 
           
    
@@ -649,8 +667,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.statistical_test.functions
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.visualization.chord_plot — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.visualization.chord_plot
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -121,34 +118,50 @@ 
    Source code for jmetal.lab.visualization.chord_plot
    from jmetal.core.solution import FloatSolution
 
 
-
    [docs]def polar_to_cartesian(r, theta):
+
    
+[docs]
+def polar_to_cartesian(r, theta):
     return np.array([r * np.cos(theta), r * np.sin(theta)])
    
 
 
-
    [docs]def draw_sector(start_angle=0, end_angle=60, radius=1.0, width=0.2, lw=2, ls='-', ax=None, fc=(1, 0, 0), ec=(0, 0, 0),
-                z_order=1):
+
+
    
+[docs]
+def draw_sector(
+    start_angle=0, end_angle=60, radius=1.0, width=0.2, lw=2, ls="-", ax=None, fc=(1, 0, 0), ec=(0, 0, 0), z_order=1
+):
     if start_angle > end_angle:
         start_angle, end_angle = end_angle, start_angle
-    start_angle *= np.pi / 180.
-    end_angle *= np.pi / 180.
+    start_angle *= np.pi / 180.0
+    end_angle *= np.pi / 180.0
 
     # https://stackoverflow.com/questions/1734745/how-to-create-circle-with-b%C3%A9zier-curves
-    opt = 4. / 3. * np.tan((end_angle - start_angle) / 4.) * radius
+    opt = 4.0 / 3.0 * np.tan((end_angle - start_angle) / 4.0) * radius
     inner = radius * (1 - width)
 
-    vertsPath = [polar_to_cartesian(radius, start_angle),
-                 polar_to_cartesian(radius, start_angle) + polar_to_cartesian(opt, start_angle + 0.5 * np.pi),
-                 polar_to_cartesian(radius, end_angle) + polar_to_cartesian(opt, end_angle - 0.5 * np.pi),
-                 polar_to_cartesian(radius, end_angle),
-                 polar_to_cartesian(inner, end_angle),
-                 polar_to_cartesian(inner, end_angle) + polar_to_cartesian(opt * (1 - width), end_angle - 0.5 * np.pi),
-                 polar_to_cartesian(inner, start_angle) + polar_to_cartesian(opt * (1 - width),
-                                                                             start_angle + 0.5 * np.pi),
-                 polar_to_cartesian(inner, start_angle),
-                 polar_to_cartesian(radius, start_angle)]
-
-    codesPaths = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.LINETO, Path.CURVE4, Path.CURVE4,
-                  Path.CURVE4, Path.CLOSEPOLY]
+    vertsPath = [
+        polar_to_cartesian(radius, start_angle),
+        polar_to_cartesian(radius, start_angle) + polar_to_cartesian(opt, start_angle + 0.5 * np.pi),
+        polar_to_cartesian(radius, end_angle) + polar_to_cartesian(opt, end_angle - 0.5 * np.pi),
+        polar_to_cartesian(radius, end_angle),
+        polar_to_cartesian(inner, end_angle),
+        polar_to_cartesian(inner, end_angle) + polar_to_cartesian(opt * (1 - width), end_angle - 0.5 * np.pi),
+        polar_to_cartesian(inner, start_angle) + polar_to_cartesian(opt * (1 - width), start_angle + 0.5 * np.pi),
+        polar_to_cartesian(inner, start_angle),
+        polar_to_cartesian(radius, start_angle),
+    ]
+
+    codesPaths = [
+        Path.MOVETO,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.LINETO,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CLOSEPOLY,
+    ]
 
     if ax is None:
         return vertsPath, codesPaths
@@ -156,38 +169,67 @@ 
    Source code for jmetal.lab.visualization.chord_plot
    path = Path(vertsPath, codesPaths)
         patch = patches.PathPatch(path, facecolor=fc, edgecolor=ec, lw=lw, linestyle=ls, zorder=z_order)
         ax.add_patch(patch)
-        return (patch)
    
-
-
-
    [docs]def draw_chord(start_angle1=0, end_angle1=60, start_angle2=180, end_angle2=240, radius=1.0, chord_width=0.7, ax=None,
-               color=(1, 0, 0), z_order=1):
+        return patch
    
+
+
+
+
    
+[docs]
+def draw_chord(
+    start_angle1=0,
+    end_angle1=60,
+    start_angle2=180,
+    end_angle2=240,
+    radius=1.0,
+    chord_width=0.7,
+    ax=None,
+    color=(1, 0, 0),
+    z_order=1,
+):
     if start_angle1 > end_angle1:
         start_angle1, end_angle1 = end_angle1, start_angle1
     if start_angle2 > end_angle2:
         start_angle2, end_angle2 = end_angle2, start_angle2
-    start_angle1 *= np.pi / 180.
-    end_angle1 *= np.pi / 180.
-    start_angle2 *= np.pi / 180.
-    end_angle2 *= np.pi / 180.
+    start_angle1 *= np.pi / 180.0
+    end_angle1 *= np.pi / 180.0
+    start_angle2 *= np.pi / 180.0
+    end_angle2 *= np.pi / 180.0
 
-    optAngle1 = 4. / 3. * np.tan((end_angle1 - start_angle1) / 4.) * radius
-    optAngle2 = 4. / 3. * np.tan((end_angle2 - start_angle2) / 4.) * radius
+    optAngle1 = 4.0 / 3.0 * np.tan((end_angle1 - start_angle1) / 4.0) * radius
+    optAngle2 = 4.0 / 3.0 * np.tan((end_angle2 - start_angle2) / 4.0) * radius
     rchord = radius * (1 - chord_width)
 
-    vertsPath = [polar_to_cartesian(radius, start_angle1),
-                 polar_to_cartesian(radius, start_angle1) + polar_to_cartesian(optAngle1, start_angle1 + 0.5 * np.pi),
-                 polar_to_cartesian(radius, end_angle1) + polar_to_cartesian(optAngle1, end_angle1 - 0.5 * np.pi),
-                 polar_to_cartesian(radius, end_angle1),
-                 polar_to_cartesian(rchord, end_angle1), polar_to_cartesian(rchord, start_angle2),
-                 polar_to_cartesian(radius, start_angle2),
-                 polar_to_cartesian(radius, start_angle2) + polar_to_cartesian(optAngle2, start_angle2 + 0.5 * np.pi),
-                 polar_to_cartesian(radius, end_angle2) + polar_to_cartesian(optAngle2, end_angle2 - 0.5 * np.pi),
-                 polar_to_cartesian(radius, end_angle2),
-                 polar_to_cartesian(rchord, end_angle2), polar_to_cartesian(rchord, start_angle1),
-                 polar_to_cartesian(radius, start_angle1)]
-
-    codesPath = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4,
-                 Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4]
+    vertsPath = [
+        polar_to_cartesian(radius, start_angle1),
+        polar_to_cartesian(radius, start_angle1) + polar_to_cartesian(optAngle1, start_angle1 + 0.5 * np.pi),
+        polar_to_cartesian(radius, end_angle1) + polar_to_cartesian(optAngle1, end_angle1 - 0.5 * np.pi),
+        polar_to_cartesian(radius, end_angle1),
+        polar_to_cartesian(rchord, end_angle1),
+        polar_to_cartesian(rchord, start_angle2),
+        polar_to_cartesian(radius, start_angle2),
+        polar_to_cartesian(radius, start_angle2) + polar_to_cartesian(optAngle2, start_angle2 + 0.5 * np.pi),
+        polar_to_cartesian(radius, end_angle2) + polar_to_cartesian(optAngle2, end_angle2 - 0.5 * np.pi),
+        polar_to_cartesian(radius, end_angle2),
+        polar_to_cartesian(rchord, end_angle2),
+        polar_to_cartesian(rchord, start_angle1),
+        polar_to_cartesian(radius, start_angle1),
+    ]
+
+    codesPath = [
+        Path.MOVETO,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+        Path.CURVE4,
+    ]
 
     if ax == None:
         return vertsPath, codesPath
@@ -195,10 +237,13 @@ 
    Source code for jmetal.lab.visualization.chord_plot
    path = Path(vertsPath, codesPath)
         patch = patches.PathPatch(path, facecolor=color + (0.5,), edgecolor=color + (0.4,), lw=2, alpha=0.5)
         ax.add_patch(patch)
-        return (patch)
    
+        return patch
    
+
 
 
-
    [docs]def hover_over_bin(event, handle_tickers, handle_plots, colors, fig):
+
    
+[docs]
+def hover_over_bin(event, handle_tickers, handle_plots, colors, fig):
     is_found = False
 
     for iobj in range(len(handle_tickers)):
@@ -220,8 +265,17 @@ 
    Source code for jmetal.lab.visualization.chord_plot
    fig.canvas.draw_idle()
    
 
 
-
    [docs]def chord_diagram(solutions: List[FloatSolution], nbins='auto', ax=None, obj_labels=None,
-                  prop_labels=dict(fontsize=13, ha='center', va='center'), pad=6):
+
+
    
+[docs]
+def chord_diagram(
+    solutions: List[FloatSolution],
+    nbins="auto",
+    ax=None,
+    obj_labels=None,
+    prop_labels=dict(fontsize=13, ha="center", va="center"),
+    pad=6,
+):
     points_matrix = np.array([s.objectives for s in solutions])
     (NPOINTS, NOBJ) = np.shape(points_matrix)
 
@@ -230,13 +284,13 @@ 
    Source code for jmetal.lab.visualization.chord_plot
    if ax is None:
         fig = plt.figure(figsize=(6, 6))
-        ax = plt.axes([0, 0, 1, 1], aspect='equal')
+        ax = plt.axes([0, 0, 1, 1], aspect="equal")
 
     ax.set_xlim(-2.3, 2.3)
     ax.set_ylim(-2.3, 2.3)
-    ax.axis('off')
+    ax.axis("off")
 
-    y = np.array([1. / NOBJ] * NOBJ) * (360 - pad * NOBJ)
+    y = np.array([1.0 / NOBJ] * NOBJ) * (360 - pad * NOBJ)
     sector_angles = []
     labels_pos_and_ros = []
 
@@ -256,9 +310,13 @@ 
    Source code for jmetal.lab.visualization.chord_plot
    angleText -= 270
 
         labels_pos_and_ros.append(
-            tuple(polar_to_cartesian(1.0, 0.5 * (start_angle + end_angle) * np.pi / 180.)) + (angle_diff,) +
-            tuple(polar_to_cartesian(0.725, (start_angle - 2.5) * np.pi / 180.)) + (angleText,) +
-            tuple(polar_to_cartesian(0.85, (start_angle - 2.5) * np.pi / 180.)) + (angleText,))
+            tuple(polar_to_cartesian(1.0, 0.5 * (start_angle + end_angle) * np.pi / 180.0))
+            + (angle_diff,)
+            + tuple(polar_to_cartesian(0.725, (start_angle - 2.5) * np.pi / 180.0))
+            + (angleText,)
+            + tuple(polar_to_cartesian(0.85, (start_angle - 2.5) * np.pi / 180.0))
+            + (angleText,)
+        )
         start_angle = end_angle + pad
 
     arc_points = []
@@ -275,15 +333,41 @@ 
    Source code for jmetal.lab.visualization.chord_plot
    handle_tickers = []
     handle_plots = []
 
-    for iobj in tqdm(range(NOBJ), ascii=True, desc='Chord diagram'):
-        draw_sector(start_angle=sector_angles[iobj][0], end_angle=sector_angles[iobj][1], radius=0.925, width=0.225,
-                    ax=ax,
-                    fc=(1, 1, 1, 0.0), ec=(0, 0, 0), lw=2, z_order=10)
-        draw_sector(start_angle=sector_angles[iobj][0], end_angle=sector_angles[iobj][1], radius=0.925, width=0.05,
-                    ax=ax,
-                    fc=colors[iobj], ec=(0, 0, 0), lw=2, z_order=10)
-        draw_sector(start_angle=sector_angles[iobj][0], end_angle=sector_angles[iobj][1], radius=0.7 + 0.15, width=0.0,
-                    ax=ax, fc=colors[iobj], ec=colors[iobj], lw=2, ls=':', z_order=5)
+    for iobj in tqdm(range(NOBJ), ascii=True, desc="Chord diagram"):
+        draw_sector(
+            start_angle=sector_angles[iobj][0],
+            end_angle=sector_angles[iobj][1],
+            radius=0.925,
+            width=0.225,
+            ax=ax,
+            fc=(1, 1, 1, 0.0),
+            ec=(0, 0, 0),
+            lw=2,
+            z_order=10,
+        )
+        draw_sector(
+            start_angle=sector_angles[iobj][0],
+            end_angle=sector_angles[iobj][1],
+            radius=0.925,
+            width=0.05,
+            ax=ax,
+            fc=colors[iobj],
+            ec=(0, 0, 0),
+            lw=2,
+            z_order=10,
+        )
+        draw_sector(
+            start_angle=sector_angles[iobj][0],
+            end_angle=sector_angles[iobj][1],
+            radius=0.7 + 0.15,
+            width=0.0,
+            ax=ax,
+            fc=colors[iobj],
+            ec=colors[iobj],
+            lw=2,
+            ls=":",
+            z_order=5,
+        )
 
         histValues, binsDim = np.histogram(points_matrix[:, iobj], bins=nbins)
         relativeHeightBinPre = 0.025
@@ -292,73 +376,126 @@ 
    Source code for jmetal.lab.visualization.chord_plot
    handle_plots.append([])
 
         for indexBin in range(len(histValues)):
-            startAngleBin = sector_angles[iobj][0] + (sector_angles[iobj][1] - sector_angles[iobj][0]) * binsDim[
-                indexBin]
-            endAngleBin = sector_angles[iobj][0] + (sector_angles[iobj][1] - sector_angles[iobj][0]) * binsDim[
-                indexBin + 1]
+            startAngleBin = (
+                sector_angles[iobj][0] + (sector_angles[iobj][1] - sector_angles[iobj][0]) * binsDim[indexBin]
+            )
+            endAngleBin = (
+                sector_angles[iobj][0] + (sector_angles[iobj][1] - sector_angles[iobj][0]) * binsDim[indexBin + 1]
+            )
             relativeHeightBin = 0.15 * histValues[indexBin] / max(histValues)
             handle_tickers[-1].append(
-                draw_sector(start_angle=startAngleBin, end_angle=endAngleBin, radius=0.69, width=0.08, ax=ax, lw=1,
-                            fc=(1, 1, 1), ec=(0, 0, 0)))
+                draw_sector(
+                    start_angle=startAngleBin,
+                    end_angle=endAngleBin,
+                    radius=0.69,
+                    width=0.08,
+                    ax=ax,
+                    lw=1,
+                    fc=(1, 1, 1),
+                    ec=(0, 0, 0),
+                )
+            )
             handle_plots[-1].append([])
 
             if histValues[indexBin] > 0:
-                draw_sector(start_angle=startAngleBin, end_angle=endAngleBin, radius=0.7 + relativeHeightBin, width=0,
-                            ax=ax, lw=1, fc=colors[iobj], ec=colors[iobj])
-                plotPoint1 = polar_to_cartesian(0.7 + relativeHeightBinPre, startAngleBin * np.pi / 180.)
-                plotPoint2 = polar_to_cartesian(0.7 + relativeHeightBin, startAngleBin * np.pi / 180.)
+                draw_sector(
+                    start_angle=startAngleBin,
+                    end_angle=endAngleBin,
+                    radius=0.7 + relativeHeightBin,
+                    width=0,
+                    ax=ax,
+                    lw=1,
+                    fc=colors[iobj],
+                    ec=colors[iobj],
+                )
+                plotPoint1 = polar_to_cartesian(0.7 + relativeHeightBinPre, startAngleBin * np.pi / 180.0)
+                plotPoint2 = polar_to_cartesian(0.7 + relativeHeightBin, startAngleBin * np.pi / 180.0)
                 plt.plot([plotPoint1[0], plotPoint2[0]], [plotPoint1[1], plotPoint2[1]], c=colors[iobj], lw=1)
                 relativeHeightBinPre = relativeHeightBin
             else:
-                plotPoint1 = polar_to_cartesian(0.7 + relativeHeightBinPre, startAngleBin * np.pi / 180.)
-                plotPoint2 = polar_to_cartesian(0.725 + relativeHeightBin, startAngleBin * np.pi / 180.)
+                plotPoint1 = polar_to_cartesian(0.7 + relativeHeightBinPre, startAngleBin * np.pi / 180.0)
+                plotPoint2 = polar_to_cartesian(0.725 + relativeHeightBin, startAngleBin * np.pi / 180.0)
                 plt.plot([plotPoint1[0], plotPoint2[0]], [plotPoint1[1], plotPoint2[1]], c=colors[iobj], lw=1)
                 relativeHeightBinPre = 0.025
             if indexBin == len(histValues) - 1:
-                plotPoint1 = polar_to_cartesian(0.7 + relativeHeightBin, endAngleBin * np.pi / 180.)
-                plotPoint2 = polar_to_cartesian(0.725, endAngleBin * np.pi / 180.)
+                plotPoint1 = polar_to_cartesian(0.7 + relativeHeightBin, endAngleBin * np.pi / 180.0)
+                plotPoint2 = polar_to_cartesian(0.725, endAngleBin * np.pi / 180.0)
                 plt.plot([plotPoint1[0], plotPoint2[0]], [plotPoint1[1], plotPoint2[1]], c=colors[iobj], lw=1)
 
             for ipoint in range(len(points_matrix)):
-                plotPoint1 = polar_to_cartesian(0.6, arc_points[ipoint][iobj][0] * np.pi / 180.)
-                plotPoint2 = polar_to_cartesian(0.6, arc_points[ipoint][iobj][0] * np.pi / 180.)
-                plt.plot([plotPoint1[0], plotPoint2[0]], [plotPoint1[1], plotPoint2[1]], marker='o', markersize=3,
-                         c=colors[iobj], lw=2)
-
-                if binsDim[indexBin] < points_matrix[ipoint, iobj] <= binsDim[
-                    indexBin + 1]:
+                plotPoint1 = polar_to_cartesian(0.6, arc_points[ipoint][iobj][0] * np.pi / 180.0)
+                plotPoint2 = polar_to_cartesian(0.6, arc_points[ipoint][iobj][0] * np.pi / 180.0)
+                plt.plot(
+                    [plotPoint1[0], plotPoint2[0]],
+                    [plotPoint1[1], plotPoint2[1]],
+                    marker="o",
+                    markersize=3,
+                    c=colors[iobj],
+                    lw=2,
+                )
+
+                if binsDim[indexBin] < points_matrix[ipoint, iobj] <= binsDim[indexBin + 1]:
                     for jdim in range(NOBJ):
                         if jdim >= 1:
                             handle_plots[iobj][indexBin].append(
-                                draw_chord(arc_points[ipoint][jdim - 1][0], arc_points[ipoint][jdim - 1][1],
-                                           arc_points[ipoint][jdim][0], arc_points[ipoint][jdim][1], radius=0.55,
-                                           color=colors[iobj], chord_width=1, ax=ax))
+                                draw_chord(
+                                    arc_points[ipoint][jdim - 1][0],
+                                    arc_points[ipoint][jdim - 1][1],
+                                    arc_points[ipoint][jdim][0],
+                                    arc_points[ipoint][jdim][1],
+                                    radius=0.55,
+                                    color=colors[iobj],
+                                    chord_width=1,
+                                    ax=ax,
+                                )
+                            )
                             handle_plots[iobj][indexBin][-1].set_visible(False)
                     handle_plots[iobj][indexBin].append(
-                        draw_chord(arc_points[ipoint][-1][0], arc_points[ipoint][-1][1], arc_points[ipoint][0][0],
-                                   arc_points[ipoint][0][1], radius=0.55, color=colors[iobj], chord_width=1, ax=ax))
+                        draw_chord(
+                            arc_points[ipoint][-1][0],
+                            arc_points[ipoint][-1][1],
+                            arc_points[ipoint][0][0],
+                            arc_points[ipoint][0][1],
+                            radius=0.55,
+                            color=colors[iobj],
+                            chord_width=1,
+                            ax=ax,
+                        )
+                    )
                     handle_plots[iobj][indexBin][-1].set_visible(False)
 
     if obj_labels is None:
-        obj_labels = ['$f_{' + str(i) + '}(\mathbf{x})$' for i in range(NOBJ)]
+        obj_labels = ["$f_{" + str(i) + "}(\mathbf{x})$" for i in range(NOBJ)]
 
-    prop_legend_bins = dict(fontsize=9, ha='center', va='center')
+    prop_legend_bins = dict(fontsize=9, ha="center", va="center")
 
     for i in range(NOBJ):
-        p0, p1 = polar_to_cartesian(0.975, sector_angles[i][0] * np.pi / 180.)
-        ax.text(p0, p1, '0', **prop_legend_bins)
-        p0, p1 = polar_to_cartesian(0.975, sector_angles[i][1] * np.pi / 180.)
-        ax.text(p0, p1, '1', **prop_legend_bins)
-        ax.text(labels_pos_and_ros[i][0], labels_pos_and_ros[i][1], obj_labels[i], rotation=labels_pos_and_ros[i][2],
-                **prop_labels)
-        ax.text(labels_pos_and_ros[i][3], labels_pos_and_ros[i][4], '0', **prop_legend_bins, color=colors[i])
-        ax.text(labels_pos_and_ros[i][6], labels_pos_and_ros[i][7], str(max_hist_values[i]), **prop_legend_bins,
-                color=colors[i])
+        p0, p1 = polar_to_cartesian(0.975, sector_angles[i][0] * np.pi / 180.0)
+        ax.text(p0, p1, "0", **prop_legend_bins)
+        p0, p1 = polar_to_cartesian(0.975, sector_angles[i][1] * np.pi / 180.0)
+        ax.text(p0, p1, "1", **prop_legend_bins)
+        ax.text(
+            labels_pos_and_ros[i][0],
+            labels_pos_and_ros[i][1],
+            obj_labels[i],
+            rotation=labels_pos_and_ros[i][2],
+            **prop_labels
+        )
+        ax.text(labels_pos_and_ros[i][3], labels_pos_and_ros[i][4], "0", **prop_legend_bins, color=colors[i])
+        ax.text(
+            labels_pos_and_ros[i][6],
+            labels_pos_and_ros[i][7],
+            str(max_hist_values[i]),
+            **prop_legend_bins,
+            color=colors[i]
+        )
 
     plt.axis([-1.2, 1.2, -1.2, 1.2])
-    fig.canvas.mpl_connect("motion_notify_event",
-                           lambda event: hover_over_bin(event, handle_tickers, handle_plots, colors, fig))
+    fig.canvas.mpl_connect(
+        "motion_notify_event", lambda event: hover_over_bin(event, handle_tickers, handle_plots, colors, fig)
+    )
     plt.show()
    
+
 
    
 
           
    
@@ -375,8 +512,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.visualization.chord_plot
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.visualization.interactive — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.visualization.interactive
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -110,7 +107,7 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.lab.visualization.interactive
     import logging
-from typing import TypeVar, List
+from typing import List, TypeVar
 
 import pandas as pd
 from plotly import graph_objs as go
@@ -119,25 +116,30 @@ 
    Source code for jmetal.lab.visualization.interactive
    from jmetal.lab.visualization.plotting import Plot
 
-LOGGER = logging.getLogger('jmetal')
-
-S = TypeVar('S')
+logger = logging.getLogger(__name__)
 
+S = TypeVar("S")
 
-
    [docs]class InteractivePlot(Plot):
 
-    def __init__(self,
-                 title: str = 'Pareto front approximation',
-                 reference_front: List[S] = None,
-                 reference_point: list = None,
-                 axis_labels: list = None):
+
    
+[docs]
+class InteractivePlot(Plot):
+    def __init__(
+        self,
+        title: str = "Pareto front approximation",
+        reference_front: List[S] = None,
+        reference_point: list = None,
+        axis_labels: list = None,
+    ):
         super(InteractivePlot, self).__init__(title, reference_front, reference_point, axis_labels)
         self.figure = None
         self.layout = None
         self.data = []
 
-
    [docs]    def plot(self, front, label=None, normalize: bool = False, filename: str = None, format: str = 'HTML'):
-        """ Plot a front of solutions (2D, 3D or parallel coordinates).
+
    
+[docs]
+    def plot(self, front, label=None, normalize: bool = False, filename: str = None, format: str = "HTML"):
+        """Plot a front of solutions (2D, 3D or parallel coordinates).
 
         :param front: List of solutions.
         :param label: Front name.
@@ -150,109 +152,127 @@ 
    Source code for jmetal.lab.visualization.interactive
    self.layout = go.Layout(
             margin=dict(l=80, r=80, b=80, t=150),
             height=800,
-            title='{}<br>{}'.format(self.plot_title, label[0]),
+            title="{}<br>{}".format(self.plot_title, label[0]),
             scene=dict(
                 xaxis=dict(title=self.axis_labels[0:1][0] if self.axis_labels[0:1] else None),
                 yaxis=dict(title=self.axis_labels[1:2][0] if self.axis_labels[1:2] else None),
-                zaxis=dict(title=self.axis_labels[2:3][0] if self.axis_labels[2:3] else None)
+                zaxis=dict(title=self.axis_labels[2:3][0] if self.axis_labels[2:3] else None),
             ),
-            hovermode='closest'
+            hovermode="closest",
         )
 
         # If any reference front, plot
         if self.reference_front:
             points, _ = self.get_points(self.reference_front)
-            trace = self.__generate_trace(points=points, legend='Reference front', normalize=normalize,
-                                          color='black', size=2)
+            trace = self.__generate_trace(
+                points=points, legend="Reference front", normalize=normalize, color="black", size=2
+            )
             self.data.append(trace)
 
         # If any reference point, plot
         if self.reference_point:
             points = pd.DataFrame(self.reference_point)
-            trace = self.__generate_trace(points=points, legend='Reference point', color='red', size=8)
+            trace = self.__generate_trace(points=points, legend="Reference point", color="red", size=8)
             self.data.append(trace)
 
         # Get points and metadata
         points, _ = self.get_points(front)
         metadata = list(solution.__str__() for solution in front)
 
-        trace = self.__generate_trace(points=points, metadata=metadata, legend='Front approximation',
-                                      normalize=normalize)
+        trace = self.__generate_trace(
+            points=points, metadata=metadata, legend="Front approximation", normalize=normalize
+        )
         self.data.append(trace)
         self.figure = go.Figure(data=self.data, layout=self.layout)
 
         # Plot the figure
         if filename:
-            if format == 'HTML':
+            if format == "HTML":
                 self.export_to_html(filename)
+                logger.info("Figure {_filename} exported to HTML file")
             else:
-                pio.write_image(self.figure, filename + '.' + format)
    
+                _filename = filename + "." + format
 
-
    [docs]    def export_to_html(self, filename: str) -> str:
-        """ Export the graph to an interactive HTML (solutions can be selected to show some metadata).
+                pio.write_image(self.figure, _filename)
+                logger.info("Figure {_filename} saved to file")
    
+
+
+
    
+[docs]
+    def export_to_html(self, filename: str) -> str:
+        """Export the graph to an interactive HTML (solutions can be selected to show some metadata).
 
         :param filename: Output file name.
-        :return: Script as string. """
-        html_string = '''
-        <!DOCTYPE html>
-        <html>
-            <head>
-                <meta charset="utf-8"/>
-                <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
-                <script src="https://unpkg.com/sweetalert2@7.7.0/dist/sweetalert2.all.js"></script>
-                <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css">
-            </head>
-            <body>
-                ''' + self.export_to_div(filename=None, include_plotlyjs=False) + '''
-                <script>                
-                    var myPlot = document.querySelectorAll('div')[0];
-                    myPlot.on('plotly_click', function(data){
-                        var pts = '';
-
-                        for(var i=0; i < data.points.length; i++){
-                            pts = '(x, y) = ('+data.points[i].x +', '+ data.points[i].y.toPrecision(4)+')';
-                            cs = data.points[i].customdata
-                        }
-
-                        if(typeof cs !== "undefined"){
-                            swal({
-                              title: 'Closest solution clicked:',
-                              text: cs,
-                              type: 'info',
-                              position: 'bottom-end'
-                            })
-                        }
-                    });
-
-                    window.onresize = function() {
-                       Plotly.Plots.resize(myPlot);
-                    };
-                </script>
-            </body>
-        </html>'''
-
-        with open(filename + '.html', 'w') as outf:
+        :return: Script as string."""
+        html_string = (
+            """
+        <!DOCTYPE html>
+        <html>
+            <head>
+                <meta charset="utf-8"/>
+                <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
+                <script src="https://unpkg.com/sweetalert2@7.7.0/dist/sweetalert2.all.js"></script>
+                <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css">
+            </head>
+            <body>
+                """
+            + self.export_to_div(filename=None, include_plotlyjs=False)
+            + """
+                <script>                
+                    var myPlot = document.querySelectorAll('div')[0];
+                    myPlot.on('plotly_click', function(data){
+                        var pts = '';
+
+                        for(var i=0; i < data.points.length; i++){
+                            pts = '(x, y) = ('+data.points[i].x +', '+ data.points[i].y.toPrecision(4)+')';
+                            cs = data.points[i].customdata
+                        }
+
+                        if(typeof cs !== "undefined"){
+                            swal({
+                              title: 'Closest solution clicked:',
+                              text: cs,
+                              type: 'info',
+                              position: 'bottom-end'
+                            })
+                        }
+                    });
+
+                    window.onresize = function() {
+                       Plotly.Plots.resize(myPlot);
+                    };
+                </script>
+            </body>
+        </html>"""
+        )
+
+        with open(filename + ".html", "w") as outf:
             outf.write(html_string)
 
         return html_string
    
 
-
    [docs]    def export_to_div(self, filename=None, include_plotlyjs: bool = False) -> str:
-        """ Export as a `div` for embedding the graph in an HTML file.
+
+
    
+[docs]
+    def export_to_div(self, filename=None, include_plotlyjs: bool = False) -> str:
+        """Export as a `div` for embedding the graph in an HTML file.
 
         :param filename: Output file name (if desired, default to None).
         :param include_plotlyjs: If True, include plot.ly JS script (default to False).
         :return: Script as string.
         """
-        script = offline.plot(self.figure, output_type='div', include_plotlyjs=include_plotlyjs, show_link=False)
+        script = offline.plot(self.figure, output_type="div", include_plotlyjs=include_plotlyjs, show_link=False)
 
         if filename:
-            with open(filename + '.html', 'w') as outf:
+            with open(filename + ".html", "w") as outf:
                 outf.write(script)
 
         return script
    
 
-    def __generate_trace(self, points: pd.DataFrame, legend: str, metadata: list = None, normalize: bool = False,
-                         **kwargs):
+
+    def __generate_trace(
+        self, points: pd.DataFrame, legend: str, metadata: list = None, normalize: bool = False, **kwargs
+    ):
         dimension = points.shape[1]
 
         # tweak points size for 3D plots
@@ -265,54 +285,39 @@ 
    Source code for jmetal.lab.visualization.interactive
    points = (points - points.min()) / (points.max() - points.min())
 
         marker = dict(
-            color='#236FA4',
-            size=marker_size,
-            symbol='circle',
-            line=dict(
-                color='#236FA4',
-                width=1
-            ),
-            opacity=0.8
+            color="#236FA4", size=marker_size, symbol="circle", line=dict(color="#236FA4", width=1), opacity=0.8
         )
         marker.update(**kwargs)
 
         if dimension == 2:
             trace = go.Scattergl(
-                x=points[0],
-                y=points[1],
-                mode='markers',
-                marker=marker,
-                name=legend,
-                customdata=metadata
+                x=points[0], y=points[1], mode="markers", marker=marker, name=legend, customdata=metadata
             )
         elif dimension == 3:
             trace = go.Scatter3d(
-                x=points[0],
-                y=points[1],
-                z=points[2],
-                mode='markers',
-                marker=marker,
-                name=legend,
-                customdata=metadata
+                x=points[0], y=points[1], z=points[2], mode="markers", marker=marker, name=legend, customdata=metadata
             )
         else:
             dimensions = list()
             for column in points:
                 dimensions.append(
-                    dict(range=[0, 1],
-                         label=self.axis_labels[column:column + 1][0] if self.axis_labels[column:column + 1] else None,
-                         values=points[column])
+                    dict(
+                        range=[0, 1],
+                        label=self.axis_labels[column : column + 1][0]
+                        if self.axis_labels[column : column + 1]
+                        else None,
+                        values=points[column],
+                    )
                 )
 
             trace = go.Parcoords(
-                line=dict(
-                    color='#236FA4'
-                ),
+                line=dict(color="#236FA4"),
                 dimensions=dimensions,
                 name=legend,
             )
 
         return trace
    
+
 
    
 
           
    
@@ -329,8 +334,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.visualization.interactive
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.visualization.plotting — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
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    • 
+          
  • Module code »
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+        
  • jmetal.lab.visualization.plotting
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -110,26 +107,28 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.lab.visualization.plotting
     import logging
-from typing import TypeVar, List, Tuple
+from typing import List, Tuple, TypeVar
 
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
-from pandas import plotting
-
-LOGGER = logging.getLogger('jmetal')
 
-S = TypeVar('S')
+logger = logging.getLogger(__name__)
 
+S = TypeVar("S")
 
-
    [docs]class Plot:
 
-    def __init__(self,
-                 title: str = 'Pareto front approximation',
-                 reference_front: List[S] = None,
-                 reference_point: list = None,
-                 axis_labels: list = None):
-        """
+
    
+[docs]
+class Plot:
+    def __init__(
+        self,
+        title: str = "Pareto front approximation",
+        reference_front: List[S] = None,
+        reference_point: list = None,
+        axis_labels: list = None,
+    ):
+        """
         :param title: Title of the graph.
         :param axis_labels: List of axis labels.
         :param reference_point: Reference point (e.g., [0.4, 1.2]).
@@ -145,21 +144,26 @@ 
    Source code for jmetal.lab.visualization.plotting
    self.reference_front = reference_front
         self.dimension = None
 
-
    [docs]    @staticmethod
+
    
+[docs]
+    @staticmethod
     def get_points(solutions: List[S]) -> Tuple[pd.DataFrame, int]:
-        """ Get points for each solution of the front.
+        """Get points for each solution of the front.
 
         :param solutions: List of solutions.
         :return: Pandas dataframe with one column for each objective and one row for each solution.
         """
         if solutions is None:
-            raise Exception('Front is none!')
+            raise Exception("Front is none!")
 
         points = pd.DataFrame(list(solution.objectives for solution in solutions))
         return points, points.shape[1]
    
 
-
    [docs]    def plot(self, front, label='', normalize: bool = False, filename: str = None, format: str = 'eps'):
-        """ Plot any arbitrary number of fronts in 2D, 3D or p-coords.
+
+
    
+[docs]
+    def plot(self, front, label="", normalize: bool = False, filename: str = None, format: str = "eps"):
+        """Plot any arbitrary number of fronts in 2D, 3D or p-coords.
 
         :param front: Pareto front or a list of them.
         :param label: Pareto front title or a list of them.
@@ -174,9 +178,9 @@ 
    Source code for jmetal.lab.visualization.plotting
    label = [label]
 
         if len(front) != len(label):
-            raise Exception('Number of fronts and labels must be the same')
+            raise Exception("Number of fronts and labels must be the same")
 
-        dimension = front[0][0].number_of_objectives
+        dimension = len(front[0][0].objectives)
 
         if dimension == 2:
             self.two_dim(front, label, filename, format)
@@ -185,8 +189,11 @@ 
    Source code for jmetal.lab.visualization.plotting
    else:
             self.pcoords(front, normalize, filename, format)
    
 
-
    [docs]    def two_dim(self, fronts: List[list], labels: List[str] = None, filename: str = None, format: str = 'eps'):
-        """ Plot any arbitrary number of fronts in 2D.
+
+
    
+[docs]
+    def two_dim(self, fronts: List[list], labels: List[str] = None, filename: str = None, format: str = "eps"):
+        """Plot any arbitrary number of fronts in 2D.
 
         :param fronts: List of fronts (containing solutions).
         :param labels: List of fronts title (if any).
@@ -204,33 +211,39 @@ 
    Source code for jmetal.lab.visualization.plotting
    points, _ = self.get_points(fronts[i])
 
             ax = fig.add_subplot(n, n, i + 1)
-            points.plot(kind='scatter', x=0, y=1, ax=ax, s=10, color='#236FA4', alpha=1.0)
+            points.plot(kind="scatter", x=0, y=1, ax=ax, s=10, color="#236FA4", alpha=1.0)
 
             if labels:
                 ax.set_title(labels[i])
 
             if self.reference_front:
-                reference.plot(x=0, y=1, ax=ax, color='k', legend=False)
+                reference.plot(x=0, y=1, ax=ax, color="k", legend=False)
 
             if self.reference_point:
                 for point in self.reference_point:
-                    plt.plot([point[0]], [point[1]], marker='o', markersize=5, color='r')
-                    plt.axvline(x=point[0], color='r', linestyle=':')
-                    plt.axhline(y=point[1], color='r', linestyle=':')
+                    plt.plot([point[0]], [point[1]], marker="o", markersize=5, color="r")
+                    plt.axvline(x=point[0], color="r", linestyle=":")
+                    plt.axhline(y=point[1], color="r", linestyle=":")
 
             if self.axis_labels:
                 plt.xlabel(self.axis_labels[0])
                 plt.ylabel(self.axis_labels[1])
 
         if filename:
-            plt.savefig(filename + '.' + format, format=format, dpi=200)
+            _filename = filename + "." + format
+
+            plt.savefig(_filename, format=format, dpi=1000)
+            logger.info("Figure {_filename} saved to file")
         else:
             plt.show()
 
         plt.close(fig=fig)
    
 
-
    [docs]    def three_dim(self, fronts: List[list], labels: List[str] = None, filename: str = None, format: str = 'eps'):
-        """ Plot any arbitrary number of fronts in 3D.
+
+
    
+[docs]
+    def three_dim(self, fronts: List[list], labels: List[str] = None, filename: str = None, format: str = "eps"):
+        """Plot any arbitrary number of fronts in 3D.
 
         :param fronts: List of fronts (containing solutions).
         :param labels: List of fronts title (if any).
@@ -241,18 +254,22 @@ 
    Source code for jmetal.lab.visualization.plotting
    fig.suptitle(self.plot_title, fontsize=16)
 
         for i, _ in enumerate(fronts):
-            ax = fig.add_subplot(n, n, i + 1, projection='3d')
-            ax.scatter([s.objectives[0] for s in fronts[i]],
-                       [s.objectives[1] for s in fronts[i]],
-                       [s.objectives[2] for s in fronts[i]])
+            ax = fig.add_subplot(n, n, i + 1, projection="3d")
+            ax.scatter(
+                [s.objectives[0] for s in fronts[i]],
+                [s.objectives[1] for s in fronts[i]],
+                [s.objectives[2] for s in fronts[i]],
+            )
 
             if labels:
                 ax.set_title(labels[i])
 
             if self.reference_front:
-                ax.scatter([s.objectives[0] for s in self.reference_front],
-                           [s.objectives[1] for s in self.reference_front],
-                           [s.objectives[2] for s in self.reference_front])
+                ax.scatter(
+                    [s.objectives[0] for s in self.reference_front],
+                    [s.objectives[1] for s in self.reference_front],
+                    [s.objectives[2] for s in self.reference_front],
+                )
 
             if self.reference_point:
                 # todo
@@ -264,14 +281,20 @@ 
    Source code for jmetal.lab.visualization.plotting
    ax.locator_params(nbins=4)
 
         if filename:
-            plt.savefig(filename + '.' + format, format=format, dpi=1000)
+            _filename = filename + "." + format
+
+            plt.savefig(_filename, format=format, dpi=1000)
+            logger.info("Figure {_filename} saved to file")
         else:
             plt.show()
 
         plt.close(fig=fig)
    
 
-
    [docs]    def pcoords(self, fronts: List[list], normalize: bool = False, filename: str = None, format: str = 'eps'):
-        """ Plot any arbitrary number of fronts in parallel coordinates.
+
+
    
+[docs]
+    def pcoords(self, fronts: List[list], normalize: bool = False, filename: str = None, format: str = "eps"):
+        """Plot any arbitrary number of fronts in parallel coordinates.
 
         :param fronts: List of fronts (containing solutions).
         :param filename: Output filename.
@@ -289,8 +312,8 @@ 
    Source code for jmetal.lab.visualization.plotting
    ax = fig.add_subplot(n, n, i + 1)
 
             min_, max_ = points.values.min(), points.values.max()
-            points['scale'] = np.linspace(0, 1, len(points)) * (max_ - min_) + min_
-            pd.plotting.parallel_coordinates(points, 'scale', ax=ax)
+            points["scale"] = np.linspace(0, 1, len(points)) * (max_ - min_) + min_
+            pd.plotting.parallel_coordinates(points, "scale", ax=ax)
 
             ax.get_legend().remove()
 
@@ -298,11 +321,13 @@ 
    Source code for jmetal.lab.visualization.plotting
    ax.set_xticklabels(self.axis_labels)
 
         if filename:
-            plt.savefig(filename + '.' + format, format=format, dpi=1000)
+            plt.savefig(filename + "." + format, format=format, dpi=1000)
         else:
             plt.show()
 
-        plt.close(fig=fig)
    
+        plt.close(fig=fig)
    
+
    
+
 
    
 
           
    
@@ -319,8 +344,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.visualization.plotting
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.visualization.posterior — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
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+          
  • Module code »
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+        
  • jmetal.lab.visualization.posterior
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -109,14 +106,25 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.lab.visualization.posterior
    -import numpy as np
+import logging
+
+import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
 
+logger = logging.getLogger(__name__)
 
-
    [docs]def plot_posterior(sample, higher_is_better: bool = False, min_points_per_hexbin: int = 2, alg_names: list = None,
-                   filename: str = 'posterior.eps'):
-    """
+
+
    
+[docs]
+def plot_posterior(
+    sample,
+    higher_is_better: bool = False,
+    min_points_per_hexbin: int = 2,
+    alg_names: list = None,
+    filename: str = "posterior.eps",
+):
+    """
     Plots the sample from posterior distribution of a Bayesian statistical test.
     Parameters:
     -----------
@@ -135,20 +143,18 @@ 
    Source code for jmetal.lab.visualization.posterior
    if sample.ndim == 2:
         nrow, ncol = sample.shape
         if ncol != 3:
-            raise ValueError(
-                'Initialization ERROR. Incorrect number of dimensions in axis 1.')
+            raise ValueError("Initialization ERROR. Incorrect number of dimensions in axis 1.")
     else:
-        raise ValueError(
-            'Initialization ERROR. Incorrect number of dimensions for sample')
+        raise ValueError("Initialization ERROR. Incorrect number of dimensions for sample")
 
     def transform(p):
         lambda1, lambda2, lambda3 = p.T
-        x = (0.1 * lambda1 + 0.5 * lambda2 + 0.9 * lambda3)
+        x = 0.1 * lambda1 + 0.5 * lambda2 + 0.9 * lambda3
         y = (0.2 * lambda1 + 1.4 * lambda2 + 0.2 * lambda3) / np.sqrt(3)
         return np.vstack((x, y)).T
 
     # Initialize figure
-    fig = plt.figure(figsize=(5, 5), facecolor='white')
+    fig = plt.figure(figsize=(5, 5), facecolor="white")
     ax = fig.add_axes([0, 0, 1, 1])
     ax.set_xlim(0, 1)
     ax.set_ylim(0, 1)
@@ -158,34 +164,22 @@ 
    Source code for jmetal.lab.visualization.posterior
    if not higher_is_better:
         if not alg_names:
-            ax.text(x=0.5, y=1.4 / np.sqrt(3) + 0.005,
-                    s='P(rope)', ha='center', va='bottom')
-            ax.text(x=0.15, y=0.175 / np.sqrt(3) - 0.005,
-                    s='P(alg1<alg2)', ha='right', va='top')
-            ax.text(x=0.85, y=0.175 / np.sqrt(3) - 0.005,
-                    s='P(alg1>alg2)', ha='left', va='top')
+            ax.text(x=0.5, y=1.4 / np.sqrt(3) + 0.005, s="P(rope)", ha="center", va="bottom")
+            ax.text(x=0.15, y=0.175 / np.sqrt(3) - 0.005, s="P(alg1<alg2)", ha="right", va="top")
+            ax.text(x=0.85, y=0.175 / np.sqrt(3) - 0.005, s="P(alg1>alg2)", ha="left", va="top")
         else:
-            ax.text(x=0.5, y=1.4 / np.sqrt(3) + 0.005,
-                    s='P(rope)', ha='center', va='bottom')
-            ax.text(x=0.15, y=0.175 / np.sqrt(3) - 0.005,
-                    s='P(' + alg_names[0] + ')', ha='right', va='top')
-            ax.text(x=0.85, y=0.175 / np.sqrt(3) - 0.005,
-                    s='P(' + alg_names[1] + ')', ha='left', va='top')
+            ax.text(x=0.5, y=1.4 / np.sqrt(3) + 0.005, s="P(rope)", ha="center", va="bottom")
+            ax.text(x=0.15, y=0.175 / np.sqrt(3) - 0.005, s="P(" + alg_names[0] + ")", ha="right", va="top")
+            ax.text(x=0.85, y=0.175 / np.sqrt(3) - 0.005, s="P(" + alg_names[1] + ")", ha="left", va="top")
     else:
         if not alg_names:
-            ax.text(x=0.5, y=1.4 / np.sqrt(3) + 0.005,
-                    s='P(rope)', ha='center', va='bottom')
-            ax.text(x=0.15, y=0.175 / np.sqrt(3) - 0.005,
-                    s='P(alg2<alg1)', ha='right', va='top')
-            ax.text(x=0.85, y=0.175 / np.sqrt(3) - 0.005,
-                    s='P(alg2>alg1)', ha='left', va='top')
+            ax.text(x=0.5, y=1.4 / np.sqrt(3) + 0.005, s="P(rope)", ha="center", va="bottom")
+            ax.text(x=0.15, y=0.175 / np.sqrt(3) - 0.005, s="P(alg2<alg1)", ha="right", va="top")
+            ax.text(x=0.85, y=0.175 / np.sqrt(3) - 0.005, s="P(alg2>alg1)", ha="left", va="top")
         else:
-            ax.text(x=0.5, y=1.4 / np.sqrt(3) + 0.005,
-                    s='P(rope)', ha='center', va='bottom')
-            ax.text(x=0.15, y=0.175 / np.sqrt(3) - 0.005,
-                    s='P(' + alg_names[1] + ')', ha='right', va='top')
-            ax.text(x=0.85, y=0.175 / np.sqrt(3) - 0.005,
-                    s='P(' + alg_names[0] + ')', ha='left', va='top')
+            ax.text(x=0.5, y=1.4 / np.sqrt(3) + 0.005, s="P(rope)", ha="center", va="bottom")
+            ax.text(x=0.15, y=0.175 / np.sqrt(3) - 0.005, s="P(" + alg_names[1] + ")", ha="right", va="top")
+            ax.text(x=0.85, y=0.175 / np.sqrt(3) - 0.005, s="P(" + alg_names[0] + ")", ha="left", va="top")
 
     # Conversion between barycentric and Cartesian coordinates
     sample2d = np.zeros((sample.shape[0], 2))
@@ -197,32 +191,25 @@ 
    Source code for jmetal.lab.visualization.posterior
    # Plot triangle
 
-    ax.plot([0.095, 0.505], [0.2 / np.sqrt(3), 1.4 / np.sqrt(3)],
-            linewidth=3.0, color='white')
-    ax.plot([0.505, 0.905], [1.4 / np.sqrt(3), 0.2 / np.sqrt(3)],
-            linewidth=3.0, color='white')
-    ax.plot([0.09, 0.905], [0.2 / np.sqrt(3), 0.2 / np.sqrt(3)],
-            linewidth=3.0, color='white')
+    ax.plot([0.095, 0.505], [0.2 / np.sqrt(3), 1.4 / np.sqrt(3)], linewidth=3.0, color="white")
+    ax.plot([0.505, 0.905], [1.4 / np.sqrt(3), 0.2 / np.sqrt(3)], linewidth=3.0, color="white")
+    ax.plot([0.09, 0.905], [0.2 / np.sqrt(3), 0.2 / np.sqrt(3)], linewidth=3.0, color="white")
 
-    ax.plot([0.1, 0.5], [0.2 / np.sqrt(3), 1.4 / np.sqrt(3)],
-            linewidth=3.0, color='gray')
-    ax.plot([0.5, 0.9], [1.4 / np.sqrt(3), 0.2 / np.sqrt(3)],
-            linewidth=3.0, color='gray')
-    ax.plot([0.1, 0.9], [0.2 / np.sqrt(3), 0.2 / np.sqrt(3)],
-            linewidth=3.0, color='gray')
+    ax.plot([0.1, 0.5], [0.2 / np.sqrt(3), 1.4 / np.sqrt(3)], linewidth=3.0, color="gray")
+    ax.plot([0.5, 0.9], [1.4 / np.sqrt(3), 0.2 / np.sqrt(3)], linewidth=3.0, color="gray")
+    ax.plot([0.1, 0.9], [0.2 / np.sqrt(3), 0.2 / np.sqrt(3)], linewidth=3.0, color="gray")
 
     # plot division lines
-    ax.plot([0.5, 0.5], [0.2 / np.sqrt(3), 0.6 / np.sqrt(3)],
-            linewidth=3.0, color='gray')
-    ax.plot([0.3, 0.5], [0.8 / np.sqrt(3), 0.6 / np.sqrt(3)],
-            linewidth=3.0, color='gray')
-    ax.plot([0.5, 0.7], [0.6 / np.sqrt(3), 0.8 / np.sqrt(3)],
-            linewidth=3.0, color='gray')
+    ax.plot([0.5, 0.5], [0.2 / np.sqrt(3), 0.6 / np.sqrt(3)], linewidth=3.0, color="gray")
+    ax.plot([0.3, 0.5], [0.8 / np.sqrt(3), 0.6 / np.sqrt(3)], linewidth=3.0, color="gray")
+    ax.plot([0.5, 0.7], [0.6 / np.sqrt(3), 0.8 / np.sqrt(3)], linewidth=3.0, color="gray")
 
     if filename:
-        plt.savefig(filename, bbox_inches='tight')
+        plt.savefig(filename, bbox_inches="tight")
+        logger.info("Figure {filename} saved to file")
 
     plt.show()
    
+
 
    
 
           
    
@@ -239,8 +226,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.visualization.posterior
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.lab.visualization.streaming — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
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+          
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+        
  • jmetal.lab.visualization.streaming
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -110,7 +107,7 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.lab.visualization.streaming
     import logging
-from typing import TypeVar, List
+from typing import List, TypeVar
 
 import matplotlib
 from matplotlib import pyplot as plt
@@ -118,9 +115,9 @@ 
    Source code for jmetal.lab.visualization.streaming
    from jmetal.lab.visualization.plotting import Plot
 
-LOGGER = logging.getLogger('jmetal')
+logger = logging.getLogger(__name__)
 
-S = TypeVar('S')
+S = TypeVar("S") 
 
 """
 .. module:: streaming
@@ -131,14 +128,17 @@ 
    Source code for jmetal.lab.visualization.streaming
    """
 
 
-
    [docs]class StreamingPlot:
-
-    def __init__(self,
-                 plot_title: str = 'Pareto front approximation',
-                 reference_front: List[S] = None,
-                 reference_point: list = None,
-                 axis_labels: list = None):
-        """
+
    
+[docs]
+class StreamingPlot:
+    def __init__(
+        self,
+        plot_title: str = "Pareto front approximation",
+        reference_front: List[S] = None,
+        reference_point: list = None,
+        axis_labels: list = None,
+    ):
+        """
         :param plot_title: Title of the graph.
         :param axis_labels: List of axis labels.
         :param reference_point: Reference point (e.g., [0.4, 1.2]).
@@ -155,13 +155,16 @@ 
    Source code for jmetal.lab.visualization.streaming
    self.dimension = None
 
         import warnings
+
         warnings.filterwarnings("ignore", ".*GUI is implemented.*")
 
         self.fig, self.ax = plt.subplots()
         self.sc = None
         self.axis = None
 
-
    [docs]    def plot(self, front):
+
    
+[docs]
+    def plot(self, front):
         # Get data
         points, dimension = Plot.get_points(front)
 
@@ -171,24 +174,33 @@ 
    Source code for jmetal.lab.visualization.streaming
    # If any reference point, plot
         if self.reference_point:
             for point in self.reference_point:
-                self.scp, = self.ax.plot(*[[p] for p in point], c='r', ls='None', marker='*', markersize=3)
+                (self.scp,) = self.ax.plot(*[[p] for p in point], c="r", ls="None", marker="*", markersize=3)
 
         # If any reference front, plot
         if self.reference_front:
             rpoints, _ = Plot.get_points(self.reference_front)
-            self.scf, = self.ax.plot(*[rpoints[column].tolist() for column in rpoints.columns.values],
-                                     c='k', ls='None', marker='*', markersize=1)
+            (self.scf,) = self.ax.plot(
+                *[rpoints[column].tolist() for column in rpoints.columns.values],
+                c="k",
+                ls="None",
+                marker="*",
+                markersize=1
+            )
 
         # Plot data
-        self.sc, = self.ax.plot(*[points[column].tolist() for column in points.columns.values],
-                                ls='None', marker='o', markersize=4)
+        (self.sc,) = self.ax.plot(
+            *[points[column].tolist() for column in points.columns.values], ls="None", marker="o", markersize=4
+        )
 
         # Show plot
         plt.show(block=False)
    
 
-
    [docs]    def update(self, front: List[S], reference_point: list = None) -> None:
+
+
    
+[docs]
+    def update(self, front: List[S], reference_point: list = None) -> None:
         if self.sc is None:
-            raise Exception('Figure is none')
+            raise Exception("Figure is none")
 
         points, dimension = Plot.get_points(front)
 
@@ -214,31 +226,38 @@ 
    Source code for jmetal.lab.visualization.streaming
    pause(0.01)
    
 
-
    [docs]    def create_layout(self, dimension: int) -> None:
-        self.fig.canvas.set_window_title(self.plot_title)
+
+
    
+[docs]
+    def create_layout(self, dimension: int) -> None:
+        logger.info("Creating figure layout")
+
+        self.fig.canvas.manager.set_window_title(self.plot_title)
         self.fig.suptitle(self.plot_title, fontsize=16)
 
         if dimension == 2:
             # Stylize axis
-            self.ax.spines['top'].set_visible(False)
-            self.ax.spines['right'].set_visible(False)
+            self.ax.spines["top"].set_visible(False)
+            self.ax.spines["right"].set_visible(False)
             self.ax.get_xaxis().tick_bottom()
             self.ax.get_yaxis().tick_left()
         elif dimension == 3:
             self.ax = Axes3D(self.fig)
-            self.ax.autoscale(enable=True, axis='both')
+            self.ax.autoscale(enable=True, axis="both")
         else:
-            raise Exception('Dimension must be either 2 or 3')
+            raise Exception("Dimension must be either 2 or 3")
 
         self.ax.set_autoscale_on(True)
         self.ax.autoscale_view(True, True, True)
 
         # Style options
-        self.ax.grid(color='#f0f0f5', linestyle='-', linewidth=0.5, alpha=0.5)
    
+        self.ax.grid(color="#f0f0f5", linestyle="-", linewidth=0.5, alpha=0.5)
    
+
    
+
 
 
 def pause(interval: float):
-    backend = plt.rcParams['backend']
+    backend = plt.rcParams["backend"]
 
     if backend in matplotlib.rcsetup.interactive_bk:
         figManager = matplotlib._pylab_helpers.Gcf.get_active()
@@ -264,8 +283,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.lab.visualization.streaming
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.operator.crossover — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.operator.crossover
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -114,7 +111,15 @@ 
    Source code for jmetal.operator.crossover
     from typing import List
 
 from jmetal.core.operator import Crossover
-from jmetal.core.solution import Solution, FloatSolution, BinarySolution, PermutationSolution
+from jmetal.core.solution import (
+    BinarySolution,
+    CompositeSolution,
+    FloatSolution,
+    IntegerSolution,
+    PermutationSolution,
+    Solution,
+)
+from jmetal.util.ckecking import Check
 
 """
 .. module:: crossover
@@ -125,38 +130,55 @@ 
    Source code for jmetal.operator.crossover
     """
 
 
-
    [docs]class NullCrossover(Crossover[Solution, Solution]):
-
+
    
+[docs]
+class NullCrossover(Crossover[Solution, Solution]):
     def __init__(self):
         super(NullCrossover, self).__init__(probability=0.0)
 
-
    [docs]    def execute(self, parents: List[Solution]) -> List[Solution]:
+
    
+[docs]
+    def execute(self, parents: List[Solution]) -> List[Solution]:
         if len(parents) != 2:
-            raise Exception('The number of parents is not two: {}'.format(len(parents)))
+            raise Exception("The number of parents is not two: {}".format(len(parents)))
 
         return parents
    
 
-
    [docs]    def get_number_of_parents(self) -> int:
+
+
    
+[docs]
+    def get_number_of_parents(self) -> int:
         return 2
    
 
-
    [docs]    def get_number_of_children(self) -> int:
+
+
    
+[docs]
+    def get_number_of_children(self) -> int:
         return 2
    
 
-
    [docs]    def get_name(self):
-        return 'Null crossover'
    
+
+
    
+[docs]
+    def get_name(self):
+        return "Null crossover"
    
+
    
 
 
-
    [docs]class PMXCrossover(Crossover[PermutationSolution, PermutationSolution]):
 
+
    
+[docs]
+class PMXCrossover(Crossover[PermutationSolution, PermutationSolution]):
     def __init__(self, probability: float):
         super(PMXCrossover, self).__init__(probability=probability)
 
-
    [docs]    def execute(self, parents: List[PermutationSolution]) -> List[PermutationSolution]:
+
    
+[docs]
+    def execute(self, parents: List[PermutationSolution]) -> List[PermutationSolution]:
         if len(parents) != 2:
-            raise Exception('The number of parents is not two: {}'.format(len(parents)))
+            raise Exception("The number of parents is not two: {}".format(len(parents)))
 
-        offspring = [copy.deepcopy(parents[0]), copy.deepcopy(parents[1])]
-        permutation_length = offspring[0].number_of_variables
+        offspring = copy.deepcopy(parents)
+        permutation_length = len(offspring[0].variables)
 
         rand = random.random()
         if rand <= self.probability:
@@ -188,81 +210,112 @@ 
    Source code for jmetal.operator.crossover
                                     swapped[i_son][i_chromosome] = map_[1 - i_son][map_index]
                 return s1, s2
 
-            swapped = _swap(parents[0].variables, parents[1].variables, cross_points)
+            swapped = _swap(offspring[0].variables, offspring[1].variables, cross_points)
             mapped = _map(swapped, cross_points)
 
             offspring[0].variables, offspring[1].variables = mapped
 
         return offspring
    
 
-
    [docs]    def get_number_of_parents(self) -> int:
+
+
    
+[docs]
+    def get_number_of_parents(self) -> int:
         return 2
    
 
-
    [docs]    def get_number_of_children(self) -> int:
+
+
    
+[docs]
+    def get_number_of_children(self) -> int:
         return 2
    
 
-
    [docs]    def get_name(self):
-        return 'Partially Matched crossover'
    
+
+
    
+[docs]
+    def get_name(self):
+        return "Partially Matched crossover"
    
+
    
 
 
-
    [docs]class CXCrossover(Crossover[PermutationSolution, PermutationSolution]):
 
+
    
+[docs]
+class CXCrossover(Crossover[PermutationSolution, PermutationSolution]):
     def __init__(self, probability: float):
         super(CXCrossover, self).__init__(probability=probability)
 
-
    [docs]    def execute(self, parents: List[PermutationSolution]) -> List[PermutationSolution]:
+
    
+[docs]
+    def execute(self, parents: List[PermutationSolution]) -> List[PermutationSolution]:
         if len(parents) != 2:
-            raise Exception('The number of parents is not two: {}'.format(len(parents)))
+            raise Exception("The number of parents is not two: {}".format(len(parents)))
 
-        offspring = [copy.deepcopy(parents[1]), copy.deepcopy(parents[0])]
+        offspring = copy.deepcopy(parents[::-1])
         rand = random.random()
 
         if rand <= self.probability:
-            for i in range(parents[0].number_of_variables):
-                idx = random.randint(0, len(parents[0].variables[i]) - 1)
-                curr_idx = idx
-                cycle = []
+            idx = random.randint(0, len(parents[0].variables) - 1)
+            curr_idx = idx
+            cycle = []
 
-                while True:
-                    cycle.append(curr_idx)
-                    curr_idx = parents[0].variables[i].index(parents[1].variables[i][curr_idx])
+            while True:
+                cycle.append(curr_idx)
+                curr_idx = parents[0].variables.index(parents[1].variables[curr_idx])
 
-                    if curr_idx == idx:
-                        break
+                if curr_idx == idx:
+                    break
 
-                for j in range(len(parents[0].variables[i])):
-                    if j in cycle:
-                        offspring[0].variables[i][j] = parents[0].variables[i][j]
-                        offspring[1].variables[i][j] = parents[0].variables[i][j]
+            for j in range(len(parents[0].variables)):
+                if j in cycle:
+                    offspring[0].variables[j] = parents[0].variables[j]
+                    offspring[1].variables[j] = parents[1].variables[j]
 
         return offspring
    
 
-
    [docs]    def get_number_of_parents(self) -> int:
+
+
    
+[docs]
+    def get_number_of_parents(self) -> int:
         return 2
    
 
-
    [docs]    def get_number_of_children(self) -> int:
+
+
    
+[docs]
+    def get_number_of_children(self) -> int:
         return 2
    
 
-
    [docs]    def get_name(self):
-        return 'Cycle crossover'
    
+
+
    
+[docs]
+    def get_name(self):
+        return "Cycle crossover"
    
+
    
 
 
-
    [docs]class SBXCrossover(Crossover[FloatSolution, FloatSolution]):
+
+
    
+[docs]
+class SBXCrossover(Crossover[FloatSolution, FloatSolution]):
     __EPS = 1.0e-14
 
     def __init__(self, probability: float, distribution_index: float = 20.0):
         super(SBXCrossover, self).__init__(probability=probability)
         self.distribution_index = distribution_index
+        if distribution_index < 0:
+            raise Exception("The distribution index is negative: " + str(distribution_index))
 
-
    [docs]    def execute(self, parents: List[FloatSolution]) -> List[FloatSolution]:
-        if len(parents) != 2:
-            raise Exception('The number of parents is not two: {}'.format(len(parents)))
+
    
+[docs]
+    def execute(self, parents: List[FloatSolution]) -> List[FloatSolution]:
+        Check.that(issubclass(type(parents[0]), FloatSolution), "Solution type invalid: " + str(type(parents[0])))
+        Check.that(issubclass(type(parents[1]), FloatSolution), "Solution type invalid")
+        Check.that(len(parents) == 2, "The number of parents is not two: {}".format(len(parents)))
 
-        offspring = [copy.deepcopy(parents[0]), copy.deepcopy(parents[1])]
+        offspring = copy.deepcopy(parents)
         rand = random.random()
 
         if rand <= self.probability:
-            for i in range(parents[0].number_of_variables):
+            for i in range(len(parents[0].variables)):
                 value_x1, value_x2 = parents[0].variables[i], parents[1].variables[i]
 
                 if random.random() <= 0.5:
@@ -317,26 +370,137 @@ 
    Source code for jmetal.operator.crossover
                         offspring[1].variables[i] = value_x2
         return offspring
    
 
-
    [docs]    def get_number_of_parents(self) -> int:
+
+
    
+[docs]
+    def get_number_of_parents(self) -> int:
+        return 2
    
+
+
+
    
+[docs]
+    def get_number_of_children(self) -> int:
+        return 2
    
+
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "SBX crossover"
    
+
    
+
+
+
+
    
+[docs]
+class IntegerSBXCrossover(Crossover[IntegerSolution, IntegerSolution]):
+    __EPS = 1.0e-14
+
+    def __init__(self, probability: float, distribution_index: float = 20.0):
+        super(IntegerSBXCrossover, self).__init__(probability=probability)
+        self.distribution_index = distribution_index
+
+
    
+[docs]
+    def execute(self, parents: List[IntegerSolution]) -> List[IntegerSolution]:
+        Check.that(issubclass(type(parents[0]), IntegerSolution), "Solution type invalid")
+        Check.that(issubclass(type(parents[1]), IntegerSolution), "Solution type invalid")
+        Check.that(len(parents) == 2, "The number of parents is not two: {}".format(len(parents)))
+
+        offspring = copy.deepcopy(parents)
+        rand = random.random()
+
+        if rand <= self.probability:
+            for i in range(len(parents[0].variables)):
+                value_x1, value_x2 = parents[0].variables[i], parents[1].variables[i]
+
+                if random.random() <= 0.5:
+                    if abs(value_x1 - value_x2) > self.__EPS:
+                        if value_x1 < value_x2:
+                            y1, y2 = value_x1, value_x2
+                        else:
+                            y1, y2 = value_x2, value_x1
+
+                        lower_bound, upper_bound = parents[0].lower_bound[i], parents[1].upper_bound[i]
+
+                        beta = 1.0 + (2.0 * (y1 - lower_bound) / (y2 - y1))
+                        alpha = 2.0 - pow(beta, -(self.distribution_index + 1.0))
+
+                        rand = random.random()
+                        if rand <= (1.0 / alpha):
+                            betaq = pow(rand * alpha, (1.0 / (self.distribution_index + 1.0)))
+                        else:
+                            betaq = pow(1.0 / (2.0 - rand * alpha), 1.0 / (self.distribution_index + 1.0))
+
+                        c1 = 0.5 * (y1 + y2 - betaq * (y2 - y1))
+                        beta = 1.0 + (2.0 * (upper_bound - y2) / (y2 - y1))
+                        alpha = 2.0 - pow(beta, -(self.distribution_index + 1.0))
+
+                        if rand <= (1.0 / alpha):
+                            betaq = pow((rand * alpha), (1.0 / (self.distribution_index + 1.0)))
+                        else:
+                            betaq = pow(1.0 / (2.0 - rand * alpha), 1.0 / (self.distribution_index + 1.0))
+
+                        c2 = 0.5 * (y1 + y2 + betaq * (y2 - y1))
+
+                        if c1 < lower_bound:
+                            c1 = lower_bound
+                        if c2 < lower_bound:
+                            c2 = lower_bound
+                        if c1 > upper_bound:
+                            c1 = upper_bound
+                        if c2 > upper_bound:
+                            c2 = upper_bound
+
+                        if random.random() <= 0.5:
+                            offspring[0].variables[i] = int(c2)
+                            offspring[1].variables[i] = int(c1)
+                        else:
+                            offspring[0].variables[i] = int(c1)
+                            offspring[1].variables[i] = int(c2)
+                    else:
+                        offspring[0].variables[i] = value_x1
+                        offspring[1].variables[i] = value_x2
+                else:
+                    offspring[0].variables[i] = value_x1
+                    offspring[1].variables[i] = value_x2
+        return offspring
    
+
+
+
    
+[docs]
+    def get_number_of_parents(self) -> int:
         return 2
    
 
-
    [docs]    def get_number_of_children(self) -> int:
+
+
    
+[docs]
+    def get_number_of_children(self) -> int:
         return 2
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'SBX crossover'
    
 
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Integer SBX crossover"
    
+
    
 
-
    [docs]class SPXCrossover(Crossover[BinarySolution, BinarySolution]):
 
+
+
    
+[docs]
+class SPXCrossover(Crossover[BinarySolution, BinarySolution]):
     def __init__(self, probability: float):
         super(SPXCrossover, self).__init__(probability=probability)
 
-
    [docs]    def execute(self, parents: List[BinarySolution]) -> List[BinarySolution]:
-        if len(parents) != 2:
-            raise Exception('The number of parents is not two: {}'.format(len(parents)))
+
    
+[docs]
+    def execute(self, parents: List[BinarySolution]) -> List[BinarySolution]:
+        Check.that(type(parents[0]) is BinarySolution, "Solution type invalid")
+        Check.that(type(parents[1]) is BinarySolution, "Solution type invalid")
+        Check.that(len(parents) == 2, "The number of parents is not two: {}".format(len(parents)))
 
-        offspring = [copy.deepcopy(parents[0]), copy.deepcopy(parents[1])]
+        offspring = copy.deepcopy(parents)
         rand = random.random()
 
         if rand <= self.probability:
@@ -370,30 +534,43 @@ 
    Source code for jmetal.operator.crossover
                 offspring[1].variables[variable_to_cut] = bitset2
 
             # 6. Apply the crossover to the other variables
-            for i in range(variable_to_cut + 1, parents[0].number_of_variables):
+            for i in range(variable_to_cut + 1, len(parents[0].variables)):
                 offspring[0].variables[i] = copy.deepcopy(parents[1].variables[i])
                 offspring[1].variables[i] = copy.deepcopy(parents[0].variables[i])
 
         return offspring
    
 
-
    [docs]    def get_number_of_parents(self) -> int:
+
+
    
+[docs]
+    def get_number_of_parents(self) -> int:
         return 2
    
 
-
    [docs]    def get_number_of_children(self) -> int:
+
+
    
+[docs]
+    def get_number_of_children(self) -> int:
         return 2
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Single point crossover'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Single point crossover"
    
+
    
+
 
 
-
    [docs]class DifferentialEvolutionCrossover(Crossover[FloatSolution, FloatSolution]):
-    """ This operator receives two parameters: the current individual and an array of three parent individuals. The
+
    
+[docs]
+class DifferentialEvolutionCrossover(Crossover[FloatSolution, FloatSolution]):
+    """This operator receives two parameters: the current individual and an array of three parent individuals. The
     best and rand variants depends on the third parent, according whether it represents the current of the "best"
     individual or a random_search one. The implementation of both variants are the same, due to that the parent selection is
     external to the crossover operator.
     """
 
-    def __init__(self, CR: float, F: float, K: float):
+    def __init__(self, CR: float, F: float, K: float = 0.5):
         super(DifferentialEvolutionCrossover, self).__init__(probability=1.0)
         self.CR = CR
         self.F = F
@@ -401,15 +578,16 @@ 
    Source code for jmetal.operator.crossover
     
         self.current_individual: FloatSolution = None
 
-
    [docs]    def execute(self, parents: List[FloatSolution]) -> List[FloatSolution]:
-        """ Execute the differential evolution crossover ('best/1/bin' variant in jMetal).
-        """
+
    
+[docs]
+    def execute(self, parents: List[FloatSolution]) -> List[FloatSolution]:
+        """Execute the differential evolution crossover ('best/1/bin' variant in jMetal)."""
         if len(parents) != self.get_number_of_parents():
-            raise Exception('The number of parents is not {}: {}'.format(self.get_number_of_parents(), len(parents)))
+            raise Exception("The number of parents is not {}: {}".format(self.get_number_of_parents(), len(parents)))
 
         child = copy.deepcopy(self.current_individual)
 
-        number_of_variables = parents[0].number_of_variables
+        number_of_variables = len(parents[0].variables)
         rand = random.randint(0, number_of_variables - 1)
 
         for i in range(number_of_variables):
@@ -427,14 +605,81 @@ 
    Source code for jmetal.operator.crossover
     
         return [child]
    
 
-
    [docs]    def get_number_of_parents(self) -> int:
+
+
    
+[docs]
+    def get_number_of_parents(self) -> int:
         return 3
    
 
-
    [docs]    def get_number_of_children(self) -> int:
+
+
    
+[docs]
+    def get_number_of_children(self) -> int:
         return 1
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Differential Evolution crossover'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Differential Evolution crossover"
    
+
    
+
+
+
+
    
+[docs]
+class CompositeCrossover(Crossover[CompositeSolution, CompositeSolution]):
+    __EPS = 1.0e-14
+
+    def __init__(self, crossover_operator_list: [Crossover]):
+        super(CompositeCrossover, self).__init__(probability=1.0)
+
+        Check.is_not_none(crossover_operator_list)
+        Check.collection_is_not_empty(crossover_operator_list)
+
+        self.crossover_operators_list = []
+        for operator in crossover_operator_list:
+            Check.that(issubclass(operator.__class__, Crossover), "Object is not a subclass of Crossover")
+            self.crossover_operators_list.append(operator)
+
+
    
+[docs]
+    def execute(self, solutions: List[CompositeSolution]) -> List[CompositeSolution]:
+        Check.is_not_none(solutions)
+        Check.that(len(solutions) == 2, "The number of parents is not two: " + str(len(solutions)))
+
+        offspring1 = []
+        offspring2 = []
+
+        number_of_solutions_in_composite_solution = len(solutions[0].variables)
+
+        for i in range(number_of_solutions_in_composite_solution):
+            parents = [solutions[0].variables[i], solutions[1].variables[i]]
+            children = self.crossover_operators_list[i].execute(parents)
+            offspring1.append(children[0])
+            offspring2.append(children[1])
+
+        return [CompositeSolution(offspring1), CompositeSolution(offspring2)]
    
+
+
+
    
+[docs]
+    def get_number_of_parents(self) -> int:
+        return 2
    
+
+
+
    
+[docs]
+    def get_number_of_children(self) -> int:
+        return 2
    
+
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Composite crossover"
    
+
    
+
 
    
 
           
    
@@ -451,8 +696,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
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+          
  • Module code »
    • 
+        
  • jmetal.operator.crossover
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.operator.mutation — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.operator.mutation
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -112,7 +109,15 @@ 
    Source code for jmetal.operator.mutation
     import random
 
 from jmetal.core.operator import Mutation
-from jmetal.core.solution import BinarySolution, Solution, FloatSolution, IntegerSolution, PermutationSolution
+from jmetal.core.solution import (
+    BinarySolution,
+    CompositeSolution,
+    FloatSolution,
+    IntegerSolution,
+    PermutationSolution,
+    Solution,
+)
+from jmetal.util.ckecking import Check
 
 """
 .. module:: mutation
@@ -123,25 +128,38 @@ 
    Source code for jmetal.operator.mutation
     """
 
 
-
    [docs]class NullMutation(Mutation[Solution]):
-
+
    
+[docs]
+class NullMutation(Mutation[Solution]):
     def __init__(self):
         super(NullMutation, self).__init__(probability=0)
 
-
    [docs]    def execute(self, solution: Solution) -> Solution:
+
    
+[docs]
+    def execute(self, solution: Solution) -> Solution:
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Null mutation'
    
+
+
    
+[docs]
+    def get_name(self):
+        return "Null mutation"
    
+
    
 
 
-
    [docs]class BitFlipMutation(Mutation[BinarySolution]):
 
+
    
+[docs]
+class BitFlipMutation(Mutation[BinarySolution]):
     def __init__(self, probability: float):
         super(BitFlipMutation, self).__init__(probability=probability)
 
-
    [docs]    def execute(self, solution: BinarySolution) -> BinarySolution:
-        for i in range(solution.number_of_variables):
+
    
+[docs]
+    def execute(self, solution: BinarySolution) -> BinarySolution:
+        Check.that(type(solution) is BinarySolution, "Solution type invalid")
+
+        for i in range(len(solution.variables)):
             for j in range(len(solution.variables[i])):
                 rand = random.random()
                 if rand <= self.probability:
@@ -149,18 +167,27 @@ 
    Source code for jmetal.operator.mutation
     
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'BitFlip mutation'
    
 
+
    
+[docs]
+    def get_name(self):
+        return "BitFlip mutation"
    
+
    
 
-
    [docs]class PolynomialMutation(Mutation[FloatSolution]):
 
+
+
    
+[docs]
+class PolynomialMutation(Mutation[FloatSolution]):
     def __init__(self, probability: float, distribution_index: float = 0.20):
         super(PolynomialMutation, self).__init__(probability=probability)
         self.distribution_index = distribution_index
 
-
    [docs]    def execute(self, solution: FloatSolution) -> FloatSolution:
-        for i in range(solution.number_of_variables):
+
    
+[docs]
+    def execute(self, solution: FloatSolution) -> FloatSolution:
+        Check.that(issubclass(type(solution), FloatSolution), "Solution type invalid")
+        for i in range(len(solution.variables)):
             rand = random.random()
 
             if rand <= self.probability:
@@ -193,18 +220,28 @@ 
    Source code for jmetal.operator.mutation
     
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Polynomial mutation'
    
+
+
    
+[docs]
+    def get_name(self):
+        return "Polynomial mutation"
    
+
    
 
 
-
    [docs]class IntegerPolynomialMutation(Mutation[IntegerSolution]):
 
+
    
+[docs]
+class IntegerPolynomialMutation(Mutation[IntegerSolution]):
     def __init__(self, probability: float, distribution_index: float = 0.20):
         super(IntegerPolynomialMutation, self).__init__(probability=probability)
         self.distribution_index = distribution_index
 
-
    [docs]    def execute(self, solution: IntegerSolution) -> IntegerSolution:
-        for i in range(solution.number_of_variables):
+
    
+[docs]
+    def execute(self, solution: IntegerSolution) -> IntegerSolution:
+        Check.that(issubclass(type(solution), IntegerSolution), "Solution type invalid")
+
+        for i in range(len(solution.variables)):
             if random.random() <= self.probability:
                 y = solution.variables[i]
                 yl, yu = solution.lower_bound[i], solution.upper_bound[i]
@@ -219,11 +256,11 @@ 
    Source code for jmetal.operator.mutation
                         if rnd <= 0.5:
                         xy = 1.0 - delta1
                         val = 2.0 * rnd + (1.0 - 2.0 * rnd) * (xy ** (self.distribution_index + 1.0))
-                        deltaq = val ** mut_pow - 1.0
+                        deltaq = val**mut_pow - 1.0
                     else:
                         xy = 1.0 - delta2
                         val = 2.0 * (1.0 - rnd) + 2.0 * (rnd - 0.5) * (xy ** (self.distribution_index + 1.0))
-                        deltaq = 1.0 - val ** mut_pow
+                        deltaq = 1.0 - val**mut_pow
 
                     y += deltaq * (yu - yl)
                     if y < solution.lower_bound[i]:
@@ -234,35 +271,56 @@ 
    Source code for jmetal.operator.mutation
                     solution.variables[i] = int(round(y))
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Polynomial mutation (Integer)'
    
+
+
    
+[docs]
+    def get_name(self):
+        return "Polynomial mutation (Integer)"
    
+
    
 
 
-
    [docs]class SimpleRandomMutation(Mutation[FloatSolution]):
 
+
    
+[docs]
+class SimpleRandomMutation(Mutation[FloatSolution]):
     def __init__(self, probability: float):
         super(SimpleRandomMutation, self).__init__(probability=probability)
 
-
    [docs]    def execute(self, solution: FloatSolution) -> FloatSolution:
-        for i in range(solution.number_of_variables):
+
    
+[docs]
+    def execute(self, solution: FloatSolution) -> FloatSolution:
+        Check.that(type(solution) is FloatSolution, "Solution type invalid")
+
+        for i in range(len(solution.variables)):
             rand = random.random()
             if rand <= self.probability:
-                solution.variables[i] = solution.lower_bound[i] + \
-                                        (solution.upper_bound[i] - solution.lower_bound[i]) * random.random()
+                solution.variables[i] = (
+                    solution.lower_bound[i] + (solution.upper_bound[i] - solution.lower_bound[i]) * random.random()
+                )
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Simple random_search mutation'
    
+
+
    
+[docs]
+    def get_name(self):
+        return "Simple random_search mutation"
    
+
    
 
 
-
    [docs]class UniformMutation(Mutation[FloatSolution]):
 
+
    
+[docs]
+class UniformMutation(Mutation[FloatSolution]):
     def __init__(self, probability: float, perturbation: float = 0.5):
         super(UniformMutation, self).__init__(probability=probability)
         self.perturbation = perturbation
 
-
    [docs]    def execute(self, solution: FloatSolution) -> FloatSolution:
-        for i in range(solution.number_of_variables):
+
    
+[docs]
+    def execute(self, solution: FloatSolution) -> FloatSolution:
+        Check.that(type(solution) is FloatSolution, "Solution type invalid")
+
+        for i in range(len(solution.variables)):
             rand = random.random()
 
             if rand <= self.probability:
@@ -278,20 +336,30 @@ 
    Source code for jmetal.operator.mutation
     
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Uniform mutation'
    
 
+
    
+[docs]
+    def get_name(self):
+        return "Uniform mutation"
    
+
    
 
-
    [docs]class NonUniformMutation(Mutation[FloatSolution]):
 
+
+
    
+[docs]
+class NonUniformMutation(Mutation[FloatSolution]):
     def __init__(self, probability: float, perturbation: float = 0.5, max_iterations: int = 0.5):
         super(NonUniformMutation, self).__init__(probability=probability)
         self.perturbation = perturbation
         self.max_iterations = max_iterations
         self.current_iteration = 0
 
-
    [docs]    def execute(self, solution: FloatSolution) -> FloatSolution:
-        for i in range(solution.number_of_variables):
+
    
+[docs]
+    def execute(self, solution: FloatSolution) -> FloatSolution:
+        Check.that(type(solution) is FloatSolution, "Solution type invalid")
+
+        for i in range(len(solution.variables)):
             if random.random() <= self.probability:
                 rand = random.random()
 
@@ -311,58 +379,123 @@ 
    Source code for jmetal.operator.mutation
     
         return solution
    
 
-
    [docs]    def set_current_iteration(self, current_iteration: int):
+
+
    
+[docs]
+    def set_current_iteration(self, current_iteration: int):
         self.current_iteration = current_iteration
    
 
+
     def __delta(self, y: float, b_mutation_parameter: float):
-        return (y * (1.0 - pow(random.random(),
-                               pow((1.0 - 1.0 * self.current_iteration / self.max_iterations), b_mutation_parameter))))
+        return y * (
+            1.0
+            - pow(
+                random.random(), pow((1.0 - 1.0 * self.current_iteration / self.max_iterations), b_mutation_parameter)
+            )
+        )
+
+
    
+[docs]
+    def get_name(self):
+        return "Uniform mutation"
    
+
    
 
-
    [docs]    def get_name(self):
-        return 'Uniform mutation'
    
 
 
-
    [docs]class PermutationSwapMutation(Mutation[PermutationSolution]):
+
    
+[docs]
+class PermutationSwapMutation(Mutation[PermutationSolution]):
+
    
+[docs]
+    def execute(self, solution: PermutationSolution) -> PermutationSolution:
+        Check.that(type(solution) is PermutationSolution, "Solution type invalid")
 
-
    [docs]    def execute(self, solution: PermutationSolution) -> PermutationSolution:
         rand = random.random()
 
         if rand <= self.probability:
-            pos_one, pos_two = random.sample(range(solution.number_of_variables - 1), 2)
-            solution.variables[pos_one], solution.variables[pos_two] = \
-                solution.variables[pos_two], solution.variables[pos_one]
+            pos_one, pos_two = random.sample(range(len(solution.variables)), 2)
+            solution.variables[pos_one], solution.variables[pos_two] = (
+                solution.variables[pos_two],
+                solution.variables[pos_one],
+            )
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Permutation Swap mutation'
    
 
+
    
+[docs]
+    def get_name(self):
+        return "Permutation Swap mutation"
    
+
    
 
-
    [docs]class ScrambleMutation(Mutation[PermutationSolution]):
 
-
    [docs]    def execute(self, solution: PermutationSolution) -> PermutationSolution:
-        for i in range(solution.number_of_variables):
-            rand = random.random()
 
-            if rand <= self.probability:
-                point1 = random.randint(0, len(solution.variables[i]))
-                point2 = random.randint(0, len(solution.variables[i]) - 1)
+
    
+[docs]
+class CompositeMutation(Mutation[Solution]):
+    def __init__(self, mutation_operator_list: [Mutation]):
+        super(CompositeMutation, self).__init__(probability=1.0)
 
-                if point2 >= point1:
-                    point2 += 1
-                else:
-                    point1, point2 = point2, point1
+        Check.is_not_none(mutation_operator_list)
+        Check.collection_is_not_empty(mutation_operator_list)
+
+        self.mutation_operators_list = []
+        for operator in mutation_operator_list:
+            Check.that(issubclass(operator.__class__, Mutation), "Object is not a subclass of Mutation")
+            self.mutation_operators_list.append(operator)
+
+
    
+[docs]
+    def execute(self, solution: CompositeSolution) -> CompositeSolution:
+        Check.is_not_none(solution)
+
+        mutated_solution_components = []
+        for i in range(len(solution.variables)):
+            mutated_solution_components.append(self.mutation_operators_list[i].execute(solution.variables[i]))
+
+        return CompositeSolution(mutated_solution_components)
    
+
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Composite mutation operator"
    
+
    
 
-                if point2 - point1 >= 20:
-                    point2 = point1 + 20
 
-                values = solution.variables[i][point1:point2]
-                solution.variables[i][point1:point2] = random.sample(values, len(values))
+
+
    
+[docs]
+class ScrambleMutation(Mutation[PermutationSolution]):
+
    
+[docs]
+    def execute(self, solution: PermutationSolution) -> PermutationSolution:
+        rand = random.random()
+
+        if rand <= self.probability:
+            point1 = random.randint(0, len(solution.variables))
+            point2 = random.randint(0, len(solution.variables) - 1)
+
+            if point2 >= point1:
+                point2 += 1
+            else:
+                point1, point2 = point2, point1
+
+            if point2 - point1 >= 20:
+                point2 = point1 + 20
+
+            values = solution.variables[point1:point2]
+            solution.variables[point1:point2] = random.sample(values, len(values))
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Scramble'
    
+
+
    
+[docs]
+    def get_name(self):
+        return "Scramble"
    
+
    
+
 
    
 
           
    
@@ -379,8 +512,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.operator.mutation
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.operator.selection — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.operator.selection
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -115,11 +112,12 @@ 
    Source code for jmetal.operator.selection
     import numpy as np
 
 from jmetal.core.operator import Selection
+from jmetal.core.solution import Solution
+from jmetal.util.comparator import Comparator, DominanceComparator
 from jmetal.util.density_estimator import CrowdingDistance
 from jmetal.util.ranking import FastNonDominatedRanking
-from jmetal.util.comparator import Comparator, DominanceComparator
 
-S = TypeVar('S')
+S = TypeVar("S", bound=Solution)
 
 """
 .. module:: selection
@@ -130,18 +128,21 @@ 
    Source code for jmetal.operator.selection
     """
 
 
-
    [docs]class RouletteWheelSelection(Selection[List[S], S]):
-    """Performs roulette wheel selection.
-    """
+
    
+[docs]
+class RouletteWheelSelection(Selection[List[S], S]):
+    """Performs roulette wheel selection."""
 
     def __init__(self):
         super(RouletteWheelSelection).__init__()
 
-
    [docs]    def execute(self, front: List[S]) -> S:
+
    
+[docs]
+    def execute(self, front: List[S]) -> S:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         elif len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
 
         maximum = sum([solution.objectives[0] for solution in front])
         rand = random.uniform(0.0, maximum)
@@ -155,21 +156,29 @@ 
    Source code for jmetal.operator.selection
     
         return None
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Roulette wheel selection'
    
 
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Roulette wheel selection"
    
+
    
 
-
    [docs]class BinaryTournamentSelection(Selection[List[S], S]):
 
+
+
    
+[docs]
+class BinaryTournamentSelection(Selection[List[S], S]):
     def __init__(self, comparator: Comparator = DominanceComparator()):
         super(BinaryTournamentSelection, self).__init__()
         self.comparator = comparator
 
-
    [docs]    def execute(self, front: List[S]) -> S:
+
    
+[docs]
+    def execute(self, front: List[S]) -> S:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         elif len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
 
         if len(front) == 1:
             result = front[0]
@@ -190,20 +199,28 @@ 
    Source code for jmetal.operator.selection
     
         return result
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Binary tournament selection'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Binary tournament selection"
    
+
    
 
 
-
    [docs]class BestSolutionSelection(Selection[List[S], S]):
 
+
    
+[docs]
+class BestSolutionSelection(Selection[List[S], S]):
     def __init__(self):
         super(BestSolutionSelection, self).__init__()
 
-
    [docs]    def execute(self, front: List[S]) -> S:
+
    
+[docs]
+    def execute(self, front: List[S]) -> S:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         elif len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
 
         result = front[0]
 
@@ -213,47 +230,63 @@ 
    Source code for jmetal.operator.selection
     
         return result
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Best solution selection'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Best solution selection"
    
+
    
 
 
-
    [docs]class NaryRandomSolutionSelection(Selection[List[S], S]):
 
+
    
+[docs]
+class NaryRandomSolutionSelection(Selection[List[S], S]):
     def __init__(self, number_of_solutions_to_be_returned: int = 1):
         super(NaryRandomSolutionSelection, self).__init__()
         if number_of_solutions_to_be_returned < 0:
-            raise Exception('The number of solutions to be returned must be positive integer')
+            raise Exception("The number of solutions to be returned must be positive integer")
 
         self.number_of_solutions_to_be_returned = number_of_solutions_to_be_returned
 
-
    [docs]    def execute(self, front: List[S]) -> S:
+
    
+[docs]
+    def execute(self, front: List[S]) -> S:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         if len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
         if len(front) < self.number_of_solutions_to_be_returned:
-            raise Exception('The front contains less elements than required')
+            raise Exception("The front contains less elements than required")
 
         # random_search sampling without replacement
         return random.sample(front, self.number_of_solutions_to_be_returned)
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Nary random_search solution selection'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Nary random_search solution selection"
    
+
    
 
 
-
    [docs]class DifferentialEvolutionSelection(Selection[List[S], List[S]]):
 
+
    
+[docs]
+class DifferentialEvolutionSelection(Selection[List[S], List[S]]):
     def __init__(self):
         super(DifferentialEvolutionSelection, self).__init__()
         self.index_to_exclude = None
 
-
    [docs]    def execute(self, front: List[S]) -> List[S]:
+
    
+[docs]
+    def execute(self, front: List[S]) -> List[S]:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         elif len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
         elif len(front) < 4:
-            raise Exception('The front has less than four solutions: ' + str(len(front)))
+            raise Exception("The front has less than four solutions: " + str(len(front)))
 
         selected_indexes = random.sample(range(len(front)), 3)
         while self.index_to_exclude in selected_indexes:
@@ -261,42 +294,61 @@ 
    Source code for jmetal.operator.selection
     
         return [front[i] for i in selected_indexes]
    
 
-
    [docs]    def set_index_to_exclude(self, index: int):
+
+
    
+[docs]
+    def set_index_to_exclude(self, index: int):
         self.index_to_exclude = index
    
 
-
    [docs]    def get_name(self) -> str:
-        return "Differential evolution selection"
    
 
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Differential evolution selection"
    
+
    
 
-
    [docs]class RandomSolutionSelection(Selection[List[S], S]):
 
+
+
    
+[docs]
+class RandomSolutionSelection(Selection[List[S], S]):
     def __init__(self):
         super(RandomSolutionSelection, self).__init__()
 
-
    [docs]    def execute(self, front: List[S]) -> S:
+
    
+[docs]
+    def execute(self, front: List[S]) -> S:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         elif len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
 
         return random.choice(front)
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Random solution selection'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Random solution selection"
    
+
    
 
 
-
    [docs]class RankingAndCrowdingDistanceSelection(Selection[List[S], List[S]]):
 
+
    
+[docs]
+class RankingAndCrowdingDistanceSelection(Selection[List[S], List[S]]):
     def __init__(self, max_population_size: int, dominance_comparator: Comparator = DominanceComparator()):
         super(RankingAndCrowdingDistanceSelection, self).__init__()
         self.max_population_size = max_population_size
         self.dominance_comparator = dominance_comparator
 
-
    [docs]    def execute(self, front: List[S]) -> List[S]:
+
    
+[docs]
+    def execute(self, front: List[S]) -> List[S]:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         elif len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
 
         ranking = FastNonDominatedRanking(self.dominance_comparator)
         crowding_distance = CrowdingDistance()
@@ -312,27 +364,35 @@ 
    Source code for jmetal.operator.selection
                 else:
                 subfront = ranking.get_subfront(ranking_index)
                 crowding_distance.compute_density_estimator(subfront)
-                sorted_subfront = sorted(subfront, key=lambda x: x.attributes['crowding_distance'], reverse=True)
+                sorted_subfront = sorted(subfront, key=lambda x: x.attributes["crowding_distance"], reverse=True)
                 for i in range((self.max_population_size - len(new_solution_list))):
                     new_solution_list.append(sorted_subfront[i])
 
         return new_solution_list
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Ranking and crowding distance selection'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Ranking and crowding distance selection"
    
+
    
 
 
-
    [docs]class RankingAndFitnessSelection(Selection[List[S], List[S]]):
 
-    def __init__(self,
-                 max_population_size: int, reference_point: S,
-                 dominance_comparator: Comparator = DominanceComparator()):
+
    
+[docs]
+class RankingAndFitnessSelection(Selection[List[S], List[S]]):
+    def __init__(
+        self, max_population_size: int, reference_point: S, dominance_comparator: Comparator = DominanceComparator()
+    ):
         super(RankingAndFitnessSelection, self).__init__()
         self.max_population_size = max_population_size
         self.dominance_comparator = dominance_comparator
         self.reference_point = reference_point
 
-
    [docs]    def hypesub(self, l, A, actDim, bounds, pvec, alpha, k):
+
    
+[docs]
+    def hypesub(self, l, A, actDim, bounds, pvec, alpha, k):
         h = [0 for _ in range(l)]
         Adim = [a[actDim - 1] for a in A]
         indices_sort = sorted(range(len(Adim)), key=Adim.__getitem__)
@@ -353,12 +413,17 @@ 
    Source code for jmetal.operator.selection
                             h[p] = h[p] + extrusion * alpha[i - 1]
             else:
                 if extrusion > 0:
-                    h = [h[j] + extrusion * self.hypesub(l, S[0:i], actDim - 1, bounds, pvec[0:i], alpha, k)[j] for j in
-                         range(l)]
+                    h = [
+                        h[j] + extrusion * self.hypesub(l, S[0:i], actDim - 1, bounds, pvec[0:i], alpha, k)[j]
+                        for j in range(l)
+                    ]
 
         return h
    
 
-
    [docs]    def compute_hypervol_fitness_values(self, population: List[S], reference_point: S, k: int):
+
+
    
+[docs]
+    def compute_hypervol_fitness_values(self, population: List[S], reference_point: S, k: int):
         points = [ind.objectives for ind in population]
         bounds = reference_point.objectives
         population_size = len(points)
@@ -376,15 +441,18 @@ 
    Source code for jmetal.operator.selection
             f = self.hypesub(population_size, points, actDim, bounds, pvec, alpha, k)
 
         for i in range(len(population)):
-            population[i].attributes['fitness'] = f[i]
+            population[i].attributes["fitness"] = f[i]
 
         return population
    
 
-
    [docs]    def execute(self, front: List[S]) -> List[S]:
+
+
    
+[docs]
+    def execute(self, front: List[S]) -> List[S]:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         elif len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
 
         ranking = FastNonDominatedRanking(self.dominance_comparator)
         ranking.compute_ranking(front)
@@ -402,29 +470,37 @@ 
    Source code for jmetal.operator.selection
                     parameter_K = len(subfront) - (self.max_population_size - len(new_solution_list))
                 while parameter_K > 0:
                     subfront = self.compute_hypervol_fitness_values(subfront, self.reference_point, parameter_K)
-                    subfront = sorted(subfront, key=lambda x: x.attributes['fitness'], reverse=True)
+                    subfront = sorted(subfront, key=lambda x: x.attributes["fitness"], reverse=True)
                     subfront = subfront[:-1]
                     parameter_K = parameter_K - 1
                 new_solution_list = new_solution_list + subfront
         return new_solution_list
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Ranking and fitness selection'
    
+
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Ranking and fitness selection"
    
+
    
 
 
-
    [docs]class BinaryTournament2Selection(Selection[List[S], S]):
 
+
    
+[docs]
+class BinaryTournament2Selection(Selection[List[S], S]):
     def __init__(self, comparator_list: List[Comparator]):
         super(BinaryTournament2Selection, self).__init__()
         self.comparator_list = comparator_list
 
-
    [docs]    def execute(self, front: List[S]) -> S:
+
    
+[docs]
+    def execute(self, front: List[S]) -> S:
         if front is None:
-            raise Exception('The front is null')
+            raise Exception("The front is null")
         elif len(front) == 0:
-            raise Exception('The front is empty')
+            raise Exception("The front is empty")
         elif not self.comparator_list:
-            raise Exception('The comparators\' list is empty')
+            raise Exception("The comparators' list is empty")
 
         winner = None
 
@@ -442,6 +518,7 @@ 
    Source code for jmetal.operator.selection
     
         return winner
    
 
+
     def __winner(self, front: List[S], comparator: Comparator):
         # Sampling without replacement
         i, j = random.sample(range(0, len(front)), 2)
@@ -460,8 +537,12 @@ 
    Source code for jmetal.operator.selection
     
         return result
 
-
    [docs]    def get_name(self) -> str:
-        return 'Binary tournament selection (experimental)'
    
+
    
+[docs]
+    def get_name(self) -> str:
+        return "Binary tournament selection (experimental)"
    
+
    
+
 
    
 
           
    
@@ -478,8 +559,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.operator.selection
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.multiobjective.constrained — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.constrained
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -109,7 +106,7 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.constrained
    -from math import pi, cos, atan
+from math import atan, cos, pi
 
 from jmetal.core.problem import FloatProblem
 from jmetal.core.solution import FloatSolution
@@ -123,22 +120,36 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    """
 
 
-
    [docs]class Srinivas(FloatProblem):
-    """ Class representing problem Srinivas. """
+
    
+[docs]
+class Srinivas(FloatProblem):
+    """Class representing problem Srinivas."""
 
     def __init__(self):
         super(Srinivas, self).__init__()
-        self.number_of_variables = 2
-        self.number_of_objectives = 2
-        self.number_of_constraints = 2
+        number_of_variables = 2
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
+
+        self.lower_bound = [-20.0 for _ in range(number_of_variables)]
+        self.upper_bound = [20.0 for _ in range(number_of_variables)]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
 
-        self.lower_bound = [-20.0 for _ in range(self.number_of_variables)]
-        self.upper_bound = [20.0 for _ in range(self.number_of_variables)]
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 2
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x1 = solution.variables[0]
         x2 = solution.variables[1]
 
@@ -149,6 +160,7 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    return solution
    
 
+
     def __evaluate_constraints(self, solution: FloatSolution) -> None:
         x1 = solution.variables[0]
         x2 = solution.variables[1]
@@ -156,27 +168,44 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    solution.constraints[0] = 1.0 - (x1 * x1 + x2 * x2) / 225.0
         solution.constraints[1] = (3.0 * x2 - x1) / 10.0 - 1.0
 
-
    [docs]    def get_name(self):
-        return 'Srinivas'
    
+
    
+[docs]
+    def name(self):
+        return "Srinivas"
    
+
    
 
 
-
    [docs]class Tanaka(FloatProblem):
-    """ Class representing problem Tanaka. """
+
+
    
+[docs]
+class Tanaka(FloatProblem):
+    """Class representing problem Tanaka."""
 
     def __init__(self):
         super(Tanaka, self).__init__()
-        self.number_of_variables = 2
-        self.number_of_objectives = 2
-        self.number_of_constraints = 2
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
+
+        number_of_variables = 2
+        self.lower_bound = [10e-5 for _ in range(number_of_variables)]
+        self.upper_bound = [pi for _ in range(number_of_variables)]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-        self.lower_bound = [10e-5 for _ in range(self.number_of_variables)]
-        self.upper_bound = [pi for _ in range(self.number_of_variables)]
 
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 2
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         solution.objectives[0] = solution.variables[0]
         solution.objectives[1] = solution.variables[1]
 
@@ -184,50 +213,61 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    return solution
    
 
+
     def __evaluate_constraints(self, solution: FloatSolution) -> None:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         x1 = solution.variables[0]
         x2 = solution.variables[1]
 
-        constraints[0] = (x1 * x1 + x2 * x2 - 1.0 - 0.1 * cos(16.0 * atan(x1 / x2)))
+        constraints[0] = x1 * x1 + x2 * x2 - 1.0 - 0.1 * cos(16.0 * atan(x1 / x2))
         constraints[1] = -2.0 * ((x1 - 0.5) * (x1 - 0.5) + (x2 - 0.5) * (x2 - 0.5) - 0.5)
 
         solution.constraints = constraints
 
-        #set_overall_constraint_violation_degree(solution)
+        # set_overall_constraint_violation_degree(solution)
 
+
    
+[docs]
+    def name(self):
+        return "Tanaka"
    
+
    
 
-
    [docs]    def get_name(self):
-        return 'Tanaka'
    
 
 
-
    [docs]class Osyczka2(FloatProblem):
-    """ Class representing problem Osyczka2. """
+
    
+[docs]
+class Osyczka2(FloatProblem):
+    """Class representing problem Osyczka2."""
 
     def __init__(self):
         super(Osyczka2, self).__init__()
-        self.number_of_variables = 6
-        self.number_of_objectives = 2
-        self.number_of_constraints = 6
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
 
         self.lower_bound = [0.0, 0.0, 1.0, 0.0, 1.0, 0.0]
         self.upper_bound = [10.0, 10.0, 5.0, 6.0, 5.0, 10.0]
 
-        FloatSolution.lower_bound = self.lower_bound
-        FloatSolution.upper_bound = self.upper_bound
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 6
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
-        solution.objectives[0] = - (25.0 *
-                                    (x[0] - 2.0) ** 2 +
-                                    (x[1] - 2.0) ** 2 +
-                                    (x[2] - 1.0) ** 2 +
-                                    (x[3] - 4.0) ** 2 +
-                                    (x[4] - 1.0) ** 2)
+        solution.objectives[0] = -(
+            25.0 * (x[0] - 2.0) ** 2 + (x[1] - 2.0) ** 2 + (x[2] - 1.0) ** 2 + (x[3] - 4.0) ** 2 + (x[4] - 1.0) ** 2
+        )
 
         solution.objectives[1] = sum([x[i] ** 2 for i in range(len(x))])
 
@@ -235,8 +275,9 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    return solution
    
 
+
     def __evaluate_constraints(self, solution: FloatSolution) -> None:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         x = solution.variables
         constraints[0] = (x[0] + x[1]) / 2.0 - 1.0
@@ -248,29 +289,43 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    solution.constraints = constraints
 
-
    [docs]    def get_name(self):
-        return 'Osyczka2'
    
+
    
+[docs]
+    def name(self):
+        return "Osyczka2"
    
+
    
+
 
 
-
    [docs]class Binh2(FloatProblem):
-    """ Class representing problem Binh2. """
+
    
+[docs]
+class Binh2(FloatProblem):
+    """Class representing problem Binh2."""
 
     def __init__(self):
         super(Binh2, self).__init__()
-        self.number_of_variables = 2
-        self.number_of_objectives = 2
-        self.number_of_constraints = 2
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
 
         self.lower_bound = [0.0, 0.0]
         self.upper_bound = [5.0, 3.0]
 
-        FloatSolution.lower_bound = self.lower_bound
-        FloatSolution.upper_bound = self.upper_bound
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 2
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
         solution.objectives[0] = 4.0 * x[0] * x[0] + 4 * x[1] * x[1]
         solution.objectives[1] = (x[0] - 5.0) * (x[0] - 5.0) + (x[1] - 5.0) * (x[1] - 5.0)
@@ -279,15 +334,20 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    return solution
    
 
+
     def __evaluate_constraints(self, solution: FloatSolution) -> None:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         x = solution.variables
         constraints[0] = -1.0 * (x[0] - 5) * (x[0] - 5) - x[1] * x[1] + 25.0
         constraints[1] = (x[0] - 8) * (x[0] - 8) + (x[1] + 3) * (x[1] + 3) - 7.7
 
-
    [docs]    def get_name(self):
-        return 'Binh2'
    
+
    
+[docs]
+    def name(self):
+        return "Binh2"
    
+
    
+
 
    
 
           
    
@@ -304,8 +364,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.constrained
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.multiobjective.dtlz — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.dtlz
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -109,7 +106,7 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.dtlz
    -from math import pi, cos, sin
+from math import cos, pi, sin
 
 from jmetal.core.problem import FloatProblem
 from jmetal.core.solution import FloatSolution
@@ -123,254 +120,333 @@ 
    Source code for jmetal.problem.multiobjective.dtlz
    """
 
 
-
    [docs]class DTLZ1(FloatProblem):
-    """ Problem DTLZ1. Continuous problem having a flat Pareto front
+
    
+[docs]
+class DTLZ1(FloatProblem):
+    """Problem DTLZ1. Continuous problem having a flat Pareto front
 
     .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 7 and 3.
     """
 
     def __init__(self, number_of_variables: int = 7, number_of_objectives=3):
-        """ :param number_of_variables: number of decision variables of the problem.
-        """
+        """:param number_of_variables: number of decision variables of the problem."""
         super(DTLZ1, self).__init__()
-        self.number_of_variables = number_of_variables
-        self.number_of_objectives = number_of_objectives
-        self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE] * number_of_objectives
-        self.obj_labels = ['$ f_{} $'.format(i) for i in range(number_of_objectives)]
+        self.obj_labels = ["$ f_{} $".format(i) for i in range(number_of_objectives)]
+
+        self.lower_bound = number_of_variables * [0.0]
+        self.upper_bound = number_of_variables * [1.0]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
+    
+
    
+[docs]
+    def number_of_variables(self) -> int:
+        return len(self.lower_bound)
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
 
-        self.lower_bound = self.number_of_variables * [0.0]
-        self.upper_bound = self.number_of_variables * [1.0]
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([(x - 0.5) * (x - 0.5) - cos(20.0 * pi * (x - 0.5))
-                 for x in solution.variables[self.number_of_variables - k:]])
+        g = sum(
+            [
+                (x - 0.5) * (x - 0.5) - cos(20.0 * pi * (x - 0.5))
+                for x in solution.variables[self.number_of_variables() - k :]
+            ]
+        )
 
         g = 100 * (k + g)
 
-        solution.objectives = [(1.0 + g) * 0.5] * self.number_of_objectives
+        solution.objectives = [(1.0 + g) * 0.5] * self.number_of_objectives()
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 solution.objectives[i] *= solution.variables[j]
 
             if i != 0:
-                solution.objectives[i] *= 1 - solution.variables[self.number_of_objectives - (i + 1)]
+                solution.objectives[i] *= 1 - solution.variables[self.number_of_objectives() - (i + 1)]
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'DTLZ1'
    
+
+
    
+[docs]
+    def name(self):
+        return "DTLZ1"
    
+
    
 
 
-
    [docs]class DTLZ2(DTLZ1):
-    """ Problem DTLZ2. Continuous problem having a convex Pareto front
+
+
    
+[docs]
+class DTLZ2(DTLZ1):
+    """Problem DTLZ2. Continuous problem having a convex Pareto front
 
     .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
     def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
-        """:param number_of_variables: number of decision variables of the problem
-        """
+        """:param number_of_variables: number of decision variables of the problem"""
         super(DTLZ2, self).__init__(number_of_variables, number_of_objectives)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([(x - 0.5) * (x - 0.5) for x in solution.variables[self.number_of_variables - k:]])
+        g = sum([(x - 0.5) * (x - 0.5) for x in solution.variables[self.number_of_variables() - k :]])
 
-        solution.objectives = [1.0 + g] * self.number_of_objectives
+        solution.objectives = [1.0 + g] * self.number_of_objectives()
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 solution.objectives[i] *= cos(solution.variables[j] * 0.5 * pi)
 
             if i != 0:
-                solution.objectives[i] *= sin(0.5 * pi * solution.variables[self.number_of_objectives - (i + 1)])
+                solution.objectives[i] *= sin(0.5 * pi * solution.variables[self.number_of_objectives() - (i + 1)])
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'DTLZ2'
    
 
+
    
+[docs]
+    def name(self):
+        return "DTLZ2"
    
+
    
 
-
    [docs]class DTLZ3(DTLZ1):
-    """ Problem DTLZ3. Continuous problem having a convex Pareto front
+
+
+
    
+[docs]
+class DTLZ3(DTLZ1):
+    """Problem DTLZ3. Continuous problem having a convex Pareto front
 
     .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
     def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
-        """:param number_of_variables: number of decision variables of the problem
-        """
+        """:param number_of_variables: number of decision variables of the problem"""
         super(DTLZ3, self).__init__(number_of_variables, number_of_objectives)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([(x - 0.5) ** 2 - cos(20.0 * pi * (x - 0.5)) for x in solution.variables[self.number_of_variables - k:]])
+        g = sum(
+            [(x - 0.5) ** 2 - cos(20.0 * pi * (x - 0.5)) for x in solution.variables[self.number_of_variables() - k :]]
+        )
         g = 100.0 * (k + g)
 
-        f = [1.0 + g for _ in range(self.number_of_objectives)]
+        f = [1.0 + g for _ in range(self.number_of_objectives())]
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 f[i] *= cos(solution.variables[j] * 0.5 * pi)
 
             if i != 0:
-                aux = self.number_of_objectives - (i + 1)
+                aux = self.number_of_objectives() - (i + 1)
                 f[i] *= sin(solution.variables[aux] * 0.5 * pi)
 
-        solution.objectives = [f[x] for x in range(self.number_of_objectives)]
+        solution.objectives = [f[x] for x in range(self.number_of_objectives())]
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'DTLZ3'
    
+
+
    
+[docs]
+    def name(self):
+        return "DTLZ3"
    
+
    
+
 
 
-
    [docs]class DTLZ4(DTLZ1):
-    """ Problem DTLZ4. Continuous problem having a convex Pareto front
+
    
+[docs]
+class DTLZ4(DTLZ1):
+    """Problem DTLZ4. Continuous problem having a convex Pareto front
 
     .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
     def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
-        """:param number_of_variables: number of decision variables of the problem
-        """
+        """:param number_of_variables: number of decision variables of the problem"""
         super(DTLZ4, self).__init__(number_of_variables, number_of_objectives)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         alpha = 100.0
-        k = self.number_of_variables - self.number_of_objectives + 1
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([(x - 0.5) ** 2 for x in solution.variables[self.number_of_variables - k:]])
-        f = [1.0 + g for _ in range(self.number_of_objectives)]
+        g = sum([(x - 0.5) ** 2 for x in solution.variables[self.number_of_variables() - k :]])
+        f = [1.0 + g for _ in range(self.number_of_objectives())]
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 f[i] *= cos(pow(solution.variables[j], alpha) * pi / 2.0)
 
             if i != 0:
-                aux = self.number_of_objectives - (i + 1)
+                aux = self.number_of_objectives() - (i + 1)
                 f[i] *= sin(pow(solution.variables[aux], alpha) * pi / 2.0)
 
-        solution.objectives = [f[x] for x in range(self.number_of_objectives)]
+        solution.objectives = [f[x] for x in range(self.number_of_objectives())]
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'DTLZ4'
    
+
+
    
+[docs]
+    def name(self):
+        return "DTLZ4"
    
+
    
 
 
-
    [docs]class DTLZ5(DTLZ1):
-    """ Problem DTLZ5. Continuous problem having a convex Pareto front
+
+
    
+[docs]
+class DTLZ5(DTLZ1):
+    """Problem DTLZ5. Continuous problem having a convex Pareto front
 
     .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
     def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
-        """:param number_of_variables: number of decision variables of the problem
-        """
+        """:param number_of_variables: number of decision variables of the problem"""
         super(DTLZ5, self).__init__(number_of_variables, number_of_objectives)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([(x - 0.5) ** 2 for x in solution.variables[self.number_of_variables - k:]])
-        t = pi/(4.0 * (1.0 + g))
+        g = sum([(x - 0.5) ** 2 for x in solution.variables[self.number_of_variables() - k :]])
+        t = pi / (4.0 * (1.0 + g))
 
-        theta = [0.0]*(self.number_of_objectives - 1)
-        theta[0] = solution.variables[0]*pi/2.0
-        theta[1:] = [t * (1.0 + 2.0 * g * solution.variables[i]) for i in range(1,self.number_of_objectives-1)]
+        theta = [0.0] * (self.number_of_objectives() - 1)
+        theta[0] = solution.variables[0] * pi / 2.0
+        theta[1:] = [t * (1.0 + 2.0 * g * solution.variables[i]) for i in range(1, self.number_of_objectives() - 1)]
 
-        f = [1.0 + g for _ in range(self.number_of_objectives)]
+        f = [1.0 + g for _ in range(self.number_of_objectives())]
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 f[i] *= cos(theta[j])
 
             if i != 0:
-                aux = self.number_of_objectives - (i + 1)
+                aux = self.number_of_objectives() - (i + 1)
                 f[i] *= sin(theta[aux])
 
-        solution.objectives = [f[x] for x in range(self.number_of_objectives)]
+        solution.objectives = [f[x] for x in range(self.number_of_objectives())]
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'DTLZ5'
    
+
+
    
+[docs]
+    def name(self):
+        return "DTLZ5"
    
+
    
+
 
 
-
    [docs]class DTLZ6(DTLZ1):
-    """ Problem DTLZ6. Continuous problem having a convex Pareto front
+
    
+[docs]
+class DTLZ6(DTLZ1):
+    """Problem DTLZ6. Continuous problem having a convex Pareto front
 
     .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
     def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
-        """:param number_of_variables: number of decision variables of the problem
-        """
+        """:param number_of_variables: number of decision variables of the problem"""
         super(DTLZ6, self).__init__(number_of_variables, number_of_objectives)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([pow(x, 0.1) for x in solution.variables[self.number_of_variables - k:]])
-        t = pi/(4.0 * (1.0 + g))
+        g = sum([pow(x, 0.1) for x in solution.variables[self.number_of_variables() - k :]])
+        t = pi / (4.0 * (1.0 + g))
 
-        theta = [0.0]*(self.number_of_objectives - 1)
-        theta[0] = solution.variables[0]*pi/2.0
-        theta[1:] = [t * (1.0 + 2.0 * g * solution.variables[i]) for i in range(1,self.number_of_objectives-1)]
+        theta = [0.0] * (self.number_of_objectives() - 1)
+        theta[0] = solution.variables[0] * pi / 2.0
+        theta[1:] = [t * (1.0 + 2.0 * g * solution.variables[i]) for i in range(1, self.number_of_objectives() - 1)]
 
-        f = [1.0 + g for _ in range(self.number_of_objectives)]
+        f = [1.0 + g for _ in range(self.number_of_objectives())]
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 f[i] *= cos(theta[j])
 
             if i != 0:
-                aux = self.number_of_objectives - (i + 1)
+                aux = self.number_of_objectives() - (i + 1)
                 f[i] *= sin(theta[aux])
 
-        solution.objectives = [f[x] for x in range(self.number_of_objectives)]
+        solution.objectives = [f[x] for x in range(self.number_of_objectives())]
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'DTLZ6'
    
+
+
    
+[docs]
+    def name(self):
+        return "DTLZ6"
    
+
    
+
 
 
-
    [docs]class DTLZ7(DTLZ1):
-    """ Problem DTLZ6. Continuous problem having a disconnected Pareto front
+
    
+[docs]
+class DTLZ7(DTLZ1):
+    """Problem DTLZ6. Continuous problem having a disconnected Pareto front
 
     .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 22 and 3.
     """
 
     def __init__(self, number_of_variables: int = 22, number_of_objectives=3):
-        """:param number_of_variables: number of decision variables of the problem
-        """
+        """:param number_of_variables: number of decision variables of the problem"""
         super(DTLZ7, self).__init__(number_of_variables, number_of_objectives)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([x for x in solution.variables[self.number_of_variables - k:]])
+        g = sum([x for x in solution.variables[self.number_of_variables() - k :]])
         g = 1.0 + (9.0 * g) / k
 
-        h = sum([(x / (1.0 + g)) * (1 + sin(3.0 * pi * x)) for x in solution.variables[:self.number_of_objectives-1]])
-        h = self.number_of_objectives - h
+        h = sum(
+            [(x / (1.0 + g)) * (1 + sin(3.0 * pi * x)) for x in solution.variables[: self.number_of_objectives() - 1]]
+        )
+        h = self.number_of_objectives() - h
 
-        solution.objectives[:self.number_of_objectives-1] = solution.variables[:self.number_of_objectives-1]
+        solution.objectives[: self.number_of_objectives() - 1] = solution.variables[: self.number_of_objectives() - 1]
         solution.objectives[-1] = (1.0 + g) * h
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'DTLZ7'
    
+
+
    
+[docs]
+    def name(self):
+        return "DTLZ7"
    
+
    
+
 
    
 
           
    
@@ -387,8 +463,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.dtlz
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.multiobjective.fda — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.fda
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -110,11 +107,11 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.problem.multiobjective.fda
     from abc import ABC, abstractmethod
-from math import sqrt, pow, sin, pi, floor, cos
+from math import cos, floor, pi, pow, sin, sqrt
 
 import numpy
 
-from jmetal.core.problem import FloatProblem, DynamicProblem
+from jmetal.core.problem import DynamicProblem, FloatProblem
 from jmetal.core.solution import FloatSolution
 
 """
@@ -126,7 +123,9 @@ 
    Source code for jmetal.problem.multiobjective.fda
    """
 
 
-
    [docs]class FDA(DynamicProblem, FloatProblem, ABC):
+
    
+[docs]
+class FDA(DynamicProblem, FloatProblem, ABC):
     def __init__(self):
         super(FDA, self).__init__()
         self.tau_T = 5
@@ -134,45 +133,61 @@ 
    Source code for jmetal.problem.multiobjective.fda
    self.time = 1.0
         self.problem_modified = False
 
-
    [docs]    def update(self, *args, **kwargs):
+
    
+[docs]
+    def update(self, *args, **kwargs):
         counter: int = kwargs["COUNTER"]
         self.time = (1.0 / self.nT) * floor(counter * 1.0 / self.tau_T)
         self.problem_modified = True
    
 
-
    [docs]    def the_problem_has_changed(self) -> bool:
+
+
    
+[docs]
+    def the_problem_has_changed(self) -> bool:
         return self.problem_modified
    
 
-
    [docs]    def clear_changed(self) -> None:
+
+
    
+[docs]
+    def clear_changed(self) -> None:
         self.problem_modified = False
    
 
-
    [docs]    @abstractmethod
+
+
    
+[docs]
+    @abstractmethod
     def evaluate(self, solution: FloatSolution):
-        pass
    
+        pass
    
+
    
+
 
 
-
    [docs]class FDA1(FDA):
-    """ Problem FDA1.
+
    
+[docs]
+class FDA1(FDA):
+    """Problem FDA1.
 
     .. note:: Bi-objective dynamic unconstrained problem. The default number of variables is 100.
     """
 
     def __init__(self, number_of_variables: int = 100):
-        """ :param number_of_variables: Number of decision variables of the problem.
-        """
+        """:param number_of_variables: Number of decision variables of the problem."""
         super(FDA1, self).__init__()
         self.number_of_variables = number_of_variables
         self.number_of_objectives = 2
         self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
 
         self.lower_bound = self.number_of_variables * [-1.0]
         self.upper_bound = self.number_of_variables * [1.0]
         self.lower_bound[0] = 0.0
         self.upper_bound[0] = 1.0
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         g = self.__eval_g(solution)
         h = self.__eval_h(solution.variables[0], g)
 
@@ -181,6 +196,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
    return solution
    
 
+
     def __eval_g(self, solution: FloatSolution):
         gT = sin(0.5 * pi * self.time)
         g = 1.0 + sum([pow(v - gT, 2) for v in solution.variables[1:]])
@@ -190,33 +206,40 @@ 
    Source code for jmetal.problem.multiobjective.fda
    def __eval_h(self, f: float, g: float) -> float:
         return 1.0 - sqrt(f / g)
 
-
    [docs]    def get_name(self):
-        return 'FDA1'
    
+
    
+[docs]
+    def get_name(self):
+        return "FDA1"
    
+
    
+
 
 
-
    [docs]class FDA2(FDA):
-    """ Problem FDA2
+
    
+[docs]
+class FDA2(FDA):
+    """Problem FDA2
 
     .. note:: Bi-objective dynamic unconstrained problem. The default number of variables is 31.
     """
 
     def __init__(self, number_of_variables: int = 31):
-        """ :param number_of_variables: Number of decision variables of the problem.
-        """
+        """:param number_of_variables: Number of decision variables of the problem."""
         super(FDA2, self).__init__()
         self.number_of_variables = number_of_variables
         self.number_of_objectives = 2
         self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
 
         self.lower_bound = self.number_of_variables * [-1.0]
         self.upper_bound = self.number_of_variables * [1.0]
         self.lower_bound[0] = 0.0
         self.upper_bound[0] = 1.0
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         g = self.__eval_g(solution, 1, len(solution.variables))
         h = self.__eval_h(solution.variables[0], g)
 
@@ -225,6 +248,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
    return solution
    
 
+
     def __eval_g(self, solution: FloatSolution, lower_limit: int, upper_limit: int):
         g = sum([pow(v, 2) for v in solution.variables[lower_limit:upper_limit]])
         g += 1.0 + sum([pow(v + 1.0, 2.0) for v in solution.variables[upper_limit:]])
@@ -235,19 +259,24 @@ 
    Source code for jmetal.problem.multiobjective.fda
    ht = 0.2 + 4.8 * pow(self.time, 2.0)
         return 1.0 - pow(f / g, ht)
 
-
    [docs]    def get_name(self):
-        return 'FDA2'
    
+
    
+[docs]
+    def get_name(self):
+        return "FDA2"
    
+
    
+
 
 
-
    [docs]class FDA3(FDA):
-    """ Problem FDA3
+
    
+[docs]
+class FDA3(FDA):
+    """Problem FDA3
 
     .. note:: Bi-objective dynamic unconstrained problem. The default number of variables is 30.
     """
 
     def __init__(self, number_of_variables: int = 30):
-        """ :param number_of_variables: Number of decision variables of the problem.
-        """
+        """:param number_of_variables: Number of decision variables of the problem."""
         super(FDA3, self).__init__()
         self.number_of_variables = number_of_variables
         self.number_of_objectives = 2
@@ -257,14 +286,16 @@ 
    Source code for jmetal.problem.multiobjective.fda
    self.limitInfII = 1
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
 
         self.lower_bound = self.number_of_variables * [-1.0]
         self.upper_bound = self.number_of_variables * [1.0]
         self.lower_bound[0] = 0.0
         self.upper_bound[0] = 1.0
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         g = self.__eval_g(solution, self.limitInfII)
         h = self.__eval_h(solution.variables[0], g)
 
@@ -273,6 +304,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
    return solution
    
 
+
     def __eval_f(self, solution: FloatSolution, lower_limit: int, upper_limit: int):
         f = 0.0
         aux = 2.0 * sin(0.5 * pi * self.time)
@@ -292,32 +324,40 @@ 
    Source code for jmetal.problem.multiobjective.fda
    h = 1.0 - sqrt(f / g)
         return h
 
-
    [docs]    def get_name(self):
-        return 'FDA3'
    
+
    
+[docs]
+    def get_name(self):
+        return "FDA3"
    
+
    
+
 
 
-
    [docs]class FDA4(FDA):
-    """ Problem FDA4
+
    
+[docs]
+class FDA4(FDA):
+    """Problem FDA4
 
     .. note:: Three-objective dynamic unconstrained problem. The default number of variables is 12.
     """
+
     M = 3
 
     def __init__(self, number_of_variables: int = 12):
-        """ :param number_of_variables: Number of decision variables of the problem.
-        """
+        """:param number_of_variables: Number of decision variables of the problem."""
         super(FDA4, self).__init__()
         self.number_of_variables = number_of_variables
         self.number_of_objectives = 3
         self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)', 'f(z)']
+        self.obj_labels = ["f(x)", "f(y)", "f(z)"]
 
         self.lower_bound = self.number_of_variables * [0.0]
         self.upper_bound = self.number_of_variables * [1.0]
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         g = self.__eval_g(solution, self.M - 1)
 
         solution.objectives[0] = self.__eval_f1(solution, g)
@@ -326,6 +366,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
    return solution
    
 
+
     def __eval_g(self, solution: FloatSolution, lower_limit: int):
         gt = abs(sin(0.5 * pi * self.time))
         g = sum([pow(v - gt, 2) for v in solution.variables[lower_limit:]])
@@ -334,14 +375,14 @@ 
    Source code for jmetal.problem.multiobjective.fda
    def __eval_f1(self, solution: FloatSolution, g: float) -> float:
         f = 1.0 + g
-        mult = numpy.prod([cos(v * pi / 2.0) for v in solution.variables[:self.M - 1]])
+        mult = numpy.prod([cos(v * pi / 2.0) for v in solution.variables[: self.M - 1]])
 
         return f * mult
 
     def __eval_fk(self, solution: FloatSolution, g: float, k: int) -> float:
         f = 1.0 + g
         aux = sin((solution.variables[self.M - k] * pi) / 2.0)
-        mult = numpy.prod([cos(v * pi / 2.0) for v in solution.variables[:self.M - k]])
+        mult = numpy.prod([cos(v * pi / 2.0) for v in solution.variables[: self.M - k]])
 
         return f * mult * aux
 
@@ -351,32 +392,40 @@ 
    Source code for jmetal.problem.multiobjective.fda
    return fm
 
-
    [docs]    def get_name(self):
-        return 'FDA4'
    
+
    
+[docs]
+    def get_name(self):
+        return "FDA4"
    
+
    
 
 
-
    [docs]class FDA5(FDA):
-    """ Problem FDA5
+
+
    
+[docs]
+class FDA5(FDA):
+    """Problem FDA5
 
     .. note:: Three-objective dynamic unconstrained problem. The default number of variables is 12.
     """
+
     M = 3
 
     def __init__(self, number_of_variables: int = 12):
-        """ :param number_of_variables: Number of decision variables of the problem.
-        """
+        """:param number_of_variables: Number of decision variables of the problem."""
         super(FDA5, self).__init__()
         self.number_of_variables = number_of_variables
         self.number_of_objectives = 3
         self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)', 'f(z)']
+        self.obj_labels = ["f(x)", "f(y)", "f(z)"]
 
         self.lower_bound = self.number_of_variables * [0.0]
         self.upper_bound = self.number_of_variables * [1.0]
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         g = self.__eval_g(solution, self.M - 1)
         ft = 1.0 + 100.0 * pow(sin(0.5 * pi * self.time), 4.0)
 
@@ -386,6 +435,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
    return solution
    
 
+
     def __eval_g(self, solution: FloatSolution, lower_limit: int):
         gt = abs(sin(0.5 * pi * self.time))
         g = sum([pow(v - gt, 2) for v in solution.variables[lower_limit:]])
@@ -394,14 +444,14 @@ 
    Source code for jmetal.problem.multiobjective.fda
    def __eval_f1(self, solution: FloatSolution, g: float, ft: float) -> float:
         f = 1.0 + g
-        mult = numpy.prod([cos(pow(v, ft) * pi / 2.0) for v in solution.variables[:self.M - 1]])
+        mult = numpy.prod([cos(pow(v, ft) * pi / 2.0) for v in solution.variables[: self.M - 1]])
 
         return f * mult
 
     def __eval_fk(self, solution: FloatSolution, g: float, k: int, ft: float) -> float:
         f = 1.0 + g
 
-        mult = numpy.prod([cos(pow(v, ft) * pi / 2.0) for v in solution.variables[:self.M - k]])
+        mult = numpy.prod([cos(pow(v, ft) * pi / 2.0) for v in solution.variables[: self.M - k]])
         yy = pow(solution.variables[self.M - k], ft)
         mult *= sin(yy * pi / 2.0)
 
@@ -414,8 +464,12 @@ 
    Source code for jmetal.problem.multiobjective.fda
    return fm * mult
 
-
    [docs]    def get_name(self):
-        return 'FDA5'
    
+
    
+[docs]
+    def get_name(self):
+        return "FDA5"
    
+
    
+
 
    
 
           
    
@@ -432,8 +486,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.fda
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.multiobjective.lircmop — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.lircmop
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -109,14 +106,17 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.lircmop
    -from math import sin, pi, cos, sqrt
+from math import cos, pi, sin, sqrt
+from typing import List
 
 from jmetal.core.problem import FloatProblem
 from jmetal.core.solution import FloatSolution
 
 
-
    [docs]class LIRCMOP1(FloatProblem):
-    """ Class representing problem LIR-CMOP1, defined in:
+
    
+[docs]
+class LIRCMOP1(FloatProblem):
+    """Class representing problem LIR-CMOP1, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -124,17 +124,28 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP1, self).__init__()
-        self.number_of_variables = number_of_variables
-        self.number_of_objectives = 2
-        self.number_of_constraints = 2
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
+
+        self.lower_bound = [0.0 for _ in range(number_of_variables)]
+        self.upper_bound = [1.0 for _ in range(number_of_variables)]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 2
    
 
-        self.lower_bound = [0.0 for _ in range(self.number_of_variables)]
-        self.upper_bound = [1.0 for _ in range(self.number_of_variables)]
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
 
         solution.objectives[0] = x[0] + self.g1(x)
@@ -144,9 +155,12 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+        constraints = [0.0 for _ in range(len(solution.constraints))]
 
         a = 0.51
         b = 0.5
@@ -158,26 +172,39 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def g1(self, x: [float]) -> float:
+
+
    
+[docs]
+    def g1(self, x: List[float]) -> float:
         result = 0
-        for i in range(2, self.number_of_variables, 2):
+        for i in range(2, self.number_of_variables(), 2):
             result += pow(x[i] - sin(0.5 * pi * x[0]), 2.0)
 
         return result
    
 
-
    [docs]    def g2(self, x: [float]) -> float:
+
+
    
+[docs]
+    def g2(self, x: List[float]) -> float:
         result = 0
-        for i in range(1, self.number_of_variables, 2):
+        for i in range(1, self.number_of_variables(), 2):
             result += pow(x[i] - cos(0.5 * pi * x[0]), 2.0)
 
         return result
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP1'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP1"
    
+
    
+
 
 
-
    [docs]class LIRCMOP2(LIRCMOP1):
-    """ Class representing problem LIR-CMOP1, defined in:
+
    
+[docs]
+class LIRCMOP2(LIRCMOP1):
+    """Class representing problem LIR-CMOP1, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -186,7 +213,9 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP2, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
 
         solution.objectives[0] = x[0] + self.g1(x)
@@ -196,12 +225,19 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP2'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP2"
    
+
    
 
 
-
    [docs]class LIRCMOP3(LIRCMOP1):
-    """ Class representing problem LIR-CMOP3, defined in:
+
+
    
+[docs]
+class LIRCMOP3(LIRCMOP1):
+    """Class representing problem LIR-CMOP3, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -210,9 +246,17 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP3, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 3
    
+
+
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         a = 0.51
         b = 0.5
@@ -226,12 +270,19 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP3'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP3"
    
+
    
 
 
-
    [docs]class LIRCMOP4(LIRCMOP2):
-    """ Class representing problem LIR-CMOP4, defined in:
+
+
    
+[docs]
+class LIRCMOP4(LIRCMOP2):
+    """Class representing problem LIR-CMOP4, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -240,9 +291,17 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP4, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 3
    
+
+
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         a = 0.51
         b = 0.5
@@ -256,12 +315,19 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP4'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP4"
    
+
    
 
 
-
    [docs]class LIRCMOP5(FloatProblem):
-    """ Class representing problem LIR-CMOP5, defined in:
+
+
    
+[docs]
+class LIRCMOP5(FloatProblem):
+    """Class representing problem LIR-CMOP5, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -269,17 +335,28 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP5, self).__init__()
-        self.number_of_variables = number_of_variables
-        self.number_of_objectives = 2
-        self.number_of_constraints = 2
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
+
+        self.lower_bound = [0.0 for _ in range(number_of_variables)]
+        self.upper_bound = [1.0 for _ in range(number_of_variables)]
 
-        self.lower_bound = [0.0 for _ in range(self.number_of_variables)]
-        self.upper_bound = [1.0 for _ in range(self.number_of_variables)]
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 2
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
 
         solution.objectives[0] = x[0] + 10 * self.g1(x) + 0.7057
@@ -289,8 +366,11 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         r = 0.1
         theta = -0.25 * pi
@@ -302,36 +382,49 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    f2 = solution.objectives[1]
 
         for i in range(len(x_offset)):
-            constraints[i] = pow(
-                ((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2) + \
-                             pow(
-                                 ((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i],
-                                 2) - r
+            constraints[i] = (
+                pow(((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2)
+                + pow(((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i], 2)
+                - r
+            )
 
         solution.constraints = constraints
 
         return solution
    
 
-
    [docs]    def g1(self, x: [float]) -> float:
+
+
    
+[docs]
+    def g1(self, x: [float]) -> float:
         result = 0
-        for i in range(2, self.number_of_variables, 2):
+        for i in range(2, self.number_of_variables(), 2):
             result += pow(x[i] - sin(0.5 * i / len(x) * pi * x[0]), 2.0)
 
         return result
    
 
-
    [docs]    def g2(self, x: [float]) -> float:
+
+
    
+[docs]
+    def g2(self, x: [float]) -> float:
         result = 0
-        for i in range(1, self.number_of_variables, 2):
+        for i in range(1, self.number_of_variables(), 2):
             result += pow(x[i] - cos(0.5 * i / len(x) * pi * x[0]), 2.0)
 
         return result
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP5'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP5"
    
+
    
 
 
-
    [docs]class LIRCMOP6(LIRCMOP5):
-    """ Class representing problem LIR-CMOP6, defined in:
+
+
    
+[docs]
+class LIRCMOP6(LIRCMOP5):
+    """Class representing problem LIR-CMOP6, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -340,7 +433,9 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP6, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
 
         solution.objectives[0] = x[0] + 10 * self.g1(x) + 0.7057
@@ -350,8 +445,11 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         r = 0.1
         theta = -0.25 * pi
@@ -363,22 +461,29 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    f2 = solution.objectives[1]
 
         for i in range(len(x_offset)):
-            constraints[i] = pow(
-                ((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2) + \
-                             pow(
-                                 ((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i],
-                                 2) - r
+            constraints[i] = (
+                pow(((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2)
+                + pow(((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i], 2)
+                - r
+            )
 
         solution.constraints = constraints
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP6'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP6"
    
+
    
 
 
-
    [docs]class LIRCMOP7(LIRCMOP5):
-    """ Class representing problem LIR-CMOP7, defined in:
+
+
    
+[docs]
+class LIRCMOP7(LIRCMOP5):
+    """Class representing problem LIR-CMOP7, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -387,8 +492,10 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP7, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         r = 0.1
         theta = -0.25 * pi
@@ -400,22 +507,29 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    f2 = solution.objectives[1]
 
         for i in range(len(x_offset)):
-            constraints[i] = pow(
-                ((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2) + \
-                             pow(
-                                 ((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i],
-                                 2) - r
+            constraints[i] = (
+                pow(((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2)
+                + pow(((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i], 2)
+                - r
+            )
 
         solution.constraints = constraints
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP7'
    
 
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP7"
    
+
    
 
-
    [docs]class LIRCMOP8(LIRCMOP6):
-    """ Class representing problem LIR-CMOP8, defined in:
+
+
+
    
+[docs]
+class LIRCMOP8(LIRCMOP6):
+    """Class representing problem LIR-CMOP8, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -424,8 +538,10 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP8, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         r = 0.1
         theta = -0.25 * pi
@@ -437,22 +553,29 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    f2 = solution.objectives[1]
 
         for i in range(len(x_offset)):
-            constraints[i] = pow(
-                ((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2) + \
-                             pow(
-                                 ((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i],
-                                 2) - r
+            constraints[i] = (
+                pow(((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2)
+                + pow(((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i], 2)
+                - r
+            )
 
         solution.constraints = constraints
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP8'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP8"
    
+
    
+
 
 
-
    [docs]class LIRCMOP9(LIRCMOP8):
-    """ Class representing problem LIR-CMOP9, defined in:
+
    
+[docs]
+class LIRCMOP9(LIRCMOP8):
+    """Class representing problem LIR-CMOP9, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -461,7 +584,9 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP9, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
 
         solution.objectives[0] = 1.7057 * x[0] * (10 * self.g1(x) + 1)
@@ -471,36 +596,49 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         theta = -0.25 * pi
         n = 4.0
         f0 = solution.objectives[0]
         f1 = solution.objectives[1]
 
-        constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2;
+        constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2
 
         x_offset = 1.40
         y_offset = 1.40
         a = 1.5
         b = 6.0
-        r = 0.1;
+        r = 0.1
 
-        constraints[1] = pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2) + pow(
-            ((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2) - r
+        constraints[1] = (
+            pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2)
+            + pow(((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2)
+            - r
+        )
 
         solution.constraints = constraints
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP9'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP9"
    
+
    
+
 
 
-
    [docs]class LIRCMOP10(LIRCMOP8):
-    """ Class representing problem LIR-CMOP10, defined in:
+
    
+[docs]
+class LIRCMOP10(LIRCMOP8):
+    """Class representing problem LIR-CMOP10, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -509,7 +647,9 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP10, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
 
         solution.objectives[0] = 1.7057 * x[0] * (10 * self.g1(x) + 1)
@@ -519,35 +659,48 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         theta = -0.25 * pi
         n = 4.0
         f0 = solution.objectives[0]
         f1 = solution.objectives[1]
 
-        constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 1;
+        constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 1
 
         x_offset = 1.1
         y_offset = 1.2
         a = 2.0
         b = 4.0
-        r = 0.1;
+        r = 0.1
 
-        constraints[1] = pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2) + pow(
-            ((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2) - r
+        constraints[1] = (
+            pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2)
+            + pow(((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2)
+            - r
+        )
 
         solution.constraints = constraints
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP10'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP10"
    
+
    
+
 
 
-
    [docs]class LIRCMOP11(LIRCMOP10):
-    """ Class representing problem LIR-CMOP11, defined in:
+
    
+[docs]
+class LIRCMOP11(LIRCMOP10):
+    """Class representing problem LIR-CMOP11, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -556,35 +709,47 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP11, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         theta = -0.25 * pi
         n = 4.0
         f0 = solution.objectives[0]
         f1 = solution.objectives[1]
 
-        constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2.1;
+        constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2.1
 
         x_offset = 1.2
         y_offset = 1.2
         a = 1.5
         b = 5.0
-        r = 0.1;
+        r = 0.1
 
-        constraints[1] = pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2) + pow(
-            ((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2) - r
+        constraints[1] = (
+            pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2)
+            + pow(((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2)
+            - r
+        )
 
         solution.constraints = constraints
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP11'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP11"
    
+
    
+
 
 
-
    [docs]class LIRCMOP12(LIRCMOP9):
-    """ Class representing problem LIR-CMOP9, defined in:
+
    
+[docs]
+class LIRCMOP12(LIRCMOP9):
+    """Class representing problem LIR-CMOP9, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -593,35 +758,47 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP12, self).__init__(number_of_variables)
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
 
         theta = -0.25 * pi
         n = 4.0
         f0 = solution.objectives[0]
         f1 = solution.objectives[1]
 
-        constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2.5;
+        constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2.5
 
         x_offset = 1.6
         y_offset = 1.6
         a = 1.5
         b = 6.0
-        r = 0.1;
+        r = 0.1
 
-        constraints[1] = pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2) + pow(
-            ((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2) - r
+        constraints[1] = (
+            pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2)
+            + pow(((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2)
+            - r
+        )
 
         solution.constraints = constraints
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP12'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP12"
    
+
    
 
 
-
    [docs]class LIRCMOP13(FloatProblem):
-    """ Class representing problem LIR-CMOP13, defined in:
+
+
    
+[docs]
+class LIRCMOP13(FloatProblem):
+    """Class representing problem LIR-CMOP13, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -629,17 +806,27 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP13, self).__init__()
-        self.number_of_variables = number_of_variables
-        self.number_of_objectives = 3
-        self.number_of_constraints = 2
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
+
+        self.lower_bound = [0.0 for _ in range(number_of_variables)]
+        self.upper_bound = [1.0 for _ in range(number_of_variables)]
 
-        self.lower_bound = [0.0 for _ in range(self.number_of_variables)]
-        self.upper_bound = [1.0 for _ in range(self.number_of_variables)]
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 2
    
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
 
         solution.objectives[0] = (1.7057 + self.g1(x)) * cos(0.5 * pi * x[0]) * cos(0.5 * pi + x[1])
@@ -650,10 +837,13 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
 
-        f = sum([pow(solution.objectives[i], 2) for i in range(solution.number_of_objectives)])
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
+
+        f = sum([pow(solution.objectives[i], 2) for i in range(len(solution.objectives))])
 
         constraints[0] = (f - 9) * (f - 4)
         constraints[1] = (f - 1.9 * 1.9) * (f - 1.8 * 1.8)
@@ -662,19 +852,29 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def g1(self, x: [float]) -> float:
+
+
    
+[docs]
+    def g1(self, x: [float]) -> float:
         result = 0
-        for i in range(2, self.number_of_variables, 2):
+        for i in range(2, self.number_of_variables(), 2):
             result += 10 * pow(x[i] - 0.5, 2.0)
 
         return result
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP13'
    
 
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP13"
    
+
    
 
-
    [docs]class LIRCMOP14(LIRCMOP13):
-    """ Class representing problem LIR-CMOP14, defined in:
+
+
+
    
+[docs]
+class LIRCMOP14(LIRCMOP13):
+    """Class representing problem LIR-CMOP14, defined in:
 
     * An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
       Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
@@ -682,12 +882,25 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP14, self).__init__(number_of_variables)
-        self.number_of_constraints = 3
 
-
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
-        constraints = [0.0 for _ in range(self.number_of_constraints)]
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 3
    
+
 
-        f = sum([pow(solution.objectives[i], 2) for i in range(solution.number_of_objectives)])
+
    
+[docs]
+    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
+        constraints = [0.0 for _ in range(self.number_of_constraints())]
+
+        f = sum([pow(solution.objectives[i], 2) for i in range(len(solution.objectives))])
 
         constraints[0] = (f - 9) * (f - 4)
         constraints[1] = (f - 1.9 * 1.9) * (f - 1.8 * 1.8)
@@ -697,8 +910,13 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
    return solution
    
 
-
    [docs]    def get_name(self):
-        return 'LIR-CMOP14'
    
+
+
    
+[docs]
+    def name(self):
+        return "LIR-CMOP14"
    
+
    
+
 
    
 
           
    
@@ -715,8 +933,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.lircmop
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.multiobjective.lz09 — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.lz09
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -124,48 +121,53 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    """
 
 
-
    [docs]class LZ09(FloatProblem):
+
    
+[docs]
+class LZ09(FloatProblem):
 
     __metaclass__ = ABCMeta
 
-    def __init__(self,
-                 number_of_variables: int,
-                 number_of_objectives: int,
-                 number_of_constraints: int,
-                 ptype: int,
-                 dtype: int,
-                 ltype: int):
-        """ LZ09 benchmark family as defined in:
+    def __init__(
+        self,
+        number_of_variables: int,
+        ptype: int,
+        dtype: int,
+        ltype: int,
+    ):
+        """LZ09 benchmark family as defined in:
 
         * H. Li and Q. Zhang. Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II.
         IEEE Transactions on Evolutionary Computation, 12(2):284-302, April 2009.
         """
         super(LZ09, self).__init__()
-        self.number_of_variables = number_of_variables
-        self.number_of_objectives = number_of_objectives
-        self.number_of_constraints = number_of_constraints
-
-        self.obj_directions = [self.MINIMIZE, self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)', 'f(z)']
 
-        self.lower_bound = self.number_of_variables * [0.0]
-        self.upper_bound = self.number_of_variables * [1.0]
+        self.lower_bound = number_of_variables * [0.0]
+        self.upper_bound = number_of_variables * [1.0]
 
         self.ptype = ptype
         self.dtype = dtype
         self.ltype = ltype
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x = solution.variables
         y = self.objective(x)
 
-        for i in range(self.number_of_objectives):
+        for i in range(self.number_of_objectives()):
             solution.objectives[i] = y[i]
 
         return solution
    
 
+
     def __ps_func2(self, x: float, t1: float, dim: int, type: int, css: int) -> float:
-        """ Control the PS shapes of 2-D instances.
+        """Control the PS shapes of 2-D instances.
 
         :param type: The type of the curve.
         :param css: The class of the index.
@@ -175,13 +177,13 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    if type == 21:
             xy = 2 * (x - 0.5)
-            beta = xy - math.pow(t1, 0.5 * (self.number_of_variables + 3 * dim - 8) / (self.number_of_variables - 2))
+            beta = xy - math.pow(t1, 0.5 * (self.number_of_variables() + 3 * dim - 8) / (self.number_of_variables() - 2))
         if type == 22:
-            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables
+            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables()
             xy = 2 * (x - 0.5)
             beta = xy - math.sin(theta)
         if type == 23:
-            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables
+            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables()
             ra = 0.8 * t1
             xy = 2 * (x - 0.5)
             if css == 1:
@@ -189,7 +191,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    else:
                 beta = xy - ra * math.sin(theta)
         if type == 24:
-            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables
+            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables()
             xy = 2 * (x - 0.5)
             ra = 0.8 * t1
             if css == 1:
@@ -199,7 +201,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    if type == 25:
             rho = 0.8
             phi = math.pi * t1
-            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables
+            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables()
             xy = 2 * (x - 0.5)
             if css == 1:
                 beta = xy - rho * math.sin(phi) * math.sin(theta)
@@ -208,7 +210,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    else:
                 beta = xy - rho * math.cos(phi)
         if type == 26:
-            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables
+            theta = 6 * math.pi * t1 + dim * math.pi / self.number_of_variables()
             ra = 0.3 * t1 * (t1 * math.cos(4 * theta) + 2)
             xy = 2 * (x - 0.5)
             if css == 1:
@@ -219,26 +221,25 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    return beta
 
     def __ps_func3(self, x: float, t1: float, t2: float, dim: int, type: int):
-        """ Control the PS shapes of 3-D instances.
+        """Control the PS shapes of 3-D instances.
         :param type: The type of curve.
         """
         beta = 0.0
         dim += 1
 
         if type == 31:
-            xy = 4 * (x - .5)
-            rate = 1.0 * dim / self.number_of_variables
+            xy = 4 * (x - 0.5)
+            rate = 1.0 * dim / self.number_of_variables()
             beta = xy - 4 * (t1 * t1 * rate + t2 * (1.0 - rate)) + 2
         if type == 32:
-            theta = 2 * math.pi * t1 + dim * math.pi / self.number_of_variables
-            xy = 4 * (x - .5)
+            theta = 2 * math.pi * t1 + dim * math.pi / self.number_of_variables()
+            xy = 4 * (x - 0.5)
             beta = xy - 2 * t2 * math.sin(theta)
 
         return beta
 
     def __alpha_func(self, x: list, dim: int, type: int) -> list:
-        """ Control the PF shape.
-        """
+        """Control the PF shape."""
         alpha = [0.0] * dim
 
         if dim == 2:
@@ -275,8 +276,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    return alpha
 
     def __beta_func(self, x: list, type: int) -> float:
-        """ Control the distance.
-        """
+        """Control the distance."""
         beta = 0.0
         dim = len(x)
 
@@ -294,7 +294,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    sum, xx = 0, 0
             for i in range(dim):
                 xx = 2 * x[i]
-                sum += (xx * xx - math.cos(4 * math.pi * xx) + 1)
+                sum += xx * xx - math.cos(4 * math.pi * xx) + 1
             beta = 2.0 * sum / dim
         if type == 4:
             sum, prod, xx = 0, 1, 0
@@ -306,16 +306,18 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    return beta
 
-
    [docs]    def objective(self, x_variables: list) -> list:
+
    
+[docs]
+    def objective(self, x_variables: list) -> list:
         aa = []
         bb = []
         cc = []
 
-        y_objectives = [0.0] * self.number_of_objectives
+        y_objectives = [0.0] * self.number_of_objectives()
 
-        if self.number_of_objectives == 2:
+        if self.number_of_objectives() == 2:
             if self.ltype in [21, 22, 23, 24, 26]:
-                for n in range(1, self.number_of_variables):
+                for n in range(1, self.number_of_variables()):
                     if n % 2 == 0:
                         a = self.__ps_func2(x_variables[n], x_variables[0], n, self.ltype, 1)
                         aa.append(a)
@@ -331,7 +333,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    y_objectives[0] = alpha[0] + h
                 y_objectives[1] = alpha[1] + g
             if self.ltype == 25:
-                for n in range(1, self.number_of_variables):
+                for n in range(1, self.number_of_variables()):
                     if n % 3 == 0:
                         a = self.__ps_func2(x_variables[n], x_variables[0], n, self.ltype, 1)
                         aa.append(a)
@@ -353,10 +355,10 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    y_objectives[0] = alpha[0] + h
                 y_objectives[1] = alpha[1] + g
 
-        if self.number_of_objectives == 3:
+        if self.number_of_objectives() == 3:
             if self.ltype == 31 or self.ltype == 32:
 
-                for n in range(2, self.number_of_variables):
+                for n in range(2, self.number_of_variables()):
                     a = self.__ps_func3(x_variables[n], x_variables[0], x_variables[1], n, self.ltype)
 
                     if n % 3 == 0:
@@ -378,98 +380,232 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    return y_objectives
    
 
-
    [docs]    def get_name(self):
-        return 'LZ09'
    
 
+
    
+[docs]
+    def get_name(self):
+        return "LZ09"
    
+
    
+
+
+
+
    
+[docs]
+class LZ09_F1(LZ09):
+    def __init__(self, number_of_variables=10):
+        super(LZ09_F1, self).__init__(
+            number_of_variables, dtype=1, ltype=21, ptype=21
+        )
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)"]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
+
+
    
+[docs]
+    def name(self):
+        return "LZ09_F1"
    
+
    
+
+
+
+
    
+[docs]
+class LZ09_F2(LZ09):
+    def __init__(self, number_of_variables=30):
+        super(LZ09_F2, self).__init__(
+            number_of_variables, dtype=1, ltype=22, ptype=21
+        )
+
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)"]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]class LZ09_F1(LZ09):
 
-    def __init__(self):
-        super(LZ09_F1, self).__init__(number_of_variables=10, number_of_objectives=2, number_of_constraints=0,
-                                      dtype=1, ltype=21, ptype=21)
+
    
+[docs]
+    def name(self):
+        return "LZ09_F2"
    
+
    
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F1'
    
 
 
-
    [docs]class LZ09_F2(LZ09):
+
    
+[docs]
+class LZ09_F3(LZ09):
+    def __init__(self, number_of_variables=30):
+        super(LZ09_F3, self).__init__(
+            number_of_variables, dtype=1, ltype=23, ptype=21
+        )
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)"]
 
-    def __init__(self):
-        super(LZ09_F2, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
-                                      dtype=1, ltype=22, ptype=21)
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F2'
    
 
+
    
+[docs]
+    def name(self):
+        return "LZ09_F3"
    
+
    
 
-
    [docs]class LZ09_F3(LZ09):
 
-    def __init__(self):
-        super(LZ09_F3, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
-                                      dtype=1, ltype=23, ptype=21)
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F3'
    
+
    
+[docs]
+class LZ09_F4(LZ09):
+    def __init__(self, number_of_variables=30):
+        super(LZ09_F4, self).__init__(
+            number_of_variables, dtype=1, ltype=24, ptype=21
+        )
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)"]
 
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]class LZ09_F4(LZ09):
 
-    def __init__(self):
-        super(LZ09_F4, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
-                                      dtype=1, ltype=24, ptype=21)
+
    
+[docs]
+    def name(self):
+        return "LZ09_F4"
    
+
    
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F4'
    
 
 
-
    [docs]class LZ09_F5(LZ09):
+
    
+[docs]
+class LZ09_F5(LZ09):
+    def __init__(self, number_of_variables=30):
+        super(LZ09_F5, self).__init__(
+            number_of_variables, dtype=1, ltype=26, ptype=21
+        )
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)"]
 
-    def __init__(self):
-        super(LZ09_F5, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
-                                      dtype=1, ltype=26, ptype=21)
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F5'
    
 
+
    
+[docs]
+    def name(self):
+        return "LZ09_F5"
    
+
    
 
-
    [docs]class LZ09_F6(LZ09):
 
-    def __init__(self):
-        super(LZ09_F6, self).__init__(number_of_variables=10, number_of_objectives=3, number_of_constraints=0,
-                                      dtype=1, ltype=32, ptype=31)
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F6'
    
+
    
+[docs]
+class LZ09_F6(LZ09):
+    def __init__(self, number_of_variables=10):
+        super(LZ09_F6, self).__init__(
+            number_of_variables, dtype=1, ltype=32, ptype=31
+        )
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)", "f(z)"]
 
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-
    [docs]class LZ09_F7(LZ09):
 
-    def __init__(self):
-        super(LZ09_F7, self).__init__(number_of_variables=10, number_of_objectives=2, number_of_constraints=0,
-                                      dtype=3, ltype=21, ptype=21)
+
    
+[docs]
+    def name(self):
+        return "LZ09_F6"
    
+
    
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F7'
    
 
 
-
    [docs]class LZ09_F8(LZ09):
+
    
+[docs]
+class LZ09_F7(LZ09):
+    def __init__(self, number_of_variables=10):
+        super(LZ09_F7, self).__init__(
+            number_of_variables, dtype=3, ltype=21, ptype=21
+        )
+
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)"]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
+
+
    
+[docs]
+    def name(self):
+        return "LZ09_F7"
    
+
    
 
-    def __init__(self):
-        super(LZ09_F8, self).__init__(number_of_variables=10, number_of_objectives=2, number_of_constraints=0,
-                                      dtype=4, ltype=21, ptype=21)
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F8'
    
 
+
    
+[docs]
+class LZ09_F8(LZ09):
+    def __init__(self, number_of_variables=10):
+        super(LZ09_F8, self).__init__(
+            number_of_variables, dtype=4, ltype=21, ptype=21
+        )
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)"]
 
-
    [docs]class LZ09_F9(LZ09):
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-    def __init__(self):
-        super(LZ09_F9, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
-                                      dtype=1, ltype=22, ptype=22)
 
-
    [docs]    def get_name(self):
-        return 'LZ09_F9'
    
+
    
+[docs]
+    def name(self):
+        return "LZ09_F8"
    
+
    
+
+
+
+
    
+[docs]
+class LZ09_F9(LZ09):
+    def __init__(self, number_of_variables=30):
+        super(LZ09_F9, self).__init__(
+            number_of_variables, dtype=1, ltype=22, ptype=22
+        )
+
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["f(x)", "f(y)"]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
+
+
    
+[docs]
+    def name(self):
+        return "LZ09_F9"
    
+
    
+
 
    
 
           
    
@@ -486,8 +622,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.lz09
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.multiobjective.unconstrained — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
    • 
-          
  • Module code »
    •  
+        
  • jMetalPy 1.7.0 documentation »
    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.unconstrained
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -110,10 +107,15 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.problem.multiobjective.unconstrained
     import random
-from math import sqrt, exp, pow, sin
+from math import exp, pow, sin, sqrt
 
-from jmetal.core.problem import FloatProblem, BinaryProblem
-from jmetal.core.solution import FloatSolution, BinarySolution
+from jmetal.core.problem import BinaryProblem, FloatProblem, Problem
+from jmetal.core.solution import (
+    BinarySolution,
+    CompositeSolution,
+    FloatSolution,
+    IntegerSolution,
+)
 
 """
 .. module:: constrained
@@ -124,31 +126,43 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    """
 
 
-
    [docs]class Kursawe(FloatProblem):
-    """ Class representing problem Kursawe. """
+
    
+[docs]
+class Kursawe(FloatProblem):
+    """Class representing problem Kursawe."""
 
     def __init__(self, number_of_variables: int = 3):
         super(Kursawe, self).__init__()
-        self.number_of_objectives = 2
-        self.number_of_variables = number_of_variables
-        self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
 
         self.lower_bound = [-5.0 for _ in range(number_of_variables)]
         self.upper_bound = [5.0 for _ in range(number_of_variables)]
 
-        FloatSolution.lower_bound = self.lower_bound
-        FloatSolution.upper_bound = self.upper_bound
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        fx = [0.0 for _ in range(self.number_of_objectives)]
-        for i in range(self.number_of_variables - 1):
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        fx = [0.0 for _ in range(self.number_of_objectives())]
+        for i in range(self.number_of_variables() - 1):
             xi = solution.variables[i] * solution.variables[i]
             xj = solution.variables[i + 1] * solution.variables[i + 1]
             aux = -0.2 * sqrt(xi + xj)
             fx[0] += -10 * exp(aux)
+
+        for i in range(self.number_of_variables()):
             fx[1] += pow(abs(solution.variables[i]), 0.8) + 5.0 * sin(pow(solution.variables[i], 3.0))
 
         solution.objectives[0] = fx[0]
@@ -156,85 +170,129 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Kursawe'
    
+
+
    
+[docs]
+    def name(self):
+        return "Kursawe"
    
+
    
 
 
-
    [docs]class Fonseca(FloatProblem):
 
+
    
+[docs]
+class Fonseca(FloatProblem):
     def __init__(self):
         super(Fonseca, self).__init__()
-        self.number_of_variables = 3
-        self.number_of_objectives = 2
-        self.number_of_constraints = 0
-
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
+
+        number_of_variables = 3
+
+        self.lower_bound = number_of_variables * [-4]
+        self.upper_bound = number_of_variables * [4]
 
-        self.lower_bound = self.number_of_variables * [-4]
-        self.upper_bound = self.number_of_variables * [4]
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-        FloatSolution.lower_bound = self.lower_bound
-        FloatSolution.upper_bound = self.upper_bound
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        n = self.number_of_variables
-        solution.objectives[0] = 1 - exp(-sum([(x - 1.0 / n ** 0.5) ** 2 for x in solution.variables]))
-        solution.objectives[1] = 1 - exp(-sum([(x + 1.0 / n ** 0.5) ** 2 for x in solution.variables]))
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        n = self.number_of_variables()
+        solution.objectives[0] = 1 - exp(-sum([(x - 1.0 / n**0.5) ** 2 for x in solution.variables]))
+        solution.objectives[1] = 1 - exp(-sum([(x + 1.0 / n**0.5) ** 2 for x in solution.variables]))
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Fonseca'
    
+
+
    
+[docs]
+    def name(self):
+        return "Fonseca"
    
+
    
 
 
-
    [docs]class Schaffer(FloatProblem):
 
+
    
+[docs]
+class Schaffer(FloatProblem):
     def __init__(self):
         super(Schaffer, self).__init__()
-        self.number_of_variables = 1
-        self.number_of_objectives = 2
-        self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)']
+        self.obj_labels = ["f(x)", "f(y)"]
+
+        self.lower_bound = [-1000]
+        self.upper_bound = [1000]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
 
-        self.lower_bound = [-100000]
-        self.upper_bound = [100000]
 
-        FloatSolution.lower_bound = self.lower_bound
-        FloatSolution.upper_bound = self.upper_bound
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         value = solution.variables[0]
 
-        solution.objectives[0] = value ** 2
+        solution.objectives[0] = value**2
         solution.objectives[1] = (value - 2) ** 2
 
         return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Schaffer'
    
+
+
    
+[docs]
+    def name(self):
+        return "Schaffer"
    
+
    
 
 
-
    [docs]class Viennet2(FloatProblem):
 
+
    
+[docs]
+class Viennet2(FloatProblem):
     def __init__(self):
         super(Viennet2, self).__init__()
-        self.number_of_variables = 2
-        self.number_of_objectives = 3
-        self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['f(x)', 'f(y)', 'f(z)']
+        self.obj_labels = ["f(x)", "f(y)", "f(z)"]
+
+        number_of_variables = 2
+        self.lower_bound = number_of_variables * [-4]
+        self.upper_bound = number_of_variables * [4]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
 
-        self.lower_bound = self.number_of_variables * [-4]
-        self.upper_bound = self.number_of_variables * [4]
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
 
-        FloatSolution.lower_bound = self.lower_bound
-        FloatSolution.upper_bound = self.upper_bound
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         x0 = solution.variables[0]
         x1 = solution.variables[1]
 
@@ -248,14 +306,20 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    return solution
    
 
-
    [docs]    def get_name(self):
-        return 'Viennet2'
    
+
+
    
+[docs]
+    def name(self):
+        return "Viennet2"
    
+
    
 
 
-
    [docs]class SubsetSum(BinaryProblem):
 
+
    
+[docs]
+class SubsetSum(BinaryProblem):
     def __init__(self, C: int, W: list):
-        """ The goal is to find a subset S of W whose elements sum is closest to (without exceeding) C.
+        """The goal is to find a subset S of W whose elements sum is closest to (without exceeding) C.
 
         :param C: Large integer.
         :param W: Set of non-negative integers."""
@@ -269,9 +333,11 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    self.number_of_constraints = 0
 
         self.obj_directions = [self.MAXIMIZE, self.MINIMIZE]
-        self.obj_labels = ['Sum', 'No. of Objects']
+        self.obj_labels = ["Sum", "No. of Objects"]
 
-
    [docs]    def evaluate(self, solution: BinarySolution) -> BinarySolution:
+
    
+[docs]
+    def evaluate(self, solution: BinarySolution) -> BinarySolution:
         total_sum = 0.0
         number_of_objects = 0
 
@@ -291,31 +357,61 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    return solution
    
 
-
    [docs]    def create_solution(self) -> BinarySolution:
-        new_solution = BinarySolution(number_of_variables=self.number_of_variables,
-                                      number_of_objectives=self.number_of_objectives)
-        new_solution.variables[0] = \
-            [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)]
+
+
    
+[docs]
+    def create_solution(self) -> BinarySolution:
+        new_solution = BinarySolution(
+            number_of_variables=self.number_of_variables, number_of_objectives=self.number_of_objectives
+        )
+        new_solution.variables[0] = [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)]
 
         return new_solution
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'Subset Sum'
    
+
+
    
+[docs]
+    def name(self) -> str:
+        return "Subset Sum"
    
+
    
+
 
 
-
    [docs]class OneZeroMax(BinaryProblem):
+
    
+[docs]
+class OneZeroMax(BinaryProblem):
+    """ The implementation of the OneZeroMax problems defines a single binary variable. This variable
+    will contain the bit string representing the solutions.
 
+    """
     def __init__(self, number_of_bits: int = 256):
         super(OneZeroMax, self).__init__()
-        self.number_of_bits = number_of_bits
-        self.number_of_objectives = 2
-        self.number_of_variables = 1
-        self.number_of_constraints = 0
+        self.number_of_bits_per_variable = [number_of_bits]
+
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["Zeroes", "Ones"]
+
+
    
+[docs]
+    def number_of_variables(self) -> int:
+        return 1
    
 
-        self.obj_directions = [self.MINIMIZE]
-        self.obj_labels = ['Ones']
 
-
    [docs]    def evaluate(self, solution: BinarySolution) -> BinarySolution:
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return 2
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: BinarySolution) -> BinarySolution:
         counter_of_ones = 0
         counter_of_zeroes = 0
         for bits in solution.variables[0]:
@@ -329,15 +425,96 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    return solution
    
 
-
    [docs]    def create_solution(self) -> BinarySolution:
-        new_solution = BinarySolution(number_of_variables=self.number_of_variables,
-                                      number_of_objectives=self.number_of_objectives)
-        new_solution.variables[0] = \
-            [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)]
+
+
    
+[docs]
+    def create_solution(self) -> BinarySolution:
+        new_solution = BinarySolution(
+            number_of_variables=self.number_of_variables(), number_of_objectives=self.number_of_objectives()
+        )
+        new_solution.variables[0] = [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits_per_variable[0])]
         return new_solution
    
 
-
    [docs]    def get_name(self) -> str:
-        return 'OneZeroMax'
    
+
+
    
+[docs]
+    def name(self) -> str:
+        return "OneZeroMax"
    
+
    
+
+
+
+
    
+[docs]
+class MixedIntegerFloatProblem(Problem):
+    def __init__(
+        self,
+        number_of_integer_variables=10,
+        number_of_float_variables=10,
+        n=100,
+        m=-100,
+        lower_bound=-1000,
+        upper_bound=1000,
+    ):
+        super(MixedIntegerFloatProblem, self).__init__()
+        self.number_of_objectives = 2
+        self.number_of_variables = 2
+        self.number_of_constraints = 0
+
+        self.n = n
+        self.m = m
+
+        self.float_lower_bound = [lower_bound for _ in range(number_of_float_variables)]
+        self.float_upper_bound = [upper_bound for _ in range(number_of_float_variables)]
+        self.int_lower_bound = [lower_bound for _ in range(number_of_integer_variables)]
+        self.int_upper_bound = [upper_bound for _ in range(number_of_integer_variables)]
+
+        self.obj_directions = [self.MINIMIZE]
+        self.obj_labels = ["Ones"]
+
+
    
+[docs]
+    def evaluate(self, solution: CompositeSolution) -> CompositeSolution:
+        distance_to_n = sum([abs(self.n - value) for value in solution.variables[0].variables])
+        distance_to_m = sum([abs(self.m - value) for value in solution.variables[0].variables])
+
+        distance_to_n += sum([abs(self.n - value) for value in solution.variables[1].variables])
+        distance_to_m += sum([abs(self.m - value) for value in solution.variables[1].variables])
+
+        solution.objectives[0] = distance_to_n
+        solution.objectives[1] = distance_to_m
+
+        return solution
    
+
+
+
    
+[docs]
+    def create_solution(self) -> CompositeSolution:
+        integer_solution = IntegerSolution(
+            self.int_lower_bound, self.int_upper_bound, self.number_of_objectives, self.number_of_constraints
+        )
+        float_solution = FloatSolution(
+            self.float_lower_bound, self.float_upper_bound, self.number_of_objectives, self.number_of_constraints
+        )
+
+        float_solution.variables = [
+            random.uniform(self.float_lower_bound[i] * 1.0, self.float_upper_bound[i] * 1.0)
+            for i in range(len(self.float_lower_bound))
+        ]
+        integer_solution.variables = [
+            random.uniform(self.int_lower_bound[i], self.int_upper_bound[i])
+            for i in range(len(self.int_lower_bound))
+        ]
+
+        return CompositeSolution([integer_solution, float_solution])
    
+
+
+
    
+[docs]
+    def name(self) -> str:
+        return "Mixed Integer Float Problem"
    
+
    
+
 
    
 
           
    
@@ -354,8 +531,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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-          
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+        
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    • 
+          
  • Module code »
    • 
+        
  • jmetal.problem.multiobjective.unconstrained
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.multiobjective.zdt — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
           modules |
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  • jMetalPy 1.5.3 documentation »
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+          
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  • jmetal.problem.multiobjective.zdt
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -109,10 +106,11 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.zdt
    -from math import sqrt, pow, sin, pi, cos
+import random
+from math import cos, pi, pow, sin, sqrt, exp
 
-from jmetal.core.problem import FloatProblem
-from jmetal.core.solution import FloatSolution
+from jmetal.core.problem import FloatProblem, BinaryProblem
+from jmetal.core.solution import FloatSolution, BinarySolution
 
 """
 .. module:: ZDT
@@ -123,28 +121,46 @@ 
    Source code for jmetal.problem.multiobjective.zdt
    """
 
 
-
    [docs]class ZDT1(FloatProblem):
-    """ Problem ZDT1.
+
    
+[docs]
+class ZDT1(FloatProblem):
+    """Problem ZDT1.
 
     .. note:: Bi-objective unconstrained problem. The default number of variables is 30.
     .. note:: Continuous problem having a convex Pareto front
     """
 
-    def __init__(self, number_of_variables: int=30):
-        """ :param number_of_variables: Number of decision variables of the problem.
-        """
+    def __init__(self, number_of_variables: int = 30):
+        """:param number_of_variables: Number of decision variables of the problem."""
         super(ZDT1, self).__init__()
-        self.number_of_variables = number_of_variables
-        self.number_of_objectives = 2
-        self.number_of_constraints = 0
 
         self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
-        self.obj_labels = ['x', 'y']
+        self.obj_labels = ["x", "y"]
+
+        self.lower_bound = number_of_variables * [0.0]
+        self.upper_bound = number_of_variables * [1.0]
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return len(self.obj_directions)
    
+
+    
+
    
+[docs]
+    def number_of_variables(self) -> int:
+        return len(self.lower_bound)
    
 
-        self.lower_bound = self.number_of_variables * [0.0]
-        self.upper_bound = self.number_of_variables * [1.0]
 
-
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
         g = self.eval_g(solution)
         h = self.eval_h(solution.variables[0], g)
 
@@ -153,106 +169,296 @@ 
    Source code for jmetal.problem.multiobjective.zdt
    return solution
    
 
-
    [docs]    def eval_g(self, solution: FloatSolution):
+
+
    
+[docs]
+    def eval_g(self, solution: FloatSolution):
         g = sum(solution.variables) - solution.variables[0]
 
-        constant = 9.0 / (solution.number_of_variables - 1)
+        constant = 9.0 / (len(solution.variables) - 1)
 
         return constant * g + 1.0
    
 
-
    [docs]    def eval_h(self, f: float, g: float) -> float:
+
+
    
+[docs]
+    def eval_h(self, f: float, g: float) -> float:
         return 1.0 - sqrt(f / g)
    
 
-
    [docs]    def get_name(self):
-        return 'ZDT1'
    
+
+
    
+[docs]
+    def name(self):
+        return "ZDT1"
    
+
    
+
+
+
+class ZDT1Modified(ZDT1):
+    """Problem ZDT1Modified.
+
+    .. note:: Version including a loop for increasing the computing time of the evaluation functions.
+    """
+
+    def __init__(self, number_of_variables=30):
+        super(ZDT1Modified, self).__init__(number_of_variables)
+
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        s: float = 0.0
+        for i in range(1000):
+            for j in range(10000):
+                s += i * 0.235 / 1.234 + 1.23525 * j
+        return super().evaluate(solution)
 
 
-
    [docs]class ZDT2(ZDT1):
-    """ Problem ZDT2.
+
    
+[docs]
+class ZDT1Modified(ZDT1):
+    """ Problem ZDT1Modified.
+
+    .. note:: Version including a loop for increasing the computing time of the evaluation functions.
+    """
+    def __init__(self, number_of_variables = 30):
+        super(ZDT1Modified, self).__init__(number_of_variables)
+
+
    
+[docs]
+    def evaluate(self, solution:FloatSolution) -> FloatSolution:
+        s: float = 0.0
+        for i in range(1000):
+            for j in range(10000):
+                s += i * 0.235 / 1.234 + 1.23525 * j
+        return super().evaluate(solution)
    
+
    
+
+
+
+
    
+[docs]
+class ZDT2(ZDT1):
+    """Problem ZDT2.
 
     .. note:: Bi-objective unconstrained problem. The default number of variables is 30.
     .. note:: Continuous problem having a non-convex Pareto front
     """
 
-
    [docs]    def eval_h(self, f: float, g: float) -> float:
+
    
+[docs]
+    def eval_h(self, f: float, g: float) -> float:
         return 1.0 - pow(f / g, 2.0)
    
 
-
    [docs]    def get_name(self):
-        return 'ZDT2'
    
+
+
    
+[docs]
+    def name(self):
+        return "ZDT2"
    
+
    
+
 
 
-
    [docs]class ZDT3(ZDT1):
-    """ Problem ZDT3.
+
    
+[docs]
+class ZDT3(ZDT1):
+    """Problem ZDT3.
 
     .. note:: Bi-objective unconstrained problem. The default number of variables is 30.
     .. note:: Continuous problem having a partitioned Pareto front
     """
-
    [docs]    def eval_h(self, f: float, g: float) -> float:
+
+
    
+[docs]
+    def eval_h(self, f: float, g: float) -> float:
         return 1.0 - sqrt(f / g) - (f / g) * sin(10.0 * f * pi)
    
 
-
    [docs]    def get_name(self):
-        return 'ZDT3'
    
+
+
    
+[docs]
+    def name(self):
+        return "ZDT3"
    
+
    
 
 
-
    [docs]class ZDT4(ZDT1):
-    """ Problem ZDT4.
+
+
    
+[docs]
+class ZDT4(ZDT1):
+    """Problem ZDT4.
 
     .. note:: Bi-objective unconstrained problem. The default number of variables is 10.
     .. note:: Continuous multi-modal problem having a convex Pareto front
     """
 
-    def __init__(self, number_of_variables: int=10):
-        """ :param number_of_variables: Number of decision variables of the problem.
-        """
-        super(ZDT4, self).__init__(number_of_variables=number_of_variables)
-        self.lower_bound = self.number_of_variables * [-5.0]
-        self.upper_bound = self.number_of_variables * [5.0]
+    def __init__(self, number_of_variables: int = 10):
+        """:param number_of_variables: Number of decision variables of the problem."""
+        super(ZDT4, self).__init__()
+        self.lower_bound = number_of_variables * [-5.0]
+        self.upper_bound = number_of_variables * [5.0]
         self.lower_bound[0] = 0.0
         self.upper_bound[0] = 1.0
 
-
    [docs]    def eval_g(self, solution: FloatSolution):
+
    
+[docs]
+    def eval_g(self, solution: FloatSolution):
         g = 0.0
 
-        for i in range(1, solution.number_of_variables):
+        for i in range(1, len(solution.variables)):
             g += pow(solution.variables[i], 2.0) - 10.0 * cos(4.0 * pi * solution.variables[i])
 
-        g += 1.0 + 10.0 * (solution.number_of_variables - 1)
+        g += 1.0 + 10.0 * (len(solution.variables) - 1)
 
         return g
    
 
-
    [docs]    def eval_h(self, f: float, g: float) -> float:
+
+
    
+[docs]
+    def eval_h(self, f: float, g: float) -> float:
         return 1.0 - sqrt(f / g)
    
 
-
    [docs]    def get_name(self):
-        return 'ZDT4'
    
+
+
    
+[docs]
+    def name(self):
+        return "ZDT4"
    
+
    
+
+
+
+
    
+[docs]
+class ZDT5(BinaryProblem):
+    """Problem ZDT5.
+
+    .. note:: Bi-objective binary unconstrained problem. The default number of variables is 11.
+    """
+
+    def __init__(self, number_of_variables: int = 11):
+        """:param number_of_bits: Number of bits of each variable of the problem."""
+        super(ZDT5, self).__init__()
+
+        self.number_of_bits_per_variable = [5 for _ in range(0, number_of_variables)]
+        self.number_of_bits_per_variable[0] = 30
+
+        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
+        self.obj_labels = ["x", "y"]
+
+
    
+[docs]
+    def number_of_variables(self) -> int:
+        return len(self.number_of_bits_per_variable)
    
+
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return 2
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
+
+
+
    
+[docs]
+    def evaluate(self, solution: BinarySolution) -> BinarySolution:
+        solution.objectives[0] = 1.0 + solution.cardinality(0)
+
+        g = self.eval_g(solution)
+        h = 1.0 / solution.objectives[0]
+
+        solution.objectives[1] = h * g
+
+        return solution
    
+
+
+
    
+[docs]
+    def eval_g(self, solution: BinarySolution):
+        result = 0.0
+        for i in range(1, len(solution.variables)):
+            result = result + self.eval_v(solution.cardinality(i))
+
+        return result
    
 
 
-
    [docs]class ZDT6(ZDT1):
-    """ Problem ZDT6.
+
    
+[docs]
+    def eval_v(self, value):
+        if value < 5.0:
+            return 2.0 + value
+        else:
+            return 1.0
    
+
+
+
    
+[docs]
+    def create_solution(self) -> BinarySolution:
+        new_solution = BinarySolution(number_of_variables=self.number_of_variables(), number_of_objectives=2)
+        for i in range(self.number_of_variables()):
+            new_solution.variables[i] = [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits_per_variable[i])]
+        return new_solution
    
+
+
+
    
+[docs]
+    def name(self):
+        return "ZDT5"
    
+
    
+
+
+
+
    
+[docs]
+class ZDT6(ZDT1):
+    """Problem ZDT6.
 
     .. note:: Bi-objective unconstrained problem. The default number of variables is 10.
     .. note:: Continuous problem having a non-convex Pareto front
     """
 
-    def __init__(self, number_of_variables: int=10):
-        """ :param number_of_variables: Number of decision variables of the problem.
-        """
+    def __init__(self, number_of_variables: int = 10):
+        """:param number_of_variables: Number of decision variables of the problem."""
         super(ZDT6, self).__init__(number_of_variables=number_of_variables)
 
-
    [docs]    def eval_g(self, solution: FloatSolution):
+
    
+[docs]
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        solution.objectives[0] = (
+            1.0 - exp(-4.0 * solution.variables[0]) * (sin(6.0 * pi * solution.variables[0])) ** 6.0
+        )
+
+        g = self.eval_g(solution)
+        h = self.eval_h(solution.objectives[0], g)
+        solution.objectives[1] = h * g
+
+        return solution
    
+
+
+
    
+[docs]
+    def eval_g(self, solution: FloatSolution):
         g = sum(solution.variables) - solution.variables[0]
-        g = g / (solution.number_of_variables - 1)
+        g = g / (len(solution.variables) - 1)
         g = pow(g, 0.25)
         g = 9.0 * g
         g = 1.0 + g
 
         return g
    
 
-
    [docs]    def eval_h(self, f: float, g: float) -> float:
+
+
    
+[docs]
+    def eval_h(self, f: float, g: float) -> float:
         return 1.0 - pow(f / g, 2.0)
    
 
-
    [docs]    def get_name(self):
-        return 'ZDT6'
    
+
+
    
+[docs]
+    def name(self):
+        return "ZDT6"
    
+
    
+
 
    
 
           
    
@@ -269,8 +475,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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+        
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+          
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+        
  • jmetal.problem.multiobjective.zdt
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.singleobjective.knapsack — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
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    • 
-        
  • jMetalPy 1.5.3 documentation »
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+        
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+          
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+        
  • jmetal.problem.singleobjective.knapsack
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -125,11 +122,20 @@ 
    Source code for jmetal.problem.singleobjective.knapsack
    """
 
 
-
    [docs]class Knapsack(BinaryProblem):
-    """ Class representing Knapsack Problem. """
-
-    def __init__(self, number_of_items: int = 50, capacity: float = 1000, weights: list = None,
-                 profits: list = None, from_file: bool = False, filename: str = None):
+
    
+[docs]
+class Knapsack(BinaryProblem):
+    """Class representing Knapsack Problem."""
+
+    def __init__(
+        self,
+        number_of_items: int = 50,
+        capacity: float = 1000,
+        weights: list = None,
+        profits: list = None,
+        from_file: bool = False,
+        filename: str = None,
+    ):
         super(Knapsack, self).__init__()
 
         if from_file:
@@ -140,13 +146,28 @@ 
    Source code for jmetal.problem.singleobjective.knapsack
    self.profits = profits
             self.number_of_bits = number_of_items
 
-        self.number_of_variables = 1
         self.obj_directions = [self.MAXIMIZE]
-        self.number_of_objectives = 1
-        self.number_of_constraints = 1
+
+
    
+[docs]
+    def number_of_variables(self) -> int:
+        return 1
    
+
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return 1
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 1
    
+
 
     def __read_from_file(self, filename: str):
-        """
+        """
         This function reads a Knapsack Problem instance from a file.
         It expects the following format:
 
@@ -159,7 +180,7 @@ 
    Source code for jmetal.problem.singleobjective.knapsack
            """
 
         if filename is None:
-            raise FileNotFoundError('Filename can not be None')
+            raise FileNotFoundError("Filename can not be None")
 
         with open(filename) as file:
             lines = file.readlines()
@@ -168,12 +189,14 @@ 
    Source code for jmetal.problem.singleobjective.knapsack
    self.number_of_bits = int(data[0][0])
             self.capacity = float(data[1][0])
 
-            weights_and_profits = np.asfarray(data[2:], dtype=np.float32)
+            weights_and_profits = np.asarray(data[2:], dtype=np.float32)
 
             self.weights = weights_and_profits[:, 0]
             self.profits = weights_and_profits[:, 1]
 
-
    [docs]    def evaluate(self, solution: BinarySolution) -> BinarySolution:
+
    
+[docs]
+    def evaluate(self, solution: BinarySolution) -> BinarySolution:
         total_profits = 0.0
         total_weigths = 0.0
 
@@ -188,18 +211,25 @@ 
    Source code for jmetal.problem.singleobjective.knapsack
    solution.objectives[0] = -1.0 * total_profits
         return solution
    
 
-
    [docs]    def create_solution(self) -> BinarySolution:
-        new_solution = BinarySolution(number_of_variables=self.number_of_variables,
-                                      number_of_objectives=self.number_of_objectives)
 
-        new_solution.variables[0] = \
-            [True if random.randint(0, 1) == 0 else False for _ in range(
-                self.number_of_bits)]
+
    
+[docs]
+    def create_solution(self) -> BinarySolution:
+        new_solution = BinarySolution(
+            number_of_variables=self.number_of_variables(), number_of_objectives=self.number_of_objectives()
+        )
+
+        new_solution.variables[0] = [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)]
 
         return new_solution
    
 
-
    [docs]    def get_name(self):
-        return 'Knapsack'
    
+
+
    
+[docs]
+    def name(self):
+        return "Knapsack"
    
+
    
+
 
    
 
           
    
@@ -216,8 +246,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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  • Module code »
    •  
+        
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+          
  • Module code »
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+        
  • jmetal.problem.singleobjective.knapsack
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.problem.singleobjective.tsp — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
  • 
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-        
  • jMetalPy 1.5.3 documentation »
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  • Module code »
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+        
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+          
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+        
  • jmetal.problem.singleobjective.tsp
    •  
       
     
    
     
    
@@ -51,7 +48,7 @@ 
    Navigation
    
   
    
     
    
 
@@ -125,23 +122,37 @@ 
    Source code for jmetal.problem.singleobjective.tsp
    """
 
 
-
    [docs]class TSP(PermutationProblem):
-    """ Class representing TSP Problem. """
+
    
+[docs]
+class TSP(PermutationProblem):
+    """Class representing TSP Problem."""
 
     def __init__(self, instance: str = None):
         super(TSP, self).__init__()
 
-        distance_matrix, number_of_cities = self.__read_from_file(instance)
+        self.distance_matrix, self.number_of_cities = self.__read_from_file(instance)
+        self.obj_directions = [self.MINIMIZE]
 
-        self.distance_matrix = distance_matrix
+
    
+[docs]
+    def number_of_variables(self) -> int:
+        return self.number_of_cities
    
+
+
+
    
+[docs]
+    def number_of_objectives(self) -> int:
+        return 1
    
+
+
+
    
+[docs]
+    def number_of_constraints(self) -> int:
+        return 0
    
 
-        self.obj_directions = [self.MINIMIZE]
-        self.number_of_variables = number_of_cities
-        self.number_of_objectives = 1
-        self.number_of_constraints = 0
 
     def __read_from_file(self, filename: str):
-        """
+        """
         This function reads a TSP Problem instance from a file.
 
         :param filename: File which describes the instance.
@@ -149,24 +160,24 @@ 
    Source code for jmetal.problem.singleobjective.tsp
            """
 
         if filename is None:
-            raise FileNotFoundError('Filename can not be None')
+            raise FileNotFoundError("Filename can not be None")
 
         with open(filename) as file:
             lines = file.readlines()
             data = [line.lstrip() for line in lines if line != ""]
 
-            dimension = re.compile(r'[^\d]+')
+            dimension = re.compile(r"[^\d]+")
 
             for item in data:
-                if item.startswith('DIMENSION'):
-                    dimension = int(dimension.sub('', item))
+                if item.startswith("DIMENSION"):
+                    dimension = int(dimension.sub("", item))
                     break
 
             c = [-1.0] * (2 * dimension)
 
             for item in data:
                 if item[0].isdigit():
-                    j, city_a, city_b = [int(x.strip()) for x in item.split(' ')]
+                    j, city_a, city_b = [int(x.strip()) for x in item.split(" ")]
                     c[2 * (j - 1)] = city_a
                     c[2 * (j - 1) + 1] = city_b
 
@@ -183,10 +194,12 @@ 
    Source code for jmetal.problem.singleobjective.tsp
    return matrix, dimension
 
-
    [docs]    def evaluate(self, solution: PermutationSolution) -> PermutationSolution:
+
    
+[docs]
+    def evaluate(self, solution: PermutationSolution) -> PermutationSolution:
         fitness = 0
 
-        for i in range(self.number_of_variables - 1):
+        for i in range(self.number_of_variables() - 1):
             x = solution.variables[i]
             y = solution.variables[i + 1]
 
@@ -199,19 +212,24 @@ 
    Source code for jmetal.problem.singleobjective.tsp
    return solution
    
 
-
    [docs]    def create_solution(self) -> PermutationSolution:
-        new_solution = PermutationSolution(number_of_variables=self.number_of_variables,
-                                           number_of_objectives=self.number_of_objectives)
-        new_solution.variables = random.sample(range(self.number_of_variables), k=self.number_of_variables)
+
+
    
+[docs]
+    def create_solution(self) -> PermutationSolution:
+        new_solution = PermutationSolution(
+            number_of_variables=self.number_of_variables(), number_of_objectives=self.number_of_objectives()
+        )
+        new_solution.variables = random.sample(range(self.number_of_variables()), k=self.number_of_variables())
 
         return new_solution
    
 
-    @property
-    def number_of_cities(self):
-        return self.number_of_variables
 
-
    [docs]    def get_name(self):
-        return 'Symmetric TSP'
    
+
    
+[docs]
+    def name(self):
+        return "Symmetric TSP"
    
+
    
+
    @@ -228,8 +246,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Module code »
    • +
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    • +
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    • +
  • jmetal.problem.singleobjective.tsp
    • - - - - - - - + jmetal.problem.singleobjective.unconstrained — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

    Navigation

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  • jmetal.problem.singleobjective.unconstrained
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -124,19 +121,41 @@

    Source code for jmetal.problem.singleobjective.unconstrained

    """ -
    [docs]class OneMax(BinaryProblem): +
    +[docs] +class OneMax(BinaryProblem): + """ The implementation of the OneMax problems defines a single binary variable. This variable + will contain the bit string representing the solutions. + """ def __init__(self, number_of_bits: int = 256): super(OneMax, self).__init__() - self.number_of_bits = number_of_bits - self.number_of_objectives = 1 - self.number_of_variables = 1 - self.number_of_constraints = 0 + self.number_of_bits_per_variable = [number_of_bits] self.obj_directions = [self.MINIMIZE] - self.obj_labels = ['Ones'] + self.obj_labels = ["Ones"] + +
    +[docs] + def number_of_variables(self) -> int: + return 1
    + + +
    +[docs] + def number_of_objectives(self) -> int: + return 1
    -
    [docs] def evaluate(self, solution: BinarySolution) -> BinarySolution: + +
    +[docs] + def number_of_constraints(self) -> int: + return 0
    + + +
    +[docs] + def evaluate(self, solution: BinarySolution) -> BinarySolution: counter_of_ones = 0 for bits in solution.variables[0]: if bits: @@ -146,26 +165,31 @@

    Source code for jmetal.problem.singleobjective.unconstrained

    return solution
    -
    [docs] def create_solution(self) -> BinarySolution: + +
    +[docs] + def create_solution(self) -> BinarySolution: new_solution = BinarySolution(number_of_variables=1, number_of_objectives=1) - new_solution.variables[0] = \ - [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)] + new_solution.variables[0] = [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits_per_variable[0])] return new_solution
    -
    [docs] def get_name(self) -> str: - return 'OneMax'
    + +
    +[docs] + def name(self) -> str: + return "OneMax"
    +
    -
    [docs]class Sphere(FloatProblem): +
    +[docs] +class Sphere(FloatProblem): def __init__(self, number_of_variables: int = 10): super(Sphere, self).__init__() - self.number_of_objectives = 1 - self.number_of_variables = number_of_variables - self.number_of_constraints = 0 self.obj_directions = [self.MINIMIZE] - self.obj_labels = ['f(x)'] + self.obj_labels = ["f(x)"] self.lower_bound = [-5.12 for _ in range(number_of_variables)] self.upper_bound = [5.12 for _ in range(number_of_variables)] @@ -173,7 +197,21 @@

    Source code for jmetal.problem.singleobjective.unconstrained

    FloatSolution.lower_bound = self.lower_bound FloatSolution.upper_bound = self.upper_bound -
    [docs] def evaluate(self, solution: FloatSolution) -> FloatSolution: +
    +[docs] + def number_of_objectives(self) -> int: + return 1
    + + +
    +[docs] + def number_of_constraints(self) -> int: + return 0
    + + +
    +[docs] + def evaluate(self, solution: FloatSolution) -> FloatSolution: total = 0.0 for x in solution.variables: total += x * x @@ -182,20 +220,23 @@

    Source code for jmetal.problem.singleobjective.unconstrained

    return solution
    -
    [docs] def get_name(self) -> str: - return 'Sphere'
    + +
    +[docs] + def name(self) -> str: + return "Sphere"
    +
    -
    [docs]class Rastrigin(FloatProblem): +
    +[docs] +class Rastrigin(FloatProblem): def __init__(self, number_of_variables: int = 10): super(Rastrigin, self).__init__() - self.number_of_objectives = 1 - self.number_of_variables = number_of_variables - self.number_of_constraints = 0 self.obj_directions = [self.MINIMIZE] - self.obj_labels = ['f(x)'] + self.obj_labels = ["f(x)"] self.lower_bound = [-5.12 for _ in range(number_of_variables)] self.upper_bound = [5.12 for _ in range(number_of_variables)] @@ -203,26 +244,46 @@

    Source code for jmetal.problem.singleobjective.unconstrained

    FloatSolution.lower_bound = self.lower_bound FloatSolution.upper_bound = self.upper_bound -
    [docs] def evaluate(self, solution: FloatSolution) -> FloatSolution: +
    +[docs] + def number_of_objectives(self) -> int: + return 1
    + + +
    +[docs] + def number_of_constraints(self) -> int: + return 0
    + + +
    +[docs] + def evaluate(self, solution: FloatSolution) -> FloatSolution: a = 10.0 - result = a * solution.number_of_variables + result = a * len(solution.variables) x = solution.variables - for i in range(solution.number_of_variables): + for i in range(len(solution.variables)): result += x[i] * x[i] - a * math.cos(2 * math.pi * x[i]) solution.objectives[0] = result return solution
    -
    [docs] def get_name(self) -> str: - return 'Rastrigin'
    + +
    +[docs] + def name(self) -> str: + return "Rastrigin"
    +
    -
    [docs]class SubsetSum(BinaryProblem): +
    +[docs] +class SubsetSum(BinaryProblem): def __init__(self, C: int, W: list): - """ The goal is to find a subset S of W whose elements sum is closest to (without exceeding) C. + """The goal is to find a subset S of W whose elements sum is closest to (without exceeding) C. :param C: Large integer. :param W: Set of non-negative integers.""" @@ -231,14 +292,30 @@

    Source code for jmetal.problem.singleobjective.unconstrained

    self.W = W self.number_of_bits = len(self.W) - self.number_of_objectives = 1 - self.number_of_variables = 1 - self.number_of_constraints = 0 self.obj_directions = [self.MAXIMIZE] - self.obj_labels = ['Sum'] + self.obj_labels = ["Sum"] + +
    +[docs] + def number_of_variables(self) -> int: + return 1
    + +
    +[docs] + def number_of_objectives(self) -> int: + return 1
    -
    [docs] def evaluate(self, solution: BinarySolution) -> BinarySolution: + +
    +[docs] + def number_of_constraints(self) -> int: + return 0
    + + +
    +[docs] + def evaluate(self, solution: BinarySolution) -> BinarySolution: total_sum = 0.0 for index, bits in enumerate(solution.variables[0]): @@ -255,16 +332,24 @@

    Source code for jmetal.problem.singleobjective.unconstrained

    return solution
    -
    [docs] def create_solution(self) -> BinarySolution: - new_solution = BinarySolution(number_of_variables=self.number_of_variables, - number_of_objectives=self.number_of_objectives) - new_solution.variables[0] = \ - [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)] + +
    +[docs] + def create_solution(self) -> BinarySolution: + new_solution = BinarySolution( + number_of_variables=self.number_of_variables(), number_of_objectives=self.number_of_objectives() + ) + new_solution.variables[0] = [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)] return new_solution
    -
    [docs] def get_name(self) -> str: - return 'Subset Sum'
    + +
    +[docs] + def name(self) -> str: + return "Subset Sum"
    +
    +
    @@ -281,8 +366,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
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  • jmetal.problem.singleobjective.unconstrained
    • - - - - - - - + jmetal.util.evaluator — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,8 +35,9 @@

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  • jmetal.util.evaluator
    • @@ -51,7 +48,7 @@

    Navigation

    @@ -111,8 +108,8 @@

    Table Of Contents

    Source code for jmetal.util.evaluator

     import functools
 from abc import ABC, abstractmethod
-from multiprocessing.pool import ThreadPool, Pool
-from typing import TypeVar, List, Generic
+from multiprocessing.pool import Pool, ThreadPool
+from typing import Generic, List, TypeVar
 
 try:
     import dask
@@ -126,11 +123,10 @@ 
    Source code for jmetal.util.evaluator
     
 from jmetal.core.problem import Problem
 
-S = TypeVar('S')
+S = TypeVar("S")
 
 
 class Evaluator(Generic[S], ABC):
-
     @abstractmethod
     def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
         pass
@@ -140,49 +136,69 @@ 
    Source code for jmetal.util.evaluator
             problem.evaluate(solution)
 
 
-
    [docs]class SequentialEvaluator(Evaluator[S]):
-
-
    [docs]    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
+
    
+[docs]
+class SequentialEvaluator(Evaluator[S]):
+
    
+[docs]
+    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
         for solution in solution_list:
             Evaluator.evaluate_solution(solution, problem)
 
-        return solution_list
    
+        return solution_list
    
+
    
 
 
-
    [docs]class MapEvaluator(Evaluator[S]):
 
+
    
+[docs]
+class MapEvaluator(Evaluator[S]):
     def __init__(self, processes: int = None):
         self.pool = ThreadPool(processes)
 
-
    [docs]    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
+
    
+[docs]
+    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
         self.pool.map(lambda solution: Evaluator.evaluate_solution(solution, problem), solution_list)
 
-        return solution_list
    
+        return solution_list
    
+
    
+
 
 
-
    [docs]class MultiprocessEvaluator(Evaluator[S]):
+
    
+[docs]
+class MultiprocessEvaluator(Evaluator[S]):
     def __init__(self, processes: int = None):
         super().__init__()
         self.pool = Pool(processes)
 
-
    [docs]    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
-        return self.pool.map(functools.partial(evaluate_solution, problem=problem), solution_list)
    
+
    
+[docs]
+    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
+        return self.pool.map(functools.partial(evaluate_solution, problem=problem), solution_list)
    
+
    
+
 
 
-
    [docs]class SparkEvaluator(Evaluator[S]):
+
    
+[docs]
+class SparkEvaluator(Evaluator[S]):
     def __init__(self, processes: int = 8):
-        self.spark_conf = SparkConf().setAppName("jmetalpy").setMaster(f"local[{processes}]")
+        self.spark_conf = SparkConf().setAppName("jmetalpy").setMaster(f"local[{processes}]")
         self.spark_context = SparkContext(conf=self.spark_conf)
 
         logger = self.spark_context._jvm.org.apache.log4j
         logger.LogManager.getLogger("org").setLevel(logger.Level.WARN)
 
-
    [docs]    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
+
    
+[docs]
+    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
         solutions_to_evaluate = self.spark_context.parallelize(solution_list)
 
-        return solutions_to_evaluate \
-            .map(lambda s: problem.evaluate(s)) \
-            .collect()
    
+        return solutions_to_evaluate.map(lambda s: problem.evaluate(s)).collect()
    
+
    
+
 
 
 def evaluate_solution(solution, problem):
@@ -190,15 +206,23 @@ 
    Source code for jmetal.util.evaluator
         return solution
 
 
-
    [docs]class DaskEvaluator(Evaluator[S]):
-    def __init__(self, scheduler='processes'):
+
    
+[docs]
+class DaskEvaluator(Evaluator[S]):
+    def __init__(self, scheduler="processes"):
         self.scheduler = scheduler
 
-
    [docs]    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
+
    
+[docs]
+    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
         with dask.config.set(scheduler=self.scheduler):
-            return list(dask.compute(*[
-                dask.delayed(evaluate_solution)(solution=solution, problem=problem) for solution in solution_list
-            ]))
    
+            return list(
+                dask.compute(
+                    *[dask.delayed(evaluate_solution)(solution=solution, problem=problem) for solution in solution_list]
+                )
+            )
    
+
    
+
 
    
 
           
    
@@ -215,8 +239,9 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.3 documentation »
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-          
  • Module code »
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+        
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+          
  • Module code »
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+        
  • jmetal.util.evaluator
    •  
       
     
    
 
-    
-    
-    
-    
-    
-    
-    
+    jmetal.util.observer — jMetalPy 1.7.0 documentation
+    
+    
+    
+    
+    
+    
+    
     
     
   
@@ -39,8 +35,9 @@ 
    Navigation
    
         
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-        
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+        
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+          
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+        
  • jmetal.util.observer
    •
    @@ -51,7 +48,7 @@

    Navigation

    @@ -114,17 +111,18 @@

    Source code for jmetal.util.observer

     from pathlib import Path
 from typing import List, TypeVar
 
+import numpy as np
 from tqdm import tqdm
 
 from jmetal.core.observer import Observer
 from jmetal.core.problem import DynamicProblem
 from jmetal.core.quality_indicator import InvertedGenerationalDistance
-from jmetal.lab.visualization import StreamingPlot, Plot
+from jmetal.lab.visualization import Plot, StreamingPlot
 from jmetal.util.solution import print_function_values_to_file
 
-S = TypeVar('S')
+S = TypeVar("S")
 
-LOGGER = logging.getLogger('jmetal')
+LOGGER = logging.getLogger("jmetal")
 
 """
 .. module:: observer
@@ -135,10 +133,11 @@ 
    Source code for jmetal.util.observer
     """
 
 
-
    [docs]class ProgressBarObserver(Observer):
-
+
    
+[docs]
+class ProgressBarObserver(Observer):
     def __init__(self, max: int) -> None:
-        """ Show a smart progress meter with the number of evaluations and computing time.
+        """Show a smart progress meter with the number of evaluations and computing time.
 
         :param max: Number of expected iterations.
         """
@@ -146,31 +145,38 @@ 
    Source code for jmetal.util.observer
             self.progress = 0
         self._max = max
 
-
    [docs]    def update(self, *args, **kwargs):
+
    
+[docs]
+    def update(self, *args, **kwargs):
         if not self.progress_bar:
-            self.progress_bar = tqdm(total=self._max, ascii=True, desc='Progress')
+            self.progress_bar = tqdm(total=self._max, ascii=True, desc="Progress")
 
-        evaluations = kwargs['EVALUATIONS']
+        evaluations = kwargs["EVALUATIONS"]
 
         self.progress_bar.update(evaluations - self.progress)
         self.progress = evaluations
 
         if self.progress >= self._max:
-            self.progress_bar.close()
    
+            self.progress_bar.close()
    
+
    
 
 
-
    [docs]class BasicObserver(Observer):
 
-    def __init__(self, frequency: float = 1.0) -> None:
-        """ Show the number of evaluations, best fitness and computing time.
+
    
+[docs]
+class BasicObserver(Observer):
+    def __init__(self, frequency: int = 1) -> None:
+        """Show the number of evaluations, the best fitness and the computing time.
+        :param frequency: Display frequency."""
 
-        :param frequency: Display frequency. """
         self.display_frequency = frequency
 
-
    [docs]    def update(self, *args, **kwargs):
-        computing_time = kwargs['COMPUTING_TIME']
-        evaluations = kwargs['EVALUATIONS']
-        solutions = kwargs['SOLUTIONS']
+
    
+[docs]
+    def update(self, *args, **kwargs):
+        computing_time = kwargs["COMPUTING_TIME"]
+        evaluations = kwargs["EVALUATIONS"]
+        solutions = kwargs["SOLUTIONS"]
 
         if (evaluations % self.display_frequency) == 0 and solutions:
             if type(solutions) == list:
@@ -179,23 +185,26 @@ 
    Source code for jmetal.util.observer
                     fitness = solutions.objectives
 
             LOGGER.info(
-                'Evaluations: {} \n Best fitness: {} \n Computing time: {}'.format(
-                    evaluations, fitness, computing_time
-                )
-            )
    
+                "Evaluations: {} \n Best fitness: {} \n Computing time: {}".format(evaluations, fitness, computing_time)
+            )
    
+
    
 
 
-
    [docs]class PrintObjectivesObserver(Observer):
 
-    def __init__(self, frequency: float = 1.0) -> None:
-        """ Show the number of evaluations, best fitness and computing time.
+
    
+[docs]
+class PrintObjectivesObserver(Observer):
+    def __init__(self, frequency: int = 1) -> None:
+        """Show the number of evaluations, best fitness and computing time.
 
-        :param frequency: Display frequency. """
+        :param frequency: Display frequency."""
         self.display_frequency = frequency
 
-
    [docs]    def update(self, *args, **kwargs):
-        evaluations = kwargs['EVALUATIONS']
-        solutions = kwargs['SOLUTIONS']
+
    
+[docs]
+    def update(self, *args, **kwargs):
+        evaluations = kwargs["EVALUATIONS"]
+        solutions = kwargs["SOLUTIONS"]
 
         if (evaluations % self.display_frequency) == 0 and solutions:
             if type(solutions) == list:
@@ -203,124 +212,137 @@ 
    Source code for jmetal.util.observer
                 else:
                 fitness = solutions.objectives
 
-            LOGGER.info(
-                'Evaluations: {}. fitness: {}'.format(
-                    evaluations, fitness
-                )
-            )
    
+            LOGGER.info("Evaluations: {}. fitness: {}".format(evaluations, fitness))
    
+
    
 
 
-
    [docs]class WriteFrontToFileObserver(Observer):
 
+
    
+[docs]
+class WriteFrontToFileObserver(Observer):
     def __init__(self, output_directory: str) -> None:
-        """ Write function values of the front into files.
+        """Write function values of the front into files.
 
-        :param output_directory: Output directory. Each front will be saved on a file `FUN.x`. """
+        :param output_directory: Output directory. Each front will be saved on a file `FUN.x`."""
         self.counter = 0
         self.directory = output_directory
 
         if Path(self.directory).is_dir():
-            LOGGER.warning('Directory {} exists. Removing contents.'.format(self.directory))
+            LOGGER.warning("Directory {} exists. Removing contents.".format(self.directory))
             for file in os.listdir(self.directory):
-                os.remove('{0}/{1}'.format(self.directory, file))
+                os.remove("{0}/{1}".format(self.directory, file))
         else:
-            LOGGER.warning('Directory {} does not exist. Creating it.'.format(self.directory))
+            LOGGER.warning("Directory {} does not exist. Creating it.".format(self.directory))
             Path(self.directory).mkdir(parents=True)
 
-
    [docs]    def update(self, *args, **kwargs):
-        problem = kwargs['PROBLEM']
-        solutions = kwargs['SOLUTIONS']
+
    
+[docs]
+    def update(self, *args, **kwargs):
+        problem = kwargs["PROBLEM"]
+        solutions = kwargs["SOLUTIONS"]
 
         if solutions:
             if isinstance(problem, DynamicProblem):
-                termination_criterion_is_met = kwargs.get('TERMINATION_CRITERIA_IS_MET', None)
+                termination_criterion_is_met = kwargs.get("TERMINATION_CRITERIA_IS_MET", None)
 
                 if termination_criterion_is_met:
-                    print_function_values_to_file(solutions, '{}/FUN.{}'.format(self.directory, self.counter))
+                    print_function_values_to_file(solutions, "{}/FUN.{}".format(self.directory, self.counter))
                     self.counter += 1
             else:
-                print_function_values_to_file(solutions, '{}/FUN.{}'.format(self.directory, self.counter))
-                self.counter += 1
    
+                print_function_values_to_file(solutions, "{}/FUN.{}".format(self.directory, self.counter))
+                self.counter += 1
    
+
    
 
 
-
    [docs]class PlotFrontToFileObserver(Observer):
 
+
    
+[docs]
+class PlotFrontToFileObserver(Observer):
     def __init__(self, output_directory: str, step: int = 100, **kwargs) -> None:
-        """ Plot and save Pareto front approximations into files.
+        """Plot and save Pareto front approximations into files.
 
         :param output_directory: Output directory.
         """
         self.directory = output_directory
-        self.plot_front = Plot(title='Pareto front approximation', **kwargs)
+        self.plot_front = Plot(title="Pareto front approximation", **kwargs)
         self.last_front = []
         self.fronts = []
         self.counter = 0
         self.step = step
 
         if Path(self.directory).is_dir():
-            LOGGER.warning('Directory {} exists. Removing contents.'.format(self.directory))
+            LOGGER.warning("Directory {} exists. Removing contents.".format(self.directory))
             for file in os.listdir(self.directory):
-                os.remove('{0}/{1}'.format(self.directory, file))
+                os.remove("{0}/{1}".format(self.directory, file))
         else:
-            LOGGER.warning('Directory {} does not exist. Creating it.'.format(self.directory))
+            LOGGER.warning("Directory {} does not exist. Creating it.".format(self.directory))
             Path(self.directory).mkdir(parents=True)
 
-
    [docs]    def update(self, *args, **kwargs):
-        problem = kwargs['PROBLEM']
-        solutions = kwargs['SOLUTIONS']
-        evaluations = kwargs['EVALUATIONS']
+
    
+[docs]
+    def update(self, *args, **kwargs):
+        problem = kwargs["PROBLEM"]
+        solutions = kwargs["SOLUTIONS"]
+        evaluations = kwargs["EVALUATIONS"]
 
         if solutions:
             if (evaluations % self.step) == 0:
                 if isinstance(problem, DynamicProblem):
-                    termination_criterion_is_met = kwargs.get('TERMINATION_CRITERIA_IS_MET', None)
+                    termination_criterion_is_met = kwargs.get("TERMINATION_CRITERIA_IS_MET", None)
 
                     if termination_criterion_is_met:
                         if self.counter > 0:
-                            igd = InvertedGenerationalDistance(self.last_front)
-                            igd_value = igd.compute(solutions)
+                            igd = InvertedGenerationalDistance(np.array([s.objectives for s in self.last_front]))
+                            igd_value = igd.compute(np.array([s.objectives for s in solutions]))
                         else:
                             igd_value = 1
 
                         if igd_value > 0.005:
                             self.fronts += solutions
-                            self.plot_front.plot([self.fronts],
-                                                 label=problem.get_name(),
-                                                 filename=f'{self.directory}/front-{evaluations}')
+                            self.plot_front.plot(
+                                [self.fronts],
+                                label=problem.get_name(),
+                                filename=f"{self.directory}/front-{evaluations}",
+                            )
                         self.counter += 1
                         self.last_front = solutions
                 else:
-                    self.plot_front.plot([solutions],
-                                         label=f'{evaluations} evaluations',
-                                         filename=f'{self.directory}/front-{evaluations}')
-                    self.counter += 1
    
+                    self.plot_front.plot(
+                        [solutions],
+                        label=f"{evaluations} evaluations",
+                        filename=f"{self.directory}/front-{evaluations}",
+                    )
+                    self.counter += 1
    
+
    
 
 
-
    [docs]class VisualizerObserver(Observer):
 
-    def __init__(self,
-                 reference_front: List[S] = None,
-                 reference_point: list = None,
-                 display_frequency: float = 1.0) -> None:
+
    
+[docs]
+class VisualizerObserver(Observer):
+    def __init__(
+        self, reference_front: List[S] = None, reference_point: list = None, display_frequency: int = 1
+    ) -> None:
         self.figure = None
         self.display_frequency = display_frequency
 
         self.reference_point = reference_point
         self.reference_front = reference_front
 
-
    [docs]    def update(self, *args, **kwargs):
-        evaluations = kwargs['EVALUATIONS']
-        solutions = kwargs['SOLUTIONS']
+
    
+[docs]
+    def update(self, *args, **kwargs):
+        evaluations = kwargs["EVALUATIONS"]
+        solutions = kwargs["SOLUTIONS"]
 
         if solutions:
             if self.figure is None:
-                self.figure = StreamingPlot(reference_point=self.reference_point,
-                                            reference_front=self.reference_front)
+                self.figure = StreamingPlot(reference_point=self.reference_point, reference_front=self.reference_front)
                 self.figure.plot(solutions)
 
             if (evaluations % self.display_frequency) == 0:
                 # check if reference point has changed
-                reference_point = kwargs.get('REFERENCE_POINT', None)
+                reference_point = kwargs.get("REFERENCE_POINT", None)
 
                 if reference_point:
                     self.reference_point = reference_point
@@ -328,7 +350,9 @@ 
    Source code for jmetal.util.observer
                     else:
                     self.figure.update(solutions)
 
-                self.figure.ax.set_title('Eval: {}'.format(evaluations), fontsize=13)
    
+                self.figure.ax.set_title("Eval: {}".format(evaluations), fontsize=13)
    
+
    
+
    @@ -345,8 +369,9 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Module code »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Module code »
    • +
  • jmetal.util.observer
    • + + + + + + + + + + + + +
    + +
    + +
    +
    +
    + +
    +
    + Python version of the jMetal framework +
    +
    +
    +
    +

    Table Of Contents

    +
    +
    + + + + + +
    +
    +
    +
    +

    Contents

    +
    + + +
    +
    +
    +
    +
    + +
    +
    + +
    +
    +
    + + + +
    + +
    +

    About

    +

    jMetalPy is being developed by Antonio J. Nebro, full professor at the University of Málaga.

    +
    +

    Cite us

    +
    @article{BENITEZHIDALGO2019100598,
+   title = "jMetalPy: A Python framework for multi-objective optimization with metaheuristics",
+   journal = "Swarm and Evolutionary Computation",
+   pages = "100598",
+   year = "2019",
+   issn = "2210-6502",
+   doi = "https://doi.org/10.1016/j.swevo.2019.100598",
+   url = "http://www.sciencedirect.com/science/article/pii/S2210650219301397",
+   author = "Antonio Benítez-Hidalgo and Antonio J. Nebro and José García-Nieto and Izaskun Oregi and Javier Del Ser",
+   keywords = "Multi-objective optimization, Metaheuristics, Software framework, Python, Statistical analysis, Visualization",
+}
+
    +
    +
    +
    +

    References

    +
      +

    1. J.J. Durillo, A.J. Nebro jMetal: a Java Framework for Multi-Objective Optimization. Advances in Engineering Software 42 (2011) 760-771.

      2. +

    2. A.J. Nebro, J.J. Durillo, M. Vergne Redesigning the jMetal Multi-Objective Optimization Framework. GECCO (Companion) 2015, pp: 1093-1100. July 2015.

      3. +

    3. Nebro A.J. et al. (2018) Extending the Speed-Constrained Multi-objective PSO (SMPSO) with Reference Point Based Preference Articulation. In: Auger A., Fonseca C., Lourenço N., Machado P., Paquete L., Whitley D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science, vol 11101. Springer, Cham

      4. +
    +
    +
    + + +
    + +
    + +
    + ← Contributing +
    + +
    +
    + +
    +
    +
    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/api/algorithm/multiobjective/ea.html b/docs/api/algorithm/multiobjective/ea.html index e43d4443..4dab0909 100644 --- a/docs/api/algorithm/multiobjective/ea.html +++ b/docs/api/algorithm/multiobjective/ea.html @@ -1,9 +1,10 @@ - - + + + @@ -12,18 +13,16 @@ - Evolutionary Algorithms — jMetalPy 1.5.3 documentation - - - - - - - - - - - + Evolutionary Algorithms — jMetalPy 1.7.0 documentation + + + + + + + + + @@ -47,8 +46,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Multi-objective algorithms »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Multi-objective algorithms »
    • +
  • Evolutionary Algorithms
    • @@ -59,7 +59,7 @@

    Navigation

    @@ -120,10 +120,10 @@

    Table Of Contents

    -
    -

    Evolutionary Algorithms

    +
    +

    Evolutionary Algorithms

    -

    Multi-objective evolutionary algorithms:

    +

    Multi-objective evolutionary algorithms:

    -
    +
    @@ -170,8 +170,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Multi-objective algorithms »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Multi-objective algorithms »
    • +
  • Evolutionary Algorithms
    • - - - - - - - + GDE3 — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • GDE3
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,20 @@

    Contents

    @@ -138,216 +152,15 @@

    Contents

    - - -
    -

    GDE3

    -
    -

    Example

    +
    +

    GDE3

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.gde3 import GDE3
+
    from jmetal.algorithm.multiobjective.gde3 import GDE3
 from jmetal.problem import ZDT1
 from jmetal.util.termination_criterion import StoppingByEvaluations
 
@@ -373,8 +186,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -391,63 +203,63 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.gde3.GDE3(problem: jmetal.core.problem.Problem, population_size: int, cr: float, f: float, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, k: float = 0.5, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.core.algorithm.EvolutionaryAlgorithm
    
-
    
-
    
-create_initial_solutions() → List[jmetal.core.solution.FloatSolution][source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.gde3.GDE3(problem: ~jmetal.core.problem.Problem, population_size: int, cr: float, f: float, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, k: float = 0.5, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: EvolutionaryAlgorithm[FloatSolution, FloatSolution]

    +
    +
    +create_initial_solutions() → List[FloatSolution][source]

    Creates the initial list of solutions of a metaheuristic.

    -
    -
    -evaluate(solution_list: List[jmetal.core.solution.FloatSolution]) → List[jmetal.core.solution.FloatSolution][source]
    +
    +
    +evaluate(solution_list: List[FloatSolution]) → List[FloatSolution][source]

    Evaluates a solution list.

    -
    -
    -get_name() → str[source]
    -
    - -
    -
    -get_result() → List[jmetal.core.solution.FloatSolution][source]
    +
    +
    +get_name() → str[source]
    -
    -
    -replacement(population: List[S], offspring_population: List[jmetal.core.solution.FloatSolution]) → List[List[jmetal.core.solution.FloatSolution]][source]
    +
    +
    +replacement(population: List[S], offspring_population: List[FloatSolution]) → List[List[FloatSolution]][source]

    Replace least-fit population with new individuals.

    -
    -
    -reproduction(mating_pool: List[S]) → List[S][source]
    +
    +
    +reproduction(mating_pool: List[S]) → List[S][source]

    Breed new individuals through crossover and mutation operations to give birth to offspring.

    -
    -
    -selection(population: List[jmetal.core.solution.FloatSolution]) → List[jmetal.core.solution.FloatSolution][source]
    +
    +
    +result() → List[FloatSolution][source]
    +
    + +
    +
    +selection(population: List[FloatSolution]) → List[FloatSolution][source]

    Select the best-fit individuals for reproduction (parents).

    -
    -
    -stopping_condition_is_met() → bool[source]
    +
    +
    +stopping_condition_is_met() → bool[source]

    The stopping condition is met or not.

    -
    -
    + +
    @@ -482,9 +294,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • GDE3
    • - - - - - - - + Dynamic GDE3 — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -39,7 +39,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Dynamic GDE3
    • @@ -50,7 +51,7 @@

    Navigation

    @@ -84,7 +85,15 @@

    Contents

    @@ -120,216 +129,15 @@

    Contents

    - - -
    -

    Dynamic GDE3

    -
    -

    Example

    +
    +

    Dynamic GDE3

    +
    +

    Example

    [ ]:
    -
    -from jmetal.algorithm.multiobjective.gde3 import DynamicGDE3
+
    from jmetal.algorithm.multiobjective.gde3 import DynamicGDE3
 from jmetal.problem.multiobjective.fda import FDA2
 from jmetal.util.observable import TimeCounter
 from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
@@ -356,34 +164,34 @@ 
    Example
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.gde3.DynamicGDE3(problem: jmetal.core.problem.DynamicProblem, population_size: int, cr: float, f: float, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion, k: float = 0.5, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.algorithm.multiobjective.gde3.GDE3, jmetal.core.algorithm.DynamicAlgorithm
    
-
    
-
    
-restart() → None[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.gde3.DynamicGDE3(problem: ~jmetal.core.problem.DynamicProblem, population_size: int, cr: float, f: float, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion, k: float = 0.5, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: GDE3, DynamicAlgorithm

    +
    +
    +restart() → None[source]
    -
    -
    -stopping_condition_is_met()[source]
    +
    +
    +stopping_condition_is_met()[source]

    The stopping condition is met or not.

    -
    -
    -update_progress()[source]
    +
    +
    +update_progress()[source]

    Update the progress after each iteration.

    -
    -
    + +
    @@ -400,7 +208,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Dynamic GDE3
    • - - - - - - - + Preference point-based GDE3 — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -39,7 +39,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Preference point-based GDE3
    • @@ -50,7 +51,7 @@

    Navigation

    @@ -120,216 +121,15 @@

    Contents

    - - -
    -

    Preference point-based GDE3

    -
    -

    Example

    +
    +

    Preference point-based GDE3

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.gde3 import GDE3
+
    from jmetal.algorithm.multiobjective.gde3 import GDE3
 from jmetal.problem import ZDT2
 from jmetal.util.comparator import GDominanceComparator
 from jmetal.util.termination_criterion import StoppingByEvaluations
@@ -337,7 +137,7 @@ 
    Exampleproblem = ZDT2()
 
 max_evaluations = 25000
-reference_point = [.5, .5]
+reference_point = [.5, .5]
 
 algorithm = GDE3(
     problem=problem,
@@ -358,8 +158,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -376,14 +175,14 @@ 
    Example
 
    
 
    
-
    
-
    +
    +

    API

    .. autoclass:: jmetal.algorithm.multiobjective.gde3.GDE3 :members: :undoc-members: - :show-inheritance:
    -
    + :show-inheritance: +
    @@ -400,7 +199,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Preference point-based GDE3
    • - - - - - - - + HYPE — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • HYPE
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,16 @@

    Contents

    @@ -138,216 +148,15 @@

    Contents

    - - -
    -

    HYPE

    -
    -

    Example

    +
    +

    HYPE

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.hype import HYPE
+
    from jmetal.algorithm.multiobjective.hype import HYPE
 from jmetal.core.solution import FloatSolution
 from jmetal.operator import SBXCrossover, PolynomialMutation
 from jmetal.problem import ZDT1
@@ -378,8 +187,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -396,39 +204,39 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.hype.HYPE(problem: jmetal.core.problem.Problem, reference_point: jmetal.core.solution.Solution, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm
    
-
    
-
    
-evaluate(population: List[S])[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.hype.HYPE(problem: ~jmetal.core.problem.Problem, reference_point: ~jmetal.core.solution.Solution, population_size: int, offspring_population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: GeneticAlgorithm[S, R]

    +
    +
    +evaluate(population: List[S])[source]

    Evaluates a solution list.

    -
    -
    -get_name() → str[source]
    -
    - -
    -
    -get_result() → R[source]
    +
    +
    +get_name() → str[source]
    -
    -
    -replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]
    +
    +
    +replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]

    Replace least-fit population with new individuals.

    +
    +
    +result() → R[source]
    +
    +
    -
    -
    + +
    @@ -463,9 +271,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • HYPE
    • - - - - - - - + IBEA — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • IBEA
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,17 @@

    Contents

    @@ -138,216 +149,15 @@

    Contents

    - - -
    -

    IBEA

    -
    -

    Example

    +
    +

    IBEA

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.ibea import IBEA
+
    from jmetal.algorithm.multiobjective.ibea import IBEA
 from jmetal.operator import SBXCrossover, PolynomialMutation
 from jmetal.problem import ZDT1
 from jmetal.util.termination_criterion import StoppingByEvaluations
@@ -376,8 +186,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 
 plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])
 plot_front.plot(front, label='IBEA-ZDT1')
@@ -391,44 +200,44 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.ibea.IBEA(problem: jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, kappa: float, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    
-
    Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm
    
-
    
-
    
-compute_fitness_values(population: List[S], kappa: float) → List[S][source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.ibea.IBEA(problem: ~jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, kappa: float, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    +

    Bases: GeneticAlgorithm[S, R]

    +
    +
    +compute_fitness_values(population: List[S], kappa: float) → List[S][source]
    -
    -
    -create_initial_solutions() → List[S][source]
    +
    +
    +create_initial_solutions() → List[S][source]

    Creates the initial list of solutions of a metaheuristic.

    -
    -
    -get_name() → str[source]
    -
    - -
    -
    -get_result() → R[source]
    +
    +
    +get_name() → str[source]
    -
    -
    -replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]
    +
    +
    +replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]

    Replace least-fit population with new individuals.

    +
    +
    +result() → R[source]
    +
    +
    -
    -
    + +
    @@ -463,9 +272,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • IBEA
    • - - - - - - - + MOCell — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • MOCell
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,19 @@

    Contents

    @@ -138,216 +151,15 @@

    Contents

    - - -
    -

    MOCell

    -
    -

    Example

    +
    +

    MOCell

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.mocell import MOCell
+
    from jmetal.algorithm.multiobjective.mocell import MOCell
 from jmetal.operator import SBXCrossover, PolynomialMutation
 from jmetal.problem import ZDT4
 from jmetal.util.archive import CrowdingDistanceArchive
@@ -378,8 +190,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 
 plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])
 plot_front.plot(front, label='MOCell-ZDT4')
@@ -393,57 +204,57 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.mocell.MOCell(problem: jmetal.core.problem.Problem, population_size: int, neighborhood: jmetal.util.neighborhood.Neighborhood, archive: jmetal.util.archive.BoundedArchive, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm
    
-
    
-
    
-get_name() → str[source]
    
-
    
-
-
    
-
    
-get_result() → R[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.mocell.MOCell(problem: ~jmetal.core.problem.Problem, population_size: int, neighborhood: ~jmetal.util.neighborhood.Neighborhood, archive: ~jmetal.util.archive.BoundedArchive, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, selection: ~jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: GeneticAlgorithm[S, R]

    +
    +
    +get_name() → str[source]
    -
    -
    -init_progress() → None[source]
    +
    +
    +init_progress() → None[source]

    Initialize the algorithm.

    -
    -
    -replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]
    +
    +
    +replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]

    Replace least-fit population with new individuals.

    -
    -
    -reproduction(mating_population: List[S]) → List[S][source]
    +
    +
    +reproduction(mating_population: List[S]) → List[S][source]

    Breed new individuals through crossover and mutation operations to give birth to offspring.

    -
    -
    -selection(population: List[S])[source]
    +
    +
    +result() → R[source]
    +
    + +
    +
    +selection(population: List[S])[source]

    Select the best-fit individuals for reproduction (parents).

    -
    -
    -update_progress() → None[source]
    +
    +
    +update_progress() → None[source]

    Update the progress after each iteration.

    -
    -
    + +
    @@ -478,9 +289,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • MOCell
    • - - - - - - - + MOEA/D — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • MOEA/D
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,21 @@

    Contents

    @@ -138,216 +153,15 @@

    Contents

    - - -
    -

    MOEA/D

    -
    -

    Example

    +
    +

    MOEA/D

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.moead import MOEAD
+
    from jmetal.algorithm.multiobjective.moead import MOEAD
 from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
 from jmetal.problem import LZ09_F2
 from jmetal.util.aggregative_function import Tschebycheff
@@ -380,8 +194,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -398,66 +211,66 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.moead.MOEAD(problem: jmetal.core.problem.Problem, population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.operator.crossover.DifferentialEvolutionCrossover, aggregative_function: jmetal.util.aggregative_function.AggregativeFunction, neighbourhood_selection_probability: float, max_number_of_replaced_solutions: int, neighbor_size: int, weight_files_path: str, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    
-
    Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm
    
-
    
-
    
-choose_neighbor_type()[source]
    
-
    
-
-
    
-
    
-generate_permutation_of_neighbors(subproblem_id)[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.moead.MOEAD(problem: ~jmetal.core.problem.Problem, population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.operator.crossover.DifferentialEvolutionCrossover, aggregation_function: ~jmetal.util.aggregation_function.AggregationFunction, neighbourhood_selection_probability: float, max_number_of_replaced_solutions: int, neighbor_size: int, weight_files_path: str, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~typing.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    +

    Bases: GeneticAlgorithm

    +
    +
    +choose_neighbor_type()[source]
    -
    -
    -get_name()[source]
    +
    +
    +generate_permutation_of_neighbors(subproblem_id)[source]
    -
    -
    -get_result()[source]
    +
    +
    +get_name()[source]
    -
    -
    -init_progress() → None[source]
    +
    +
    +init_progress() → None[source]

    Initialize the algorithm.

    -
    -
    -replacement(population: List[S], offspring_population: List[S]) → List[S][source]
    +
    +
    +replacement(population: List[S], offspring_population: List[S]) → List[S][source]

    Replace least-fit population with new individuals.

    -
    -
    -reproduction(mating_population: List[S]) → List[S][source]
    +
    +
    +reproduction(mating_population: List[S]) → List[S][source]

    Breed new individuals through crossover and mutation operations to give birth to offspring.

    -
    -
    -selection(population: List[S])[source]
    +
    +
    +result()[source]
    +
    + +
    +
    +selection(population: List[S])[source]

    Select the best-fit individuals for reproduction (parents).

    -
    -
    -update_current_subproblem_neighborhood(new_solution, population)[source]
    +
    +
    +update_current_subproblem_neighborhood(new_solution, population)[source]
    -
    -
    + +
    @@ -492,9 +305,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • MOEA/D
    • - - - - - - - + NSGA-II — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • NSGA-II
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,15 @@

    Contents

    @@ -138,216 +147,15 @@

    Contents

    - - -
    -

    NSGA-II

    -
    -

    Example

    +
    +

    NSGA-II

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.nsgaii import NSGAII
+
    from jmetal.algorithm.multiobjective.nsgaii import NSGAII
 from jmetal.operator import SBXCrossover, PolynomialMutation
 from jmetal.problem import ZDT1
 from jmetal.util.termination_criterion import StoppingByEvaluations
@@ -375,8 +183,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -393,45 +200,45 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.nsgaii.NSGAII(problem: jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm
    
-
    
-
    
-get_name() → str[source]
    
-
    
-
-
    
-
    
-get_result() → R[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.nsgaii.NSGAII(problem: ~jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, selection: ~jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~typing.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: GeneticAlgorithm[S, R]

    +
    +
    +get_name() → str[source]
    -
    -
    -replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]
    +
    +
    +replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]

    This method joins the current and offspring populations to produce the population of the next generation by applying the ranking and crowding distance selection.

    -
    Parameters
    +
    Parameters:

    • population – Parent population.

    • offspring_population – Offspring population.

    -
    Returns
    +
    Returns:

    New population after ranking and crowding distance selection is applied.

    +
    +
    +result() → R[source]
    +
    +
    -
    -
    + +
    @@ -466,9 +273,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • NSGA-II
    • - - - - - - - + Distributed NSGA-II — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -39,7 +39,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Distributed NSGA-II
    • @@ -50,7 +51,7 @@

    Navigation

    @@ -84,7 +85,22 @@

    Contents

    @@ -120,216 +136,15 @@

    Contents

    - - -
    -

    Distributed NSGA-II

    -
    -

    Example

    +
    +

    Distributed NSGA-II

    +
    +

    Example

    [ ]:
    -
    -from math import sqrt
+
    from math import sqrt
 
 from jmetal.core.problem import FloatProblem
 from jmetal.core.solution import FloatSolution
@@ -338,7 +153,7 @@ 
    Exampleclass ZDT1Modified(FloatProblem):
 
     def __init__(self, number_of_variables: int=30):
-        """ :param number_of_variables: Number of decision variables of the problem.
+        """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(ZDT1Modified, self).__init__()
         self.number_of_variables = number_of_variables
@@ -386,8 +201,7 @@ 
    Example
    [ ]:
 
    
 
    
-
    -from dask.distributed import Client
+
    from dask.distributed import Client
 from distributed import LocalCluster
 
 from jmetal.algorithm.multiobjective.nsgaii import DistributedNSGAII
@@ -402,7 +216,7 @@ 
    Exampleclient = Client(LocalCluster(n_workers=24))
 
 ncores = sum(client.ncores().values())
-print(f'{ncores} cores available')
+print(f'{ncores} cores available')
 
 # creates the algorithm
 max_evaluations = 25000
@@ -427,8 +241,7 @@ 
    Example
    [ ]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -438,75 +251,75 @@ 
    Example
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.nsgaii.DistributedNSGAII(problem: jmetal.core.problem.Problem, population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, number_of_cores: int, client, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.core.algorithm.Algorithm
    
-
    
-
    
-create_initial_solutions() → List[S][source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.nsgaii.DistributedNSGAII(problem: ~jmetal.core.problem.Problem, population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, number_of_cores: int, client, selection: ~jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, dominance_comparator: ~jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: Algorithm[S, R]

    +
    +
    +create_initial_solutions() → List[S][source]

    Creates the initial list of solutions of a metaheuristic.

    -
    -
    -evaluate(solutions: List[S]) → List[S][source]
    +
    +
    +evaluate(solutions: List[S]) → List[S][source]

    Evaluates a solution list.

    -
    -
    -get_name() → str[source]
    +
    +
    +get_name() → str[source]
    -
    -
    -get_observable_data() → dict[source]
    -

    Get observable data, with the information that will be send to all observers each time.

    +
    +
    +init_progress() → None[source]
    +

    Initialize the algorithm.

    -
    -
    -get_result() → R[source]
    -
    - -
    -
    -init_progress() → None[source]
    -

    Initialize the algorithm.

    +
    +
    +observable_data() → dict[source]
    +

    Get observable data, with the information that will be seng to all observers each time.

    -
    -
    -run()[source]
    +
    +
    +result() → R[source]
    +
    + +
    +
    +run()[source]

    Execute the algorithm.

    -
    -
    -step() → None[source]
    +
    +
    +step() → None[source]

    Performs one iteration/step of the algorithm’s loop.

    -
    -
    -stopping_condition_is_met() → bool[source]
    +
    +
    +stopping_condition_is_met() → bool[source]

    The stopping condition is met or not.

    -
    -
    -update_progress()[source]
    +
    +
    +update_progress()[source]

    Update the progress after each iteration.

    -
    -
    + +
    @@ -523,7 +336,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Distributed NSGA-II
    • - - - - - - - + Dynamic NSGA-II — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -39,7 +39,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Dynamic NSGA-II
    • @@ -50,7 +51,7 @@

    Navigation

    @@ -84,7 +85,15 @@

    Contents

    @@ -120,216 +129,15 @@

    Contents

    - - -
    -

    Dynamic NSGA-II

    -
    -

    Example

    +
    +

    Dynamic NSGA-II

    +
    +

    Example

    [ ]:
    -
    -from jmetal.algorithm.multiobjective.nsgaii import DynamicNSGAII
+
    from jmetal.algorithm.multiobjective.nsgaii import DynamicNSGAII
 from jmetal.operator import PolynomialMutation, SBXCrossover
 from jmetal.problem.multiobjective.fda import FDA2
 from jmetal.util.observable import TimeCounter
@@ -359,34 +167,34 @@ 
    Example
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.nsgaii.DynamicNSGAII(problem: jmetal.core.problem.DynamicProblem[~S][S], population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.algorithm.multiobjective.nsgaii.NSGAII, jmetal.core.algorithm.DynamicAlgorithm
    
-
    
-
    
-restart()[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.nsgaii.DynamicNSGAII(problem: ~jmetal.core.problem.DynamicProblem[~jmetal.algorithm.multiobjective.nsgaii.S], population_size: int, offspring_population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, selection: ~jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~typing.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: NSGAII[S, R], DynamicAlgorithm

    +
    +
    +restart()[source]
    -
    -
    -stopping_condition_is_met()[source]
    +
    +
    +stopping_condition_is_met()[source]

    The stopping condition is met or not.

    -
    -
    -update_progress()[source]
    +
    +
    +update_progress()[source]

    Update the progress after each iteration.

    -
    -
    + +
    @@ -403,7 +211,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Dynamic NSGA-II
    • - - - - - - - + Preference point-based NSGA-II — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -39,7 +39,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
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  • Preference point-based NSGA-II
    • @@ -50,7 +51,7 @@

    Navigation

    @@ -84,7 +85,15 @@

    Contents

    @@ -120,216 +129,15 @@

    Contents

    - - -
    -

    Preference point-based NSGA-II

    -
    -

    Example

    +
    +

    Preference point-based NSGA-II

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.nsgaii import NSGAII
+
    from jmetal.algorithm.multiobjective.nsgaii import NSGAII
 from jmetal.operator import SBXCrossover, PolynomialMutation
 from jmetal.problem import ZDT2
 from jmetal.util.comparator import GDominanceComparator
@@ -360,8 +168,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -378,34 +185,34 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.nsgaii.DynamicNSGAII(problem: jmetal.core.problem.DynamicProblem[~S][S], population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.algorithm.multiobjective.nsgaii.NSGAII, jmetal.core.algorithm.DynamicAlgorithm
    
-
    
-
    
-restart()[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.nsgaii.DynamicNSGAII(problem: ~jmetal.core.problem.DynamicProblem[~jmetal.algorithm.multiobjective.nsgaii.S], population_size: int, offspring_population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, selection: ~jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~typing.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: NSGAII[S, R], DynamicAlgorithm

    +
    +
    +restart()[source]
    -
    -
    -stopping_condition_is_met()[source]
    +
    +
    +stopping_condition_is_met()[source]

    The stopping condition is met or not.

    -
    -
    -update_progress()[source]
    +
    +
    +update_progress()[source]

    Update the progress after each iteration.

    -
    -
    + +
    @@ -422,7 +229,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Preference point-based NSGA-II
    • - - - - - - - + NSGA-III — jMetalPy 1.7.0 documentation + + + + + + + + + @@ -47,9 +46,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • NSGA-III
    • @@ -60,7 +60,7 @@

    Navigation

    @@ -97,7 +97,15 @@

    Contents

    @@ -137,12 +145,41 @@

    Contents

    -
    -

    NSGA-III

    -
    -

    API

    -
    -
    +
    +

    NSGA-III

    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.nsgaiii.NSGAIII(reference_directions, problem: ~jmetal.core.problem.Problem, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, population_size: int = None, selection: ~jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: NSGAII

    +
    +
    +get_name() → str[source]
    +
    + +
    +
    +replacement(population: List[S], offspring_population: List[S]) → List[S][source]
    +

    Implements NSGA-III environmental selection based on reference points as described in:

    +
      +

    • Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization +Algorithm Using Reference-Point-Based Nondominated Sorting Approach, +Part I: Solving Problems With Box Constraints. IEEE Transactions on +Evolutionary Computation, 18(4), 577–601. doi:10.1109/TEVC.2013.2281535.

      • +
    +
    + +
    +
    +result()[source]
    +

    Return only non dominated solutions.

    +
    + +
    + +
    +
    @@ -177,9 +214,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • NSGA-III
    • - - - - - - - - - - - - - - - - - -
    - -
    - -
    -
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    -
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    -

    Table Of Contents

    -
    -
    - - - - - -
    -
    -
    -
    -

    Contents

    -
    - - -
    -
    -
    -
    -
    - -
    -
    - -
    -
    -
    - - - -
    - - - -
    -

    NSGA-II

    -
    -

    Examples

    -
    -
    [2]:
-
    -
    -
    -from jmetal.algorithm.multiobjective.nsgaii import NSGAII
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT1
-from jmetal.util.termination_criterion import StoppingByEvaluations
-
-problem = ZDT1()
-
-max_evaluations = 2500
-algorithm = NSGAII(
-    problem=problem,
-    population_size=100,
-    offspring_population_size=100,
-    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
-    crossover=SBXCrossover(probability=1.0, distribution_index=20),
-    termination_criterion=StoppingByEvaluations(max=max_evaluations)
-)
-
-algorithm.run()
-front = algorithm.get_result()
-
    -
    -
    -

    We can now vis

    -
    -
    -

    API

    -.. automodule:: jmetal.algorithm.multiobjective.nsgaii - :members: - :undoc-members: - :show-inheritance:
    -
    - - -
    - -
    - -
    - SPEA2 -
    - -
    - PSO Algorithms -
    -
    -
    - -
    -
    -
    - - - -
    - © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. -
    - - \ No newline at end of file diff --git a/docs/api/algorithm/multiobjective/eas/oldnsgaii.html b/docs/api/algorithm/multiobjective/eas/oldnsgaii.html deleted file mode 100644 index cf1fa25a..00000000 --- a/docs/api/algorithm/multiobjective/eas/oldnsgaii.html +++ /dev/null @@ -1,421 +0,0 @@ - - - - - - - - - - - - - - - - NSGA-II — jMetalPy 1.5.3 documentation - - - - - - - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    -
    -
    -

    Table Of Contents

    -
    -
    - - - - - -
    -
    -
    -
    -

    Contents

    -
    - - -
    -
    -
    -
    -
    - -
    -
    - -
    -
    -
    - -
    -
      -
    1. Docs
      2. - -
    2. NSGA-II
      3. -
    -
    - -
    - -
    -

    NSGA-II

    -
    -

    Examples

    -
    -

    Standard

    -
    from jmetal.algorithm.multiobjective.nsgaii import NSGAII
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT1
-from jmetal.util.solution import read_solutions
-from jmetal.util.termination_criterion import StoppingByEvaluations
-
-problem = ZDT1()
-problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')
-
-max_evaluations = 25000
-algorithm = NSGAII(
-    problem=problem,
-    population_size=100,
-    offspring_population_size=100,
-    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
-    crossover=SBXCrossover(probability=1.0, distribution_index=20),
-    termination_criterion=StoppingByEvaluations(max=max_evaluations)
-)
-
-algorithm.run()
-front = algorithm.get_result()
-
    -
    -
    -
    -

    Distributed

    -
    -

    Warning

    -

    This requires some extra dependencies

    -
    -
    from dask.distributed import Client
-from distributed import LocalCluster
-
-from examples.multiobjective.parallel.zdt1_modified import ZDT1Modified
-from jmetal.algorithm.multiobjective.nsgaii import DistributedNSGAII
-from jmetal.operator import PolynomialMutation, SBXCrossover
-from jmetal.util.termination_criterion import StoppingByEvaluations
-
-problem = ZDT1Modified()
-
-# setup Dask client
-client = Client(LocalCluster(n_workers=24))
-
-ncores = sum(client.ncores().values())
-print(f'{ncores} cores available')
-
-# creates the algorithm
-max_evaluations = 25000
-
-algorithm = DistributedNSGAII(
-    problem=problem,
-    population_size=100,
-    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
-    crossover=SBXCrossover(probability=1.0, distribution_index=20),
-    termination_criterion=StoppingByEvaluations(max=max_evaluations),
-    number_of_cores=ncores,
-    client=client
-)
-
-algorithm.run()
-front = algorithm.get_result()
-
    -
    -
    -
    -

    Dynamic

    -
    from jmetal.algorithm.multiobjective.nsgaii import DynamicNSGAII
-from jmetal.operator import PolynomialMutation, SBXCrossover
-from jmetal.problem.multiobjective.fda import FDA2
-from jmetal.util.observable import TimeCounter
-from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
-from jmetal.util.termination_criterion import StoppingByEvaluations
-
-problem = FDA2()
-
-time_counter = TimeCounter(delay=1)
-time_counter.observable.register(problem)
-time_counter.start()
-
-max_evaluations = 25000
-algorithm = DynamicNSGAII(
-    problem=problem,
-    population_size=100,
-    offspring_population_size=100,
-    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
-    crossover=SBXCrossover(probability=1.0, distribution_index=20),
-    termination_criterion=StoppingByEvaluations(max=max_evaluations)
-)
-
-algorithm.observable.register(observer=PlotFrontToFileObserver('front_vis'))
-algorithm.observable.register(observer=WriteFrontToFileObserver('front_files'))
-
-algorithm.run()
-
    -
    -
    -
    -

    Preference point-based (gNSGA-II)

    -
    from jmetal.algorithm.multiobjective.nsgaii import NSGAII
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT2
-from jmetal.util.solutions import read_solutions
-from jmetal.util.solutions.comparator import GDominanceComparator
-from jmetal.util.termination_criterion import StoppingByEvaluations
-
-problem = ZDT2()
-problem.reference_front = read_solutions(filename='resources/reference_front/ZDT2.pf')
-
-reference_point = [0.2, 0.5]
-
-max_evaluations = 25000
-algorithm = NSGAII(
-    problem=problem,
-    population_size=100,
-    offspring_population_size=100,
-    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
-    crossover=SBXCrossover(probability=1.0, distribution_index=20),
-    dominance_comparator=GDominanceComparator(reference_point),
-    termination_criterion=StoppingByEvaluations(max=max_evaluations)
-)
-
-algorithm.run()
-front = algorithm.get_result()
-
    -
    -
    -
    -
    -

    API

    -
    -
    -class jmetal.algorithm.multiobjective.nsgaii.DistributedNSGAII(problem: jmetal.core.problem.Problem, population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, number_of_cores: int, client: distributed.client.Client, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    -

    Bases: jmetal.core.algorithm.Algorithm

    -
    -
    -create_initial_solutions() → List[S][source]
    -

    Creates the initial list of solutions of a metaheuristic.

    -
    - -
    -
    -evaluate(solutions: List[S]) → List[S][source]
    -

    Evaluates a solution list.

    -
    - -
    -
    -get_name() → str[source]
    -
    - -
    -
    -get_observable_data() → dict[source]
    -

    Get observable data, with the information that will be send to all observers each time.

    -
    - -
    -
    -get_result() → R[source]
    -
    - -
    -
    -init_progress() → None[source]
    -

    Initialize the algorithm.

    -
    - -
    -
    -run()[source]
    -

    Execute the algorithm.

    -
    - -
    -
    -step() → None[source]
    -

    Performs one iteration/step of the algorithm’s loop.

    -
    - -
    -
    -stopping_condition_is_met() → bool[source]
    -

    The stopping condition is met or not.

    -
    - -
    -
    -update_progress()[source]
    -

    Update the progress after each iteration.

    -
    - -
    - -
    -
    -class jmetal.algorithm.multiobjective.nsgaii.DynamicNSGAII(problem: jmetal.core.problem.DynamicProblem[~S][S], population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    -

    Bases: jmetal.algorithm.multiobjective.nsgaii.NSGAII, jmetal.core.algorithm.DynamicAlgorithm

    -
    -
    -restart()[source]
    -
    - -
    -
    -stopping_condition_is_met()[source]
    -

    The stopping condition is met or not.

    -
    - -
    -
    -update_progress()[source]
    -

    Update the progress after each iteration.

    -
    - -
    - -
    -
    -class jmetal.algorithm.multiobjective.nsgaii.NSGAII(problem: jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    -

    Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm

    -
    -
    -get_name() → str[source]
    -
    - -
    -
    -get_result() → R[source]
    -
    - -
    -
    -replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]
    -

    This method joins the current and offspring populations to produce the population of the next generation -by applying the ranking and crowding distance selection.

    -
    -
    Parameters
    -
      -

    • population – Parent population.

      • -

    • offspring_population – Offspring population.

      • -
    -
    -
    Returns
    -

    New population after ranking and crowding distance selection is applied.

    -
    -
    -
    - -
    - -
    -
    -jmetal.algorithm.multiobjective.nsgaii.R = ~R
    -
    - -
    -
    -jmetal.algorithm.multiobjective.nsgaii.reproduction(mating_population: List[S], problem, crossover_operator, mutation_operator) → S[source]
    -
    - -
    -
    - - -
    - -
    -
    -
    - - - -
    - © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. -
    - - \ No newline at end of file diff --git a/docs/api/algorithm/multiobjective/eas/oldspea2.html b/docs/api/algorithm/multiobjective/eas/oldspea2.html deleted file mode 100644 index b3e789e0..00000000 --- a/docs/api/algorithm/multiobjective/eas/oldspea2.html +++ /dev/null @@ -1,251 +0,0 @@ - - - - - - - - - - - - - - - - SPEA2 — jMetalPy 1.5.3 documentation - - - - - - - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    -
    -
    -

    Table Of Contents

    -
    -
    - - - - - -
    -
    -
    -
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    Contents

    -
    - - -
    -
    -
    -
    -
    - -
    -
    - -
    -
    -
    - -
    -
      -
    1. Docs
      2. - -
    2. SPEA2
      3. -
    -
    - -
    - -
    -

    SPEA2

    -
    -

    Examples

    -
    -

    Standard

    -
    from jmetal.algorithm.multiobjective.spea2 import SPEA2
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT1
-from jmetal.util.solutions import read_solutions
-from jmetal.util.termination_criterion import StoppingByEvaluations
-
-problem = ZDT1()
-problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')
-
-max_evaluations = 20000
-algorithm = SPEA2(
-    problem=problem,
-    population_size=40,
-    offspring_population_size=40,
-    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
-    crossover=SBXCrossover(probability=1.0, distribution_index=20),
-    termination_criterion=StoppingByEvaluations(max=max_evaluations)
-)
-
-algorithm.run()
-front = algorithm.get_result()
-
    -
    -
    -
    -

    Preference point-based (gSPEA2)

    -
    from jmetal.algorithm.multiobjective.spea2 import SPEA2
-from jmetal.lab.visualization import Plot, InteractivePlot
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT1
-from jmetal.util.solutions import read_solutions
-from jmetal.util.solutions.comparator import GDominanceComparator
-from jmetal.util.termination_criterion import StoppingByEvaluations
-
-problem = ZDT1()
-problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')
-
-reference_point = [0.4, 0.6]
-
-max_evaluations = 25000
-algorithm = SPEA2(
-    problem=problem,
-    population_size=40,
-    offspring_population_size=40,
-    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
-    crossover=SBXCrossover(probability=1.0, distribution_index=20),
-    termination_criterion=StoppingByEvaluations(max=max_evaluations),
-    dominance_comparator=GDominanceComparator(reference_point)
-)
-
-algorithm.run()
-front = algorithm.get_result()
-
    -
    -
    -
    -
    -

    API

    -
    -
    -class jmetal.algorithm.multiobjective.spea2.SPEA2(problem: jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    -

    Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm

    -
    -
    -get_name() → str[source]
    -
    - -
    -
    -get_result() → R[source]
    -
    - -
    -
    -replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]
    -

    This method joins the current and offspring populations to produce the population of the next generation -by applying the ranking and crowding distance selection.

    -
    -
    Parameters
    -
      -

    • population – Parent population.

      • -

    • offspring_population – Offspring population.

      • -
    -
    -
    Returns
    -

    New population after ranking and crowding distance selection is applied.

    -
    -
    -
    - -
    - -
    -
    - - -
    - -
    -
    -
    - - - -
    - © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. -
    - - \ No newline at end of file diff --git a/docs/api/algorithm/multiobjective/eas/spea2.html b/docs/api/algorithm/multiobjective/eas/spea2.html index 4a388cb7..995f9b60 100644 --- a/docs/api/algorithm/multiobjective/eas/spea2.html +++ b/docs/api/algorithm/multiobjective/eas/spea2.html @@ -1,9 +1,10 @@ - - + + + @@ -12,18 +13,17 @@ - SPEA2 — jMetalPy 1.5.3 documentation - - - - - - - - - - - + SPEA2 — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
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  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • SPEA2
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,15 @@

    Contents

    @@ -138,216 +147,15 @@

    Contents

    - - -
    -

    SPEA2

    -
    -

    Example

    +
    +

    SPEA2

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.spea2 import SPEA2
+
    from jmetal.algorithm.multiobjective.spea2 import SPEA2
 from jmetal.operator import SBXCrossover, PolynomialMutation
 from jmetal.problem import ZDT1
 from jmetal.util.termination_criterion import StoppingByEvaluations
@@ -375,8 +183,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -393,45 +200,45 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.spea2.SPEA2(problem: jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm
    
-
    
-
    
-get_name() → str[source]
    
-
    
-
-
    
-
    
-get_result() → R[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.spea2.SPEA2(problem: ~jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    +

    Bases: GeneticAlgorithm[S, R]

    +
    +
    +get_name() → str[source]
    -
    -
    -replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]
    +
    +
    +replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]

    This method joins the current and offspring populations to produce the population of the next generation by applying the ranking and crowding distance selection.

    -
    Parameters
    +
    Parameters:

    • population – Parent population.

    • offspring_population – Offspring population.

    -
    Returns
    +
    Returns:

    New population after ranking and crowding distance selection is applied.

    +
    +
    +result() → R[source]
    +
    +
    -
    -
    + +
    @@ -466,9 +273,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • Evolutionary Algorithms »
    • +
  • Evolutionary Algorithms »
    • +
  • SPEA2
    • - - - - - - - + PSO Algorithms — jMetalPy 1.7.0 documentation + + + + + + + + + @@ -47,8 +46,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Multi-objective algorithms »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Multi-objective algorithms »
    • +
  • PSO Algorithms
    • @@ -59,7 +59,7 @@

    Navigation

    @@ -120,16 +120,16 @@

    Table Of Contents

    -
    -

    PSO Algorithms

    +
    +

    PSO Algorithms

    -

    Multi-objective particle swarm optimization algorithms:

    +

    Multi-objective particle swarm optimization algorithms:

    -
    +
    @@ -164,8 +164,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Multi-objective algorithms »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Multi-objective algorithms »
    • +
  • PSO Algorithms
    • - - - - - - - + OMOPSO — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • PSO Algorithms »
    • +
  • PSO Algorithms »
    • +
  • OMOPSO
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,28 @@

    Contents

    @@ -138,216 +160,15 @@

    Contents

    - - -
    -

    OMOPSO

    -
    -

    Example

    +
    +

    OMOPSO

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.omopso import OMOPSO
+
    from jmetal.algorithm.multiobjective.omopso import OMOPSO
 from jmetal.operator import UniformMutation
 from jmetal.operator.mutation import NonUniformMutation
 from jmetal.problem import ZDT1
@@ -381,8 +202,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -399,102 +219,102 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.omopso.OMOPSO(problem: jmetal.core.problem.FloatProblem, swarm_size: int, uniform_mutation: jmetal.operator.mutation.UniformMutation, non_uniform_mutation: jmetal.operator.mutation.NonUniformMutation, leaders: Optional[jmetal.util.archive.BoundedArchive], epsilon: float, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion, swarm_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    
-
    Bases: jmetal.core.algorithm.ParticleSwarmOptimization
    
-
    
-
    
-create_initial_solutions() → List[jmetal.core.solution.FloatSolution][source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.omopso.OMOPSO(problem: ~jmetal.core.problem.FloatProblem, swarm_size: int, uniform_mutation: ~jmetal.operator.mutation.UniformMutation, non_uniform_mutation: ~jmetal.operator.mutation.NonUniformMutation, leaders: ~jmetal.util.archive.BoundedArchive | None, epsilon: float, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion, swarm_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    +

    Bases: ParticleSwarmOptimization

    +
    +
    +create_initial_solutions() → List[FloatSolution][source]

    Creates the initial list of solutions of a metaheuristic.

    -
    -
    -evaluate(solution_list: List[jmetal.core.solution.FloatSolution])[source]
    +
    +
    +evaluate(solution_list: List[FloatSolution])[source]

    Evaluates a solution list.

    -
    -
    -get_name() → str[source]
    -
    - -
    -
    -get_result() → List[jmetal.core.solution.FloatSolution][source]
    +
    +
    +get_name() → str[source]
    -
    -
    -init_progress() → None[source]
    +
    +
    +init_progress() → None[source]

    Initialize the algorithm.

    -
    -
    -initialize_global_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +initialize_global_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -initialize_particle_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +initialize_particle_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -initialize_velocity(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +initialize_velocity(swarm: List[FloatSolution]) → None[source]
    -
    -
    -perturbation(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +perturbation(swarm: List[FloatSolution]) → None[source]
    -
    -
    -select_global_best() → jmetal.core.solution.FloatSolution[source]
    +
    +
    +result() → List[FloatSolution][source]
    -
    -
    -stopping_condition_is_met() → bool[source]
    +
    +
    +select_global_best() → FloatSolution[source]
    +
    + +
    +
    +stopping_condition_is_met() → bool[source]

    The stopping condition is met or not.

    -
    -
    -update_global_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +update_global_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -update_particle_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +update_particle_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -update_position(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +update_position(swarm: List[FloatSolution]) → None[source]
    -
    -
    -update_progress() → None[source]
    +
    +
    +update_progress() → None[source]

    Update the progress after each iteration.

    -
    -
    -update_velocity(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +update_velocity(swarm: List[FloatSolution]) → None[source]
    -
    -
    + +
    @@ -529,9 +349,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • PSO Algorithms »
    • +
  • PSO Algorithms »
    • +
  • OMOPSO
    • - - - - - - - + SMPSO — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -47,9 +47,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • PSO Algorithms »
    • +
  • PSO Algorithms »
    • +
  • SMPSO
    • @@ -60,7 +61,7 @@

    Navigation

    @@ -98,7 +99,28 @@

    Contents

    @@ -138,216 +160,15 @@

    Contents

    - - -
    -

    SMPSO

    -
    -

    Example

    +
    +

    SMPSO

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.smpso import SMPSO
+
    from jmetal.algorithm.multiobjective.smpso import SMPSO
 from jmetal.operator import PolynomialMutation
 from jmetal.problem import ZDT4
 from jmetal.util.archive import CrowdingDistanceArchive
@@ -374,8 +195,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -392,102 +212,102 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.smpso.SMPSO(problem: jmetal.core.problem.FloatProblem, swarm_size: int, mutation: jmetal.core.operator.Mutation, leaders: Optional[jmetal.util.archive.BoundedArchive], termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, swarm_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    
-
    Bases: jmetal.core.algorithm.ParticleSwarmOptimization
    
-
    
-
    
-create_initial_solutions() → List[jmetal.core.solution.FloatSolution][source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.smpso.SMPSO(problem: ~jmetal.core.problem.FloatProblem, swarm_size: int, mutation: ~jmetal.core.operator.Mutation, leaders: ~jmetal.util.archive.BoundedArchive | None, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, swarm_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    +

    Bases: ParticleSwarmOptimization

    +
    +
    +create_initial_solutions() → List[FloatSolution][source]

    Creates the initial list of solutions of a metaheuristic.

    -
    -
    -evaluate(solution_list: List[jmetal.core.solution.FloatSolution])[source]
    +
    +
    +evaluate(solution_list: List[FloatSolution])[source]

    Evaluates a solution list.

    -
    -
    -get_name() → str[source]
    -
    - -
    -
    -get_result() → List[jmetal.core.solution.FloatSolution][source]
    +
    +
    +get_name() → str[source]
    -
    -
    -init_progress() → None[source]
    +
    +
    +init_progress() → None[source]

    Initialize the algorithm.

    -
    -
    -initialize_global_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +initialize_global_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -initialize_particle_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +initialize_particle_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -initialize_velocity(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +initialize_velocity(swarm: List[FloatSolution]) → None[source]
    -
    -
    -perturbation(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +perturbation(swarm: List[FloatSolution]) → None[source]
    -
    -
    -select_global_best() → jmetal.core.solution.FloatSolution[source]
    +
    +
    +result() → List[FloatSolution][source]
    -
    -
    -stopping_condition_is_met() → bool[source]
    +
    +
    +select_global_best() → FloatSolution[source]
    +
    + +
    +
    +stopping_condition_is_met() → bool[source]

    The stopping condition is met or not.

    -
    -
    -update_global_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +update_global_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -update_particle_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +update_particle_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -update_position(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +update_position(swarm: List[FloatSolution]) → None[source]
    -
    -
    -update_progress() → None[source]
    +
    +
    +update_progress() → None[source]

    Update the progress after each iteration.

    -
    -
    -update_velocity(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +update_velocity(swarm: List[FloatSolution]) → None[source]
    -
    -
    + +
    @@ -522,9 +342,10 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
  • Multi-objective algorithms »
    • -
  • PSO Algorithms »
    • +
  • PSO Algorithms »
    • +
  • SMPSO
    • - - - - - - - + Dynamic SMPSO — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -39,7 +39,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Dynamic SMPSO
    • @@ -50,7 +51,7 @@

    Navigation

    @@ -84,7 +85,15 @@

    Contents

    @@ -120,216 +129,15 @@

    Contents

    - - -
    -

    Dynamic SMPSO

    -
    -

    Example

    +
    +

    Dynamic SMPSO

    +
    +

    Example

    [ ]:
    -
    -from jmetal.algorithm.multiobjective.smpso import DynamicSMPSO
+
    from jmetal.algorithm.multiobjective.smpso import DynamicSMPSO
 from jmetal.operator import PolynomialMutation
 from jmetal.problem.multiobjective.fda import FDA2
 from jmetal.util.archive import CrowdingDistanceArchive
@@ -359,34 +167,34 @@ 
    Example
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.smpso.DynamicSMPSO(problem: jmetal.core.problem.DynamicProblem[jmetal.core.solution.FloatSolution][jmetal.core.solution.FloatSolution], swarm_size: int, mutation: jmetal.core.operator.Mutation, leaders: jmetal.util.archive.BoundedArchive, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, swarm_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    
-
    Bases: jmetal.algorithm.multiobjective.smpso.SMPSO, jmetal.core.algorithm.DynamicAlgorithm
    
-
    
-
    
-restart() → None[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.smpso.DynamicSMPSO(problem: ~jmetal.core.problem.DynamicProblem[~jmetal.core.solution.FloatSolution], swarm_size: int, mutation: ~jmetal.core.operator.Mutation, leaders: ~jmetal.util.archive.BoundedArchive, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, swarm_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    +

    Bases: SMPSO, DynamicAlgorithm

    +
    +
    +restart() → None[source]
    -
    -
    -stopping_condition_is_met()[source]
    +
    +
    +stopping_condition_is_met()[source]

    The stopping condition is met or not.

    -
    -
    -update_progress()[source]
    +
    +
    +update_progress()[source]

    Update the progress after each iteration.

    -
    -
    + +
    @@ -403,7 +211,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Dynamic SMPSO
    • - - - - - - - + SMPSO/RP — jMetalPy 1.7.0 documentation + + + + + + + + + + @@ -39,7 +39,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • SMPSO/RP
    • @@ -50,7 +51,7 @@

    Navigation

    @@ -84,7 +85,21 @@

    Contents

    @@ -120,216 +135,15 @@

    Contents

    - - -
    -

    SMPSO/RP

    -
    -

    Example

    +
    +

    SMPSO/RP

    +
    +

    Example

    [1]:
    -
    -from jmetal.algorithm.multiobjective.smpso import SMPSORP
+
    from jmetal.algorithm.multiobjective.smpso import SMPSORP
 from jmetal.operator import PolynomialMutation
 from jmetal.problem import ZDT4
 from jmetal.util.archive import CrowdingDistanceArchiveWithReferencePoint
@@ -375,8 +189,7 @@ 
    Example
    [3]:
 
    
 
    
-
    -from jmetal.lab.visualization.plotting import Plot
+
    from jmetal.lab.visualization.plotting import Plot
 from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
@@ -393,64 +206,64 @@ 
    Example
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.smpso.SMPSORP(problem: jmetal.core.problem.FloatProblem, swarm_size: int, mutation: jmetal.core.operator.Mutation, reference_points: List[List[float]], leaders: List[jmetal.util.archive.ArchiveWithReferencePoint], termination_criterion: jmetal.util.termination_criterion.TerminationCriterion, swarm_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    
-
    Bases: jmetal.algorithm.multiobjective.smpso.SMPSO
    
-
    
-
    
-get_name() → str[source]
    
-
    
-
-
    
-
    
-get_reference_point()[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.algorithm.multiobjective.smpso.SMPSORP(problem: ~jmetal.core.problem.FloatProblem, swarm_size: int, mutation: ~jmetal.core.operator.Mutation, reference_points: ~typing.List[~typing.List[float]], leaders: ~typing.List[~jmetal.util.archive.ArchiveWithReferencePoint], termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion, swarm_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]
    +

    Bases: SMPSO

    +
    +
    +get_name() → str[source]
    -
    -
    -get_result() → List[jmetal.core.solution.FloatSolution][source]
    +
    +
    +get_reference_point()[source]
    -
    -
    -init_progress() → None[source]
    +
    +
    +init_progress() → None[source]

    Initialize the algorithm.

    -
    -
    -initialize_global_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +initialize_global_best(swarm: List[FloatSolution]) → None[source]
    -
    -
    -select_global_best() → jmetal.core.solution.FloatSolution[source]
    +
    +
    +result() → List[FloatSolution][source]
    -
    -
    -update_global_best(swarm: List[jmetal.core.solution.FloatSolution]) → None[source]
    +
    +
    +select_global_best() → FloatSolution[source]
    -
    -
    -update_progress() → None[source]
    +
    +
    +update_global_best(swarm: List[FloatSolution]) → None[source]
    +
    + +
    +
    +update_progress() → None[source]

    Update the progress after each iteration.

    -
    -
    -update_reference_point(new_reference_points: list)[source]
    +
    +
    +update_reference_point(new_reference_points: list)[source]
    -
    -
    + +
    @@ -467,7 +280,8 @@

    Navigation

  • modules |
    • -
  • jMetalPy 1.5.3 documentation »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • SMPSO/RP
    • - - - - - - - + Evolution Strategy — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

    Navigation

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  • jMetalPy 1.5.3 documentation »
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    • +
  • Evolution Strategy
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -92,6 +90,30 @@

    Table Of Contents

    +
    +
    +

    Contents

    +
    + + +
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    +
    - - - - - - - + Genetic Algorithm — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

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    • @@ -59,7 +57,7 @@

    Navigation

    @@ -92,6 +90,30 @@

    Table Of Contents

    +
    +
    +

    Contents

    +
    + + +
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    +
    - - - - - - - + Local Search — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

    Navigation

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    • -
  • jMetalPy 1.5.3 documentation »
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  • jMetalPy 1.7.0 documentation »
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  • Local Search
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -92,6 +90,31 @@

    Table Of Contents

    +
    +
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    Contents

    +
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    +
    - - - - - - - + Simulated annealing — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

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    • -
  • jMetalPy 1.5.3 documentation »
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  • Simulated annealing
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -92,6 +90,32 @@

    Table Of Contents

    +
    +
    +

    Contents

    +
    + + +
    +
    +
    - - - - - - - + jmetal.lab.statistical_test package — jMetalPy 1.7.0 documentation + + + + + + + @@ -39,7 +36,8 @@

    Navigation

  • modules |
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  • jMetalPy 1.5.3 documentation »
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    • @@ -50,7 +48,7 @@

    Navigation

    @@ -84,10 +82,38 @@

    Contents

    - - - - - - - + Crossover — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

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  • jMetalPy 1.5.3 documentation »
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    • +
  • Crossover
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -91,6 +89,75 @@

    Table Of Contents

    +
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    Contents

    +
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    - - - - - - - + Mutation — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

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    • @@ -59,7 +57,7 @@

    Navigation

    @@ -91,6 +89,70 @@

    Table Of Contents

    +
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    Contents

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    - - - - - - - + Selection — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

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    • @@ -59,7 +57,7 @@

    Navigation

    @@ -91,6 +89,68 @@

    Table Of Contents

    +
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    Contents

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    - - - - - - - + Multi-objective problems — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

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    • +
  • Multi-objective problems
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -96,13 +94,367 @@

    Contents

    @@ -140,277 +492,408 @@

    Contents

    -
    -

    Multi-objective problems

    -
    -

    Constrained

    -
    -
    -class jmetal.problem.multiobjective.constrained.Binh2[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    +
    +

    Multi-objective problems

    +
    +

    Constrained

    +
    +
    +class jmetal.problem.multiobjective.constrained.Binh2[source]
    +

    Bases: FloatProblem

    Class representing problem Binh2.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.constrained.Osyczka2[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    +
    +
    +class jmetal.problem.multiobjective.constrained.Osyczka2[source]
    +

    Bases: FloatProblem

    Class representing problem Osyczka2.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.constrained.Srinivas[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    +
    +
    +class jmetal.problem.multiobjective.constrained.Srinivas[source]
    +

    Bases: FloatProblem

    Class representing problem Srinivas.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.constrained.Tanaka[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    +
    +
    +class jmetal.problem.multiobjective.constrained.Tanaka[source]
    +

    Bases: FloatProblem

    Class representing problem Tanaka.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -

    Unconstrained

    -
    -
    -class jmetal.problem.multiobjective.unconstrained.Fonseca[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    + +
    +

    Unconstrained

    +
    +
    +class jmetal.problem.multiobjective.unconstrained.Fonseca[source]
    +

    Bases: FloatProblem

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.unconstrained.Kursawe(number_of_variables: int = 3)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    +
    +
    +class jmetal.problem.multiobjective.unconstrained.Kursawe(number_of_variables: int = 3)[source]
    +

    Bases: FloatProblem

    Class representing problem Kursawe.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.unconstrained.OneZeroMax(number_of_bits: int = 256)[source]
    -

    Bases: jmetal.core.problem.BinaryProblem

    -
    -
    -create_solution() → jmetal.core.solution.BinarySolution[source]
    +
    +
    +class jmetal.problem.multiobjective.unconstrained.MixedIntegerFloatProblem(number_of_integer_variables=10, number_of_float_variables=10, n=100, m=-100, lower_bound=-1000, upper_bound=1000)[source]
    +

    Bases: Problem

    +
    +
    +create_solution() → CompositeSolution[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    -
    -
    -evaluate(solution: jmetal.core.solution.BinarySolution) → jmetal.core.solution.BinarySolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: CompositeSolution) → CompositeSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name() → str[source]
    +
    +
    +name() → str[source]
    -
    -
    -class jmetal.problem.multiobjective.unconstrained.Schaffer[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +class jmetal.problem.multiobjective.unconstrained.OneZeroMax(number_of_bits: int = 256)[source]
    +

    Bases: BinaryProblem

    +

    The implementation of the OneZeroMax problems defines a single binary variable. This variable +will contain the bit string representing the solutions.

    +
    +
    +create_solution() → BinarySolution[source]
    +

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:
    +

    Solution.

    +
    +
    +
    + +
    +
    +evaluate(solution: BinarySolution) → BinarySolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name() → str[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    +
    +number_of_variables() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.unconstrained.SubsetSum(C: int, W: list)[source]
    -

    Bases: jmetal.core.problem.BinaryProblem

    -
    -
    -create_solution() → jmetal.core.solution.BinarySolution[source]
    +
    +
    +class jmetal.problem.multiobjective.unconstrained.Schaffer[source]
    +

    Bases: FloatProblem

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    Returns:
    +

    Evaluated solution.

    +
    +
    +
    + +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    + +
    +
    +class jmetal.problem.multiobjective.unconstrained.SubsetSum(C: int, W: list)[source]
    +

    Bases: BinaryProblem

    +
    +
    +create_solution() → BinarySolution[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    -
    -
    -evaluate(solution: jmetal.core.solution.BinarySolution) → jmetal.core.solution.BinarySolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: BinarySolution) → BinarySolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name() → str[source]
    +
    +
    +name() → str[source]
    -
    -
    -class jmetal.problem.multiobjective.unconstrained.Viennet2[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +class jmetal.problem.multiobjective.unconstrained.Viennet2[source]
    +

    Bases: FloatProblem

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -

    ZDT

    -
    -
    -class jmetal.problem.multiobjective.zdt.ZDT1(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    + +
    +

    ZDT

    +
    +
    +class jmetal.problem.multiobjective.zdt.ZDT1(number_of_variables: int = 30)[source]
    +

    Bases: FloatProblem

    Problem ZDT1.

    Note

    @@ -420,39 +903,77 @@

    Multi-objective problemsNote

    Continuous problem having a convex Pareto front

    -
    -
    -eval_g(solution: jmetal.core.solution.FloatSolution)[source]
    +
    +
    +eval_g(solution: FloatSolution)[source]
    -
    -
    -eval_h(f: float, g: float) → float[source]
    +
    +
    +eval_h(f: float, g: float) → float[source]
    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    +
    +number_of_variables() → int[source]
    +
    + +
    + +
    +
    +class jmetal.problem.multiobjective.zdt.ZDT1Modified(number_of_variables=30)[source]
    +

    Bases: ZDT1

    +

    Problem ZDT1Modified.

    +
    +

    Note

    +

    Version including a loop for increasing the computing time of the evaluation functions.

    +
    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    Returns:
    +

    Evaluated solution.

    +
    +
    +
    +
    -
    -
    -class jmetal.problem.multiobjective.zdt.ZDT2(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.zdt.ZDT1

    +
    +
    +class jmetal.problem.multiobjective.zdt.ZDT2(number_of_variables: int = 30)[source]
    +

    Bases: ZDT1

    Problem ZDT2.

    Note

    @@ -462,22 +983,22 @@

    Multi-objective problemsNote

    Continuous problem having a non-convex Pareto front

    -
    -
    -eval_h(f: float, g: float) → float[source]
    +
    +
    +eval_h(f: float, g: float) → float[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.zdt.ZDT3(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.zdt.ZDT1

    +
    +
    +class jmetal.problem.multiobjective.zdt.ZDT3(number_of_variables: int = 30)[source]
    +

    Bases: ZDT1

    Problem ZDT3.

    Note

    @@ -487,22 +1008,22 @@

    Multi-objective problemsNote

    Continuous problem having a partitioned Pareto front

    -
    -
    -eval_h(f: float, g: float) → float[source]
    +
    +
    +eval_h(f: float, g: float) → float[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.zdt.ZDT4(number_of_variables: int = 10)[source]
    -

    Bases: jmetal.problem.multiobjective.zdt.ZDT1

    +
    +
    +class jmetal.problem.multiobjective.zdt.ZDT4(number_of_variables: int = 10)[source]
    +

    Bases: ZDT1

    Problem ZDT4.

    Note

    @@ -512,27 +1033,91 @@

    Multi-objective problemsNote

    Continuous multi-modal problem having a convex Pareto front

    -
    -
    -eval_g(solution: jmetal.core.solution.FloatSolution)[source]
    +
    +
    +eval_g(solution: FloatSolution)[source]
    -
    -
    -eval_h(f: float, g: float) → float[source]
    +
    +
    +eval_h(f: float, g: float) → float[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.zdt.ZDT6(number_of_variables: int = 10)[source]
    -

    Bases: jmetal.problem.multiobjective.zdt.ZDT1

    +
    +
    +class jmetal.problem.multiobjective.zdt.ZDT5(number_of_variables: int = 11)[source]
    +

    Bases: BinaryProblem

    +

    Problem ZDT5.

    +
    +

    Note

    +

    Bi-objective binary unconstrained problem. The default number of variables is 11.

    +
    +
    +
    +create_solution() → BinarySolution[source]
    +

    Creates a random_search solution to the problem.

    +
    +
    Returns:
    +

    Solution.

    +
    +
    +
    + +
    +
    +eval_g(solution: BinarySolution)[source]
    +
    + +
    +
    +eval_v(value)[source]
    +
    + +
    +
    +evaluate(solution: BinarySolution) → BinarySolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    Returns:
    +

    Evaluated solution.

    +
    +
    +
    + +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    +
    +number_of_variables() → int[source]
    +
    + +
    + +
    +
    +class jmetal.problem.multiobjective.zdt.ZDT6(number_of_variables: int = 10)[source]
    +

    Bases: ZDT1

    Problem ZDT6.

    Note

    @@ -542,942 +1127,1069 @@

    Multi-objective problemsNote

    Continuous problem having a non-convex Pareto front

    -
    -
    -eval_g(solution: jmetal.core.solution.FloatSolution)[source]
    +
    +
    +eval_g(solution: FloatSolution)[source]
    -
    -
    -eval_h(f: float, g: float) → float[source]
    +
    +
    +eval_h(f: float, g: float) → float[source]
    -
    -
    -get_name()[source]
    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    Returns:
    +

    Evaluated solution.

    +
    +
    +
    + +
    +
    +name()[source]
    -
    -
    -

    DTLZ

    -
    -
    -class jmetal.problem.multiobjective.dtlz.DTLZ1(number_of_variables: int = 7, number_of_objectives=3)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    + +
    +

    DTLZ

    +
    +
    +class jmetal.problem.multiobjective.dtlz.DTLZ1(number_of_variables: int = 7, number_of_objectives=3)[source]
    +

    Bases: FloatProblem

    Problem DTLZ1. Continuous problem having a flat Pareto front

    Note

    Unconstrained problem. The default number of variables and objectives are, respectively, 7 and 3.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    +
    +number_of_variables() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.dtlz.DTLZ2(number_of_variables: int = 12, number_of_objectives=3)[source]
    -

    Bases: jmetal.problem.multiobjective.dtlz.DTLZ1

    +
    +
    +class jmetal.problem.multiobjective.dtlz.DTLZ2(number_of_variables: int = 12, number_of_objectives=3)[source]
    +

    Bases: DTLZ1

    Problem DTLZ2. Continuous problem having a convex Pareto front

    Note

    Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.dtlz.DTLZ3(number_of_variables: int = 12, number_of_objectives=3)[source]
    -

    Bases: jmetal.problem.multiobjective.dtlz.DTLZ1

    +
    +
    +class jmetal.problem.multiobjective.dtlz.DTLZ3(number_of_variables: int = 12, number_of_objectives=3)[source]
    +

    Bases: DTLZ1

    Problem DTLZ3. Continuous problem having a convex Pareto front

    Note

    Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.dtlz.DTLZ4(number_of_variables: int = 12, number_of_objectives=3)[source]
    -

    Bases: jmetal.problem.multiobjective.dtlz.DTLZ1

    +
    +
    +class jmetal.problem.multiobjective.dtlz.DTLZ4(number_of_variables: int = 12, number_of_objectives=3)[source]
    +

    Bases: DTLZ1

    Problem DTLZ4. Continuous problem having a convex Pareto front

    Note

    Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.dtlz.DTLZ5(number_of_variables: int = 12, number_of_objectives=3)[source]
    -

    Bases: jmetal.problem.multiobjective.dtlz.DTLZ1

    +
    +
    +class jmetal.problem.multiobjective.dtlz.DTLZ5(number_of_variables: int = 12, number_of_objectives=3)[source]
    +

    Bases: DTLZ1

    Problem DTLZ5. Continuous problem having a convex Pareto front

    Note

    Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.dtlz.DTLZ6(number_of_variables: int = 12, number_of_objectives=3)[source]
    -

    Bases: jmetal.problem.multiobjective.dtlz.DTLZ1

    +
    +
    +class jmetal.problem.multiobjective.dtlz.DTLZ6(number_of_variables: int = 12, number_of_objectives=3)[source]
    +

    Bases: DTLZ1

    Problem DTLZ6. Continuous problem having a convex Pareto front

    Note

    Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.dtlz.DTLZ7(number_of_variables: int = 22, number_of_objectives=3)[source]
    -

    Bases: jmetal.problem.multiobjective.dtlz.DTLZ1

    +
    +
    +class jmetal.problem.multiobjective.dtlz.DTLZ7(number_of_variables: int = 22, number_of_objectives=3)[source]
    +

    Bases: DTLZ1

    Problem DTLZ6. Continuous problem having a disconnected Pareto front

    Note

    Unconstrained problem. The default number of variables and objectives are, respectively, 22 and 3.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -

    FDA

    -
    -
    -class jmetal.problem.multiobjective.fda.FDA[source]
    -

    Bases: jmetal.core.problem.DynamicProblem, jmetal.core.problem.FloatProblem, abc.ABC

    -
    -
    -clear_changed() → None[source]
    -
    - -
    -
    -abstract evaluate(solution: jmetal.core.solution.FloatSolution)[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    + +
    +

    FDA

    +
    +
    +class jmetal.problem.multiobjective.fda.FDA[source]
    +

    Bases: DynamicProblem, FloatProblem, ABC

    +
    +
    +clear_changed() → None[source]
    +
    + +
    +
    +abstract evaluate(solution: FloatSolution)[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -the_problem_has_changed() → bool[source]
    +
    +
    +the_problem_has_changed() → bool[source]
    -
    -
    -update(*args, **kwargs)[source]
    +
    +
    +update(*args, **kwargs)[source]

    Update method.

    -
    -
    -class jmetal.problem.multiobjective.fda.FDA1(number_of_variables: int = 100)[source]
    -

    Bases: jmetal.problem.multiobjective.fda.FDA

    +
    +
    +class jmetal.problem.multiobjective.fda.FDA1(number_of_variables: int = 100)[source]
    +

    Bases: FDA

    Problem FDA1.

    Note

    Bi-objective dynamic unconstrained problem. The default number of variables is 100.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +get_name()[source]
    -
    -
    -class jmetal.problem.multiobjective.fda.FDA2(number_of_variables: int = 31)[source]
    -

    Bases: jmetal.problem.multiobjective.fda.FDA

    +
    +
    +class jmetal.problem.multiobjective.fda.FDA2(number_of_variables: int = 31)[source]
    +

    Bases: FDA

    Problem FDA2

    Note

    Bi-objective dynamic unconstrained problem. The default number of variables is 31.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +get_name()[source]
    -
    -
    -class jmetal.problem.multiobjective.fda.FDA3(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.fda.FDA

    +
    +
    +class jmetal.problem.multiobjective.fda.FDA3(number_of_variables: int = 30)[source]
    +

    Bases: FDA

    Problem FDA3

    Note

    Bi-objective dynamic unconstrained problem. The default number of variables is 30.

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +get_name()[source]
    -
    -
    -class jmetal.problem.multiobjective.fda.FDA4(number_of_variables: int = 12)[source]
    -

    Bases: jmetal.problem.multiobjective.fda.FDA

    +
    +
    +class jmetal.problem.multiobjective.fda.FDA4(number_of_variables: int = 12)[source]
    +

    Bases: FDA

    Problem FDA4

    Note

    Three-objective dynamic unconstrained problem. The default number of variables is 12.

    -
    -
    -M = 3
    +
    +
    +M = 3
    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +get_name()[source]
    -
    -
    -class jmetal.problem.multiobjective.fda.FDA5(number_of_variables: int = 12)[source]
    -

    Bases: jmetal.problem.multiobjective.fda.FDA

    +
    +
    +class jmetal.problem.multiobjective.fda.FDA5(number_of_variables: int = 12)[source]
    +

    Bases: FDA

    Problem FDA5

    Note

    Three-objective dynamic unconstrained problem. The default number of variables is 12.

    -
    -
    -M = 3
    +
    +
    +M = 3
    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +get_name()[source]
    -
    -
    -

    LIRCMOP

    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP1(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    + +
    +

    LIRCMOP

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP1(number_of_variables: int = 30)[source]
    +

    Bases: FloatProblem

    Class representing problem LIR-CMOP1, defined in:

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -g1(x: [<class 'float'>]) → float[source]
    +
    +
    +g1(x: List[float]) → float[source]
    -
    -
    -g2(x: [<class 'float'>]) → float[source]
    +
    +
    +g2(x: List[float]) → float[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP10(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP8

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP10(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP8

    Class representing problem LIR-CMOP10, defined in:

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP11(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP10

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP11(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP10

    Class representing problem LIR-CMOP11, defined in:

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP12(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP9

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP12(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP9

    Class representing problem LIR-CMOP9, defined in:

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP13(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP13(number_of_variables: int = 30)[source]
    +

    Bases: FloatProblem

    Class representing problem LIR-CMOP13, defined in:

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    +
    + +
    +
    +g1(x: [<class 'float'>]) → float[source]
    +
    + +
    +
    +name()[source]
    -
    -
    -g1(x: [<class 'float'>]) → float[source]
    +
    +
    +number_of_constraints() → int[source]
    -
    -
    -get_name()[source]
    +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP14(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP13

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP14(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP13

    Class representing problem LIR-CMOP14, defined in:

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    +
    + +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    -
    -
    -get_name()[source]
    +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP2(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP1

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP2(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP1

    Class representing problem LIR-CMOP1, defined in:

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP3(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP1

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP3(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP1

    Class representing problem LIR-CMOP3, defined in:

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP4(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP2

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP4(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP2

    Class representing problem LIR-CMOP4, defined in:

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    +
    + +
    +
    +name()[source]
    -
    -
    -get_name()[source]
    +
    +
    +number_of_constraints() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP5(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP5(number_of_variables: int = 30)[source]
    +

    Bases: FloatProblem

    Class representing problem LIR-CMOP5, defined in:

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -g1(x: [<class 'float'>]) → float[source]
    +
    +
    +g1(x: [<class 'float'>]) → float[source]
    -
    -
    -g2(x: [<class 'float'>]) → float[source]
    +
    +
    +g2(x: [<class 'float'>]) → float[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP6(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP5

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP6(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP5

    Class representing problem LIR-CMOP6, defined in:

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP7(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP5

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP7(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP5

    Class representing problem LIR-CMOP7, defined in:

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP8(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP6

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP8(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP6

    Class representing problem LIR-CMOP8, defined in:

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -class jmetal.problem.multiobjective.lircmop.LIRCMOP9(number_of_variables: int = 30)[source]
    -

    Bases: jmetal.problem.multiobjective.lircmop.LIRCMOP8

    +
    +
    +class jmetal.problem.multiobjective.lircmop.LIRCMOP9(number_of_variables: int = 30)[source]
    +

    Bases: LIRCMOP8

    Class representing problem LIR-CMOP9, defined in:

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    +
    +
    +evaluate_constraints(solution: FloatSolution) → FloatSolution[source]
    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -

    LZ09

    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09(number_of_variables: int, number_of_objectives: int, number_of_constraints: int, ptype: int, dtype: int, ltype: int)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    + +
    +

    LZ09

    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09(number_of_variables: int, ptype: int, dtype: int, ltype: int)[source]
    +

    Bases: FloatProblem

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +get_name()[source]
    -
    -
    -objective(x_variables: list) → list[source]
    +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +objective(x_variables: list) → list[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F1[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F1(number_of_variables=10)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F2[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F2(number_of_variables=30)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F3[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F3(number_of_variables=30)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F4[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F4(number_of_variables=30)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F5[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F5(number_of_variables=30)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F6[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F6(number_of_variables=10)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F7[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F7(number_of_variables=10)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F8[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F8(number_of_variables=10)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.multiobjective.lz09.LZ09_F9[source]
    -

    Bases: jmetal.problem.multiobjective.lz09.LZ09

    -
    -
    -get_name()[source]
    +
    +
    +class jmetal.problem.multiobjective.lz09.LZ09_F9(number_of_variables=30)[source]
    +

    Bases: LZ09

    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    + +
    @@ -1512,8 +2224,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Problems »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Problems »
    • +
  • Multi-objective problems
    • - - - - - - - + Single-objective problems — jMetalPy 1.7.0 documentation + + + + + + + @@ -43,8 +40,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Problems »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Problems »
    • +
  • Single-objective problems
    • @@ -55,7 +53,7 @@

    Navigation

    @@ -92,9 +90,65 @@

    Contents

    @@ -132,207 +186,284 @@

    Contents

    -
    -

    Single-objective problems

    -
    -

    Unconstrained

    -
    -
    -class jmetal.problem.singleobjective.unconstrained.OneMax(number_of_bits: int = 256)[source]
    -

    Bases: jmetal.core.problem.BinaryProblem

    -
    -
    -create_solution() → jmetal.core.solution.BinarySolution[source]
    +
    +

    Single-objective problems

    +
    +

    Unconstrained

    +
    +
    +class jmetal.problem.singleobjective.unconstrained.OneMax(number_of_bits: int = 256)[source]
    +

    Bases: BinaryProblem

    +

    The implementation of the OneMax problems defines a single binary variable. This variable +will contain the bit string representing the solutions.

    +
    +
    +create_solution() → BinarySolution[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    -
    -
    -evaluate(solution: jmetal.core.solution.BinarySolution) → jmetal.core.solution.BinarySolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: BinarySolution) → BinarySolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name() → str[source]
    +
    +
    +name() → str[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    +
    +number_of_variables() → int[source]
    -
    -
    -class jmetal.problem.singleobjective.unconstrained.Rastrigin(number_of_variables: int = 10)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +class jmetal.problem.singleobjective.unconstrained.Rastrigin(number_of_variables: int = 10)[source]
    +

    Bases: FloatProblem

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name() → str[source]
    +
    +
    +name() → str[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.singleobjective.unconstrained.Sphere(number_of_variables: int = 10)[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +class jmetal.problem.singleobjective.unconstrained.Sphere(number_of_variables: int = 10)[source]
    +

    Bases: FloatProblem

    +
    +
    +evaluate(solution: FloatSolution) → FloatSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name() → str[source]
    +
    +
    +name() → str[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    -
    -
    -class jmetal.problem.singleobjective.unconstrained.SubsetSum(C: int, W: list)[source]
    -

    Bases: jmetal.core.problem.BinaryProblem

    -
    -
    -create_solution() → jmetal.core.solution.BinarySolution[source]
    +
    +
    +class jmetal.problem.singleobjective.unconstrained.SubsetSum(C: int, W: list)[source]
    +

    Bases: BinaryProblem

    +
    +
    +create_solution() → BinarySolution[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    -
    -
    -evaluate(solution: jmetal.core.solution.BinarySolution) → jmetal.core.solution.BinarySolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: BinarySolution) → BinarySolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name() → str[source]
    +
    +
    +name() → str[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    +
    +number_of_variables() → int[source]
    -
    -
    -

    Knapsack

    -
    -
    -class jmetal.problem.singleobjective.knapsack.Knapsack(number_of_items: int = 50, capacity: float = 1000, weights: list = None, profits: list = None, from_file: bool = False, filename: str = None)[source]
    -

    Bases: jmetal.core.problem.BinaryProblem

    + +
    +

    Knapsack

    +
    +
    +class jmetal.problem.singleobjective.knapsack.Knapsack(number_of_items: int = 50, capacity: float = 1000, weights: list = None, profits: list = None, from_file: bool = False, filename: str = None)[source]
    +

    Bases: BinaryProblem

    Class representing Knapsack Problem.

    -
    -
    -create_solution() → jmetal.core.solution.BinarySolution[source]
    +
    +
    +create_solution() → BinarySolution[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    -
    -
    -evaluate(solution: jmetal.core.solution.BinarySolution) → jmetal.core.solution.BinarySolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: BinarySolution) → BinarySolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    +
    + +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    +
    +number_of_variables() → int[source]
    -
    -
    -

    TSP

    -
    -
    -class jmetal.problem.singleobjective.tsp.TSP(instance: str = None)[source]
    -

    Bases: jmetal.core.problem.PermutationProblem

    + +
    +

    TSP

    +
    +
    +class jmetal.problem.singleobjective.tsp.TSP(instance: str = None)[source]
    +

    Bases: PermutationProblem

    Class representing TSP Problem.

    -
    -
    -create_solution() → jmetal.core.solution.PermutationSolution[source]
    +
    +
    +create_solution() → PermutationSolution[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    -
    -
    -evaluate(solution: jmetal.core.solution.PermutationSolution) → jmetal.core.solution.PermutationSolution[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: PermutationSolution) → PermutationSolution[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name()[source]
    +
    +
    +name()[source]
    -
    -
    -property number_of_cities
    +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    +
    +number_of_variables() → int[source]
    -
    -
    + +
    @@ -361,8 +492,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Problems »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Problems »
    • +
  • Single-objective problems
    • + + + + + + + + + + + + + +
    + +
    + +
    + +
    + + + +
    + +
    +

    Contributing

    +

    Contributions to the jMetalPy project are welcome. +Please, take into account the following guidelines (all developers should follow these guidelines):

    +
    +

    Git WorkFlow

    +

    We have a set of branches on the remote Git server. +Some branches are temporary, and others are constant throughout the life of the repository.

    +
      +
    • +
      Branches always present in the repository:
        +
      • +
        master: You have the latest released to production, receive merges from the develop branch, or merge from a hotfix branch (emergency).
          +

        • Do I have to put a TAG when doing a merge from develop to master? yes

          • +

        • Do I have to put a TAG when doing a merge from a hotfix branch to master? yes

          • +

        • After merge from a hotfix to master, do I have to merge from master to develop? yes

          • +
        +
        +
        +
        • +

      • develop: It is considered the “Next Release”, receives merges from branches of each developer, either corrections (fix) or new features (feature).

        • +
      +
      +
      +
      • +
    • +
      Temporary branches:
        +
      • +
        feature/<task-id>-<description>: When we are doing a development, we create a local branch with the prefix “feature/”, then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are developing. The words are separated by hyphens.
          +

        • Where does this branch emerge? This branch always emerge from the develop branch

          • +

        • When I finish the development in my feature branch, which branch to merge into?: You always merge feature branch into develop branch

          • +
        +
        +
        +
        • +
      • +
        fix/<task-id>-<description>: When we are making a correction, we create a local branch with the prefix “fix/”, then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are correcting. The words are separated by hyphens.
          +

        • Where does this branch emerge? This branch always emerge from the develop branch

          • +

        • When I finish the correction in my fix branch, which branch to merge into?: You always merge feature branch into develop branch

          • +
        +
        +
        +
        • +
      • +
        hotfix/<task-id>-<description>: When we are correcting an emergency incidence in production, we create a local branch with the prefix “hotfix/”, then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are correcting. The words are separated by hyphens.
          +

        • Where does this branch emerge?: This branch always emerge from the master branch

          • +

        • When I finish the correction in my hotfix branch, which branch to merge into?: This branch always emerge from the master and develop branch

          • +
        +
        +
        +
        • +
      +
      +
      +
      • +
    • +
      Steps to follow when you are creating or going to work on a branch of any kind (feature / fix / hotfix):
        +

      1. After you create your branch (feature / fix / hotfix) locally, upload it to the remote Git server. The integration system will verify your code from the outset.

        2. +

      2. Each time you commit, as much as possible, you send a push to the server. Each push will trigger the automated launch of the tests, etc.

        3. +

      3. Once the development is finished, having done a push to the remote Git server, and that the test phase has passed without problem, you create an pull request.

        4. +
      +
      +
      +
      • +
    +
    +

    Note

    +

    Do not forget to remove your branch (feature / fix / hotfix) once the merge has been made.

    +
    +

    Some useful Git commands:

    +
      +

    • git fetch –prune: Cleaning branches removed and bringing new branches

      • +
    +
    +
    +

    PEP8!

    +

    It is really important to follow some standards when a team develops an application. If all team members format the code in the same format, then it is much easier to read the code. PEP8 is Python’s style guide. It’s a set of rules for how to format your Python code.

    +

    Some style rules:

    +
      +

    • Package and module names: Modules should have short, all-lowercase names. Underscores can be used in the module name if it improves readability. Python packages should also have short, all-lowercase names, although the use of underscores is discouraged. In Python, a module is a file with the suffix ‘.py’.

      • +

    • Class names: Class names should normally use the CapWords convention.

      • +

    • Method names and instance variables: Lowercase with words separated by underscores as necessary to improve readability.

      • +
    +

    There are many more style standards in PEP8 so, please, refer to PEP8 documentation +. The most appropriate is to use an IDE that has support for PEP8. For example, PyCharm.

    +
    +
    +

    Object-oriented programming

    +

    Object-oriented programming should be the single programming paradigm used. Avoiding as far as possible, imperative and functional programming.

    +contributing/python_poo_programming.png +contributing/python_functional_programming.png +contributing/python_imperative_programming.png +

    In classes, we directly access the attributes, which are usually defined as public.

    +contributing/without_getter_setter.png +

    Only when we want to implement additional logic in the accesses to the attributes we define getter/setter methods, but always by using the *property* annotation or the *property* function:

    +contributing/property_annotation.png +contributing/property_functional.png +

    By using *property*, we continue to access the attributes directly:

    +contributing/good_access.png +

    Do not use getter/setter methods without the property annotation or the property function:

    +contributing/with_getter_setter.png +

    Since this way of accessing the attribute is not commonly used in Python:

    +contributing/bad_access.png +
    +
    +

    Structure

    +

    Python is not Java. In Java you cannot, by design, have more than one class in a file. In Python, you can do it.

    +

    In Python, it is appropriate to group several classes into a single .py file. For that reason, the .py files are called modules.

    +
    +
    +

    Python 3.6

    +

    We always define types in the parameters of the arguments and the return value:

    +contributing/types_in_methods.png +

    We can define abstract classes (ABCs) in Python:

    +contributing/abstract.png +

    In the case that we want to define an interface class, it is done in the same way. We just have to define all the methods of the class as abstract.

    +

    Example of use of generic types:

    +contributing/generic_types.png +

    In the code below, the IDE displays a warning, since although the 2nd parameter is a float type, which is a type allowed in the definition of the generic type X, it is not of the same type as the first, since the first 2 parameters must be of the same generic type (S):

    +contributing/instance_with_generic_types1_wearning.png +

    In the code below, the IDE displays a warning, since the 2nd parameter is a type not allowed in the definition of the generic type ( TypeVar(‘S’, int, float) ):

    +contributing/instance_with_generic_types2_wearning.png +

    When the class inherits from Generic[…], the class is defined as generic. In this way we can indicate the types that will have the values of the generic types, when using the class as type. Look at the add_car() method of the Parking class.

    +
    +

    Note

    +

    The generic classes inherit from abc.ABCMeta, so they are abstract classes and abstract methods can be used.

    +
    +contributing/generic_class1.png +contributing/generic_class2.png +

    In the code below, the IDE displays a warning in the call to the add_car() method when adding the car, since the 3rd parameter of the init must be a str type, as defined in the add_car() method of the Parking class.

    +contributing/instance_with_generic_class_wearning.png +

    When inheriting from generic classes, some type variables could be fixed:

    +contributing/generic_types_fixed.png +

    Example of inheritance from non-generic class to generic class:

    +contributing/inheritance_non_generic_to_generic.png +

    Example of inheritance from generic class to another generic class:

    +contributing/inheritance_generic_to_generic.png +
    +
    +

    Create automatic documentation files with Sphinx

    +

    First, you need to know how to correctly document your code. It is important to follow these simple rules in order to automatically create good documentation for the project.

    +

    When you create a new module file (testDoc.py in this example), you should mention it using this format:

    +
    """
+.. module:: testDoc
+   :platform: Unix, Windows
+   :synopsis: A useful module indeed.
+
+.. moduleauthor:: Andrew Carter <andrew@invalid.com>
+"""
+
+class testDoc(object):
+    """We use this as a public class example class.
+
+    This class is ruled by the very trendy important method :func:`public_fn_with_sphinxy_docstring`.
+
+    .. note::
+       An example of intersphinx is this: you **cannot** use :mod:`pickle` on this class.
+    """
+
+    def __init__(self):
+        pass
+
    +
    +

    This code snippet generates the following documentation:

    +contributing/class_header.png +

    Now, you can document your methods using the following sintax:

    +
    def public_fn_with_sphinxy_docstring(self, name: str, state: bool = False) -> int:
+    """This function does something.
+
+    :param name: The name to use.
+    :type name: str.
+    :param state: Current state to be in.
+    :type state: bool.
+    :returns:  int -- the return code.
+    :raises: AttributeError, KeyError
+    """
+    return 0
+
+def public_fn_without_docstring(self):
+    return True
+
    +
    +

    And the produced output doc will be:

    +contributing/method_way_sphinx.png +

    As you may notice, if you don’t use any docstring, the method documentation will be empty.

    +
    +
    + + +
    + +
    + +
    + ← Single-objective problems +
    + +
    + About → +
    +
    +
    + +
    +
    +
    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/genindex.html b/docs/genindex.html new file mode 100644 index 00000000..15e2f13a --- /dev/null +++ b/docs/genindex.html @@ -0,0 +1,2014 @@ + + + + + + + + + + + + + + + Index — jMetalPy 1.7.0 documentation + + + + + + + + + + + + + + + +
    + +
    + +
    +
    +
    + +
    +
    + Python version of the jMetal framework +
    +
    +
    +
    +

    Table Of Contents

    +
    +
    + + + + + +
    +
    +
    +
    + +
    +
    + +
    +
    +
    + + + +
    + + +

    Index

    + +
    + A + | B + | C + | D + | E + | F + | G + | H + | I + | J + | K + | L + | M + | N + | O + | P + | Q + | R + | S + | T + | U + | V + | W + | Z + +
    +

    A

    + + + +
    + +

    B

    + + + +
    + +

    C

    + + + +
    + +

    D

    + + + +
    + +

    E

    + + + +
    + +

    F

    + + + +
    + +

    G

    + + + +
    + +

    H

    + + + +
    + +

    I

    + + + +
    + +

    J

    + + + +
      +
    • + jmetal.algorithm.singleobjective.evolution_strategy + +
      • +
    • + jmetal.algorithm.singleobjective.genetic_algorithm + +
      • +
    • + jmetal.algorithm.singleobjective.local_search + +
      • +
    • + jmetal.algorithm.singleobjective.simulated_annealing + +
      • +
    • + jmetal.core.observer + +
      • +
    • + jmetal.core.problem + +
      • +
    • + jmetal.lab.experiment + +
      • +
    • + jmetal.lab.statistical_test + +
      • +
    • + jmetal.lab.statistical_test.apv_procedures + +
      • +
    • + jmetal.lab.statistical_test.bayesian + +
      • +
    • + jmetal.lab.statistical_test.critical_distance + +
      • +
    • + jmetal.lab.statistical_test.functions + +
      • +
    • + jmetal.lab.visualization.chord_plot + +
      • +
    • + jmetal.lab.visualization.interactive + +
      • +
    • + jmetal.lab.visualization.plotting + +
      • +
    • + jmetal.lab.visualization.posterior + +
      • +
      +
    • + jmetal.lab.visualization.streaming + +
      • +
    • + jmetal.operator.crossover + +
      • +
    • + jmetal.operator.mutation + +
      • +
    • + jmetal.operator.selection + +
      • +
    • + jmetal.problem.multiobjective.constrained + +
      • +
    • + jmetal.problem.multiobjective.dtlz + +
      • +
    • + jmetal.problem.multiobjective.fda + +
      • +
    • + jmetal.problem.multiobjective.lircmop + +
      • +
    • + jmetal.problem.multiobjective.lz09 + +
      • +
    • + jmetal.problem.multiobjective.unconstrained + +
      • +
    • + jmetal.problem.multiobjective.zdt + +
      • +
    • + jmetal.problem.singleobjective.knapsack + +
      • +
    • + jmetal.problem.singleobjective.tsp + +
      • +
    • + jmetal.problem.singleobjective.unconstrained + +
      • +
    • + jmetal.util.observer + +
      • +
    • Job (class in jmetal.lab.experiment) +
      • +
    + +

    K

    + + + +
    + +

    L

    + + + +
    + +

    M

    + + + +
    + +

    N

    + + + +
    + +

    O

    + + + +
    + +

    P

    + + + +
    + +

    Q

    + + + +
    + +

    R

    + + + +
    + +

    S

    + + + +
    + +

    T

    + + + +
    + +

    U

    + + + +
    + +

    V

    + + + +
    + +

    W

    + + +
    + +

    Z

    + + + +
    + + + +
    + +
    +
    +
    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 00000000..2b0c6c24 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,254 @@ + + + + + + + + + + + + + + + + jMetalPy: Python version of the jMetal framework — jMetalPy 1.7.0 documentation + + + + + + + + + + + + + + + + + + +
    + +
    + +
    +
    +
    + +
    +
    + Python version of the jMetal framework +
    +
    +
    +
    +

    Table Of Contents

    +
    +
    + + + + + +
    +
    +
    +
    +

    Contents

    +
    + + +
    +
    +
    +
    +
    + +
    +
    + +
    +
    +
    + + + +
    + +
    +

    jMetalPy: Python version of the jMetal framework

    +
    +

    Warning

    +

    Documentation is work in progress!! Some information may be missing or incomplete.

    +
    + + + + + + +

    Target doc

    v1.7.0

    +
    +

    Content

    +
    + +
    +
    +
    +

    Installation steps

    +

    Via pip:

    +
    $ pip install jmetalpy  # or "jmetalpy[distributed]"
+
    +
    +
    +

    Note

    +

    Alternatively, you can use one of these instead:

    +
    $ pip install "jmetalpy[core]"  # Install core components of the framework (equivalent to `pip install jmetalpy`)
+$ pip install "jmetalpy[docs]"  # Install requirements for building docs
+$ pip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computing
+$ pip install "jmetalpy[complete]"  # Install all dependencies
+
    +
    +
    +

    Via source code:

    +
    $ git clone https://github.com/jMetal/jMetalPy.git
+$ python setup.py install
+
    +
    +
    +
    +

    Summary of features

    +

    The current release of jMetalPy (v1.5.3) contains the following components:

    +
      +

    • Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, MOEA/D-DRA, MOEA/D-IEpsilon, GDE3, SPEA2, HYPE, IBEA. Preference articulation-based algorithms (G-NSGA-II, G-GDE3, G-SPEA2, SMPSO/RP); Dynamic versions of NSGA-II, SMPSO, and GDE3.

      • +

    • Parallel computing based on Apache Spark and Dask.

      • +

    • Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, LIR-CMOP, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).

      • +

    • Encodings: real, binary, permutations.

      • +

    • Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).

      • +

    • Quality indicators: hypervolume, additive epsilon, GD, IGD.

      • +

    • Pareto front approximation plotting in real-time, static or interactive.

      • +

    • Experiment class for performing studies either alone or alongside jMetal.

      • +

    • Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.

      • +
    +
    +
    +

    Cite us

    +
    @article{BENITEZHIDALGO2019100598,
+   title = "jMetalPy: A Python framework for multi-objective optimization with metaheuristics",
+   journal = "Swarm and Evolutionary Computation",
+   pages = "100598",
+   year = "2019",
+   issn = "2210-6502",
+   doi = "https://doi.org/10.1016/j.swevo.2019.100598",
+   url = "http://www.sciencedirect.com/science/article/pii/S2210650219301397",
+   author = "Antonio Benítez-Hidalgo and Antonio J. Nebro and José García-Nieto and Izaskun Oregi and Javier Del Ser",
+   keywords = "Multi-objective optimization, Metaheuristics, Software framework, Python, Statistical analysis, Visualization",
+}
+
    +
    +
    +
    + + +
    + +
    + +
    + Getting started → +
    +
    +
    + +
    +
    +
    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/multiobjective.algorithms.html b/docs/multiobjective.algorithms.html new file mode 100644 index 00000000..9c1220b6 --- /dev/null +++ b/docs/multiobjective.algorithms.html @@ -0,0 +1,192 @@ + + + + + + + + + + + + + + + + Multi-objective algorithms — jMetalPy 1.7.0 documentation + + + + + + + + + + + + + + + + + + +
    + +
    + +
    +
    +
    + +
    +
    + Python version of the jMetal framework +
    +
    +
    +
    +

    Table Of Contents

    +
    +
    + + + + + +
    +
    +
    +
    + +
    +
    + +
    +
    +
    + + + +
    + +
    +

    Multi-objective algorithms

    +
    +

    List of multi-objective algorithms:

    + +
    +
    + + +
    + +
    + +
    + ← Statistical analysis +
    + +
    + Evolutionary Algorithms → +
    +
    +
    + +
    +
    +
    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/objects.inv b/docs/objects.inv index aff7d7905a9840bfed7142620aa9a43cccbee974..c48e5ced0e34a9c65f88e8833fd1108df311d782 100644 GIT binary patch literal 16451 zcmY*=V{|4#*KKT0l8Gm_ZQHh;Jh5%toY=N)+nN}YWP&GlZr=C1SocS-I;X04onBSF zs=CkKMM9|JU}J*;pCV!+C7Z`0BWCi8nrY`$6^{U#q3e{4VxPrsI*INE|WqV$SS#2sBI{#)9Ak zq15?&D03te-0rNb%JP(yP1N(&xU8-11%dc#!c06yqI181mT%rdev;sDdSCGm!RKNy zZr6(j-t4b5J;r@p42=QzZeEeMS_U0%>|D z>0cmJ;ejwDG!e!CtIoSMsE(m=0X#hWu4Z5R%kk2kMcDKo$HY5$$ksGJrbPDaODGGN z2o(9^_pmktBfwpFsalTdMRxf{vq*?X>Z38I_@w<z(BKE)Dy+2V|@6l*Dp`C7wHXMSHr^0^ucnCi$ z1~_uj7$Fku`gcQ5-$+>lxr@b zvb8m>C*Fadpq$bsJP0%MJ1>5eX}J!qU8w}LBwXOa!J^6bp%g))`l$cn6H{r=-{tn@ zEa%a5ON7w?sZ^|0;<1bSqL`G3%CfG)O9e7$Sxi+jk4!M87W6}h(v{s#BfVI>PKI?_ ze}IEE$DBjZ6fs~HWa*_BbI}IZ1O!4QG>}@g3WTpnD0CWHxdk{Xp@`7**reMlPS-&H+9_#N^XxnQ0 zl6-rTSnf-7Imb%^O{P*m$#OhUYwdX!s+2y1VG2#^8x5f`A%^F<#RW5S=XGg!VbuL0 z^i89CWFiyl*cjONoS@(5nR4&`hO*%QEMqLF_C~#|9#AlvT_24F7I|z}mWEH!Pf^ 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    +
    + Python version of the jMetal framework +
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    +

    Table Of Contents

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    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/problems.html b/docs/problems.html new file mode 100644 index 00000000..a93e7961 --- /dev/null +++ b/docs/problems.html @@ -0,0 +1,190 @@ + + + + + + + + + + + + + + + + Problems — jMetalPy 1.7.0 documentation + + + + + + + + + + + + + + + + +
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    + +
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    + Python version of the jMetal framework +
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    +
    +

    Table Of Contents

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    +

    Problems

    +
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    Benchmarking problems:

    + +
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    + + +
    + +
    + +
    + ← Selection +
    + +
    + Multi-objective problems → +
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    +
    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/py-modindex.html b/docs/py-modindex.html new file mode 100644 index 00000000..89118e5d --- /dev/null +++ b/docs/py-modindex.html @@ -0,0 +1,332 @@ + + + + + + + + + + + + + + + Python Module Index — jMetalPy 1.7.0 documentation + + + + + + + + + + + + + + + + + + +
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    +
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"variabl": [10, 28, 29, 30, 38, 40], "variant": 25, "varieti": 37, "vergn": 0, "veri": 30, "verifi": 30, "version": [10, 24, 28], "via": 31, "viennet2": [28, 31], "virtual": 39, "visual": [0, 2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 17, 19, 31, 36], "visualizerobserv": [39, 42], "vol": 0, "w": [24, 28, 29, 40], "wai": [30, 42], "want": 30, "warn": 30, "we": [2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 17, 19, 30, 40], "weight": 29, "weight_files_path": 8, "welcom": 30, "wheel": 27, "when": 30, "where": [24, 30, 38], "whether": [25, 38], "whethev": 40, "which": [30, 39], "whitlei": 0, "whose": 40, "width": 42, "wilcoxon": 38, "window": 30, "without": [30, 40], "word": 30, "work": [24, 30, 31, 42], "writefronttofileobserv": [3, 11, 18, 39], "www": [0, 31], "x": [2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 17, 19, 24, 28, 30, 40, 42], "x_variabl": 28, "xv": 0, "y": [2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 17, 19, 24, 42], "ye": 30, "year": [0, 31], "you": [30, 31], "your": 30, "z": 28, "z_0": 24, "z_order": 42, "z_valu": 24, "zdt": [34, 38], "zdt1": [2, 5, 6, 9, 10, 12, 14, 16, 28, 31, 38, 42], "zdt1modifi": [10, 28], "zdt2": [4, 12, 28, 38, 42], "zdt3": [28, 38], "zdt4": [7, 17, 19, 28], "zdt5": 28, "zdt6": 28, "zoom": 42}, "titles": ["About", "Evolutionary Algorithms", "GDE3", "Dynamic GDE3", "Preference point-based GDE3", "HYPE", "IBEA", "MOCell", "MOEA/D", "NSGA-II", "Distributed NSGA-II", "Dynamic NSGA-II", "Preference point-based NSGA-II", "NSGA-III", "SPEA2", "PSO Algorithms", "OMOPSO", "SMPSO", "Dynamic SMPSO", "SMPSO/RP", "Evolution Strategy", "Genetic Algorithm", "Local Search", "Simulated annealing", "jmetal.lab.statistical_test package", "Crossover", "Mutation", "Selection", "Multi-objective problems", "Single-objective problems", "Contributing", "jMetalPy: Python version of the jMetal framework", "Multi-objective algorithms", "Operators", "Problems", "Single-objective algorithms", "Getting started", "Evaluate solutions", "Experiments", "Extending algorithms", "Defining new problems", "Statistical analysis", "Front visualization"], "titleterms": {"3": 30, "6": 30, "about": 0, "algorithm": [1, 15, 21, 32, 35, 39], "analysi": 41, "anneal": 23, "api": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 37, 38, 39, 40, 42], "apv_procedur": 24, "automat": 30, "base": [4, 12], "bayesian": 24, "benchmark": 34, "case": 40, "chord": 42, "cite": [0, 31], "constrain": 28, "content": [24, 31], "contribut": 30, "creat": 30, "critical_dist": 24, "crossov": 25, "d": 8, "defin": 40, "distribut": [10, 37], "document": 30, "dtlz": 28, "dynam": [3, 11, 18], "evalu": 37, "evolut": 20, "evolutionari": 1, "exampl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19], "experi": 38, "extend": 39, "fda": 28, "featur": 31, "file": [30, 38], "first": 36, "framework": 31, "front": 42, "function": 24, "gde3": [2, 3, 4], "genet": [21, 33], "get": 36, "git": 30, "hype": 5, "ibea": 6, "ii": [9, 10, 11, 12], "iii": 13, "instal": 31, "interact": 42, "jmetal": [24, 31], "jmetalpi": 31, "knapsack": 29, "lab": 24, "laboratori": 36, "lircmop": 28, "list": [32, 33, 35, 39], "local": 22, "lz09": 28, "mocel": 7, "modul": 24, "moea": 8, "multi": [1, 15, 28, 32, 40], "mutat": 26, "new": 40, "nsga": [9, 10, 11, 12, 13], "object": [1, 15, 28, 29, 30, 32, 35, 40], "observ": 39, "omopso": 16, "oper": 33, "optim": 15, "orient": 30, "packag": 24, "parallel": 37, "paramet": 24, "particl": 15, "pep8": 30, "plot": 42, "point": [4, 12], "posterior": 42, "prefer": [4, 12], "problem": [28, 29, 34, 40], "program": 30, "pso": 15, "python": [30, 31], "refer": 0, "return": [24, 42], "rp": 19, "run": 38, "search": 22, "select": 27, "sequenti": 37, "simul": 23, "singl": [29, 35], "smpso": [17, 18, 19], "solut": 37, "spea2": 14, "sphinx": 30, "start": 36, "static": 42, "statist": 41, "statistical_test": 24, "step": [31, 36], "strategi": 20, "stream": 42, "structur": 30, "submodul": 24, "subset": 40, "sum": 40, "summari": [31, 38], "swarm": 15, "tsp": 29, "u": [0, 31], "unconstrain": [28, 29], "us": 40, "version": 31, "visual": 42, "workflow": 30, "zdt": 28}}) \ No newline at end of file diff --git a/docs/singleobjective.algorithms.html b/docs/singleobjective.algorithms.html new file mode 100644 index 00000000..1e195a51 --- /dev/null +++ b/docs/singleobjective.algorithms.html @@ -0,0 +1,192 @@ + + + + + + + + + + + + + + + + Single-objective algorithms — jMetalPy 1.7.0 documentation + + + + + + + + + + + + + + + + +
    + +
    + +
    +
    +
    + +
    +
    + Python version of the jMetal framework +
    +
    +
    +
    +

    Table Of Contents

    +
    +
    + + + + + +
    +
    +
    +
    + +
    +
    + +
    +
    +
    + + + +
    + +
    +

    Single-objective algorithms

    +
    +

    List of single-objective algorithms:

    + +
    +
    + + +
    + +
    + +
    + ← SMPSO +
    + +
    + Evolution Strategy → +
    +
    +
    + +
    +
    +
    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/tutorials.html b/docs/tutorials.html new file mode 100644 index 00000000..f14c8a56 --- /dev/null +++ b/docs/tutorials.html @@ -0,0 +1,191 @@ + + + + + + + + + + + + + + + + Getting started — jMetalPy 1.7.0 documentation + + + + + + + + + + + + + + + + + + +
    + +
    + +
    +
    +
    + +
    +
    + Python version of the jMetal framework +
    +
    +
    +
    +

    Table Of Contents

    +
    +
    + + + + + +
    +
    +
    +
    + +
    +
    + +
    +
    +
    + + + +
    + +
    +

    Getting started

    +
    +

    First steps:

    + +
    +
    +

    Laboratory:

    + +
    +
    + + +
    + +
    + +
    + ← jMetalPy: Python version of the jMetal framework +
    + +
    + Defining new problems → +
    +
    +
    + +
    +
    +
    + + + +
    + © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
    + + \ No newline at end of file diff --git a/docs/tutorials/evaluator.html b/docs/tutorials/evaluator.html index 6d83df45..b1286238 100644 --- a/docs/tutorials/evaluator.html +++ b/docs/tutorials/evaluator.html @@ -1,9 +1,10 @@ - - + + + @@ -12,18 +13,14 @@ - Evaluate solutions — jMetalPy 1.5.3 documentation - - - - - - - - - - - + Evaluate solutions — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Evaluate solutions
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -101,11 +99,35 @@

    Contents

    - - - - - - - + Experiments — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Experiments
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -102,7 +100,22 @@

    Contents

  • Experiments
    • @@ -140,10 +153,10 @@

    Contents

    -
    -

    Experiments

    -
    -

    Running the experiment

    +
    +

    Experiments

    +
    +

    Running the experiment

    This is an example of an experimental study based on solving three problems of the ZDT family with three different algorithms: NSGA-II, GDE3 and SMPSO.

    The hypervolume, generational distance and epsilon indicators are used for performance assessment.

    @@ -223,9 +236,9 @@

    Running the experimentexperiment.run()

    -
    -
    -

    Summary file

    + +
    +

    Summary file

    The results of this experiment can be summarized to a CSV file as follows:

    if __name__ == '__main__':
     # experiment = ...
@@ -240,41 +253,81 @@ 
    Summary file
    $ head QualityIndicatorSummary.csv
+
    $ head QualityIndicatorSummary.csv
 Algorithm,Problem,IndicatorName,ExecutionId,IndicatorValue
 NSGAII,ZDT1,EP,0,0.015705992620067832
 NSGAII,ZDT1,EP,1,0.012832504015918067
 ...
 
    
 
    
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.lab.experiment.Experiment(output_dir: str, jobs: List[jmetal.lab.experiment.Job], m_workers: int = 6)[source]
    
-
    Bases: object
    
-
    
-
    
-run() → None[source]
    
+
    +
    +

    API

    +
    +
    +class jmetal.lab.experiment.Experiment(output_dir: str, jobs: List[Job], m_workers: int = 6)[source]
    +

    Bases: object

    +
    +
    +run() → None[source]
    -
    -
    -class jmetal.lab.experiment.Job(algorithm: jmetal.core.algorithm.Algorithm, algorithm_tag: str, problem_tag: str, run: int)[source]
    -

    Bases: object

    -
    -
    -execute(output_path: str = '')[source]
    +
    +
    +class jmetal.lab.experiment.Job(algorithm: Algorithm, algorithm_tag: str, problem_tag: str, run: int)[source]
    +

    Bases: object

    +
    +
    +execute(output_path: str = '')[source]
    +
    + +
    +
    +get_algorithm_data()[source]
    -
    -
    -jmetal.lab.experiment.generate_summary_from_experiment(input_dir: str, quality_indicators: List[jmetal.core.quality_indicator.QualityIndicator], reference_fronts: str = '')[source]
    +
    +
    +jmetal.lab.experiment.generate_kolmogorov_smirnov_latex_tables(filename: str, output_dir: str = 'latex/KolmogorovSmirnov')[source]
    +
    +
    Generate Latex tables with the results of the Kolmogorov-Smirnov test. The last algorithm is considered as

    the reference algorithm, and the cells include a symbol with the p-value < 0.05.

    +
    +
    +
    +
    Parameters:
    +
      +

    • filename – Input filename (summary).

      • +

    • output_dir – Output path.

      • +
    +
    +
    +
    + +
    +
    +jmetal.lab.experiment.generate_median_and_wilcoxon_latex_tables(filename: str, output_dir: str = 'latex/meansAndWilcoxon')[source]
    +
    +
    Generate Latex tables including medians and IQRs. Additionally, the last algorithm is considered as the reference

    algorithm, and the cells include a symbol indicating whether the differences with the reference algorithm +are significant or not according to the Wilcoxon rank sum test.

    +
    +
    +
    +
    Parameters:
    +
      +

    • filename – Input filename (summary).

      • +

    • output_dir – Output path.

      • +
    +
    +
    +
    + +
    +
    +jmetal.lab.experiment.generate_summary_from_experiment(input_dir: str, quality_indicators: List[QualityIndicator], reference_fronts: str = '')[source]

    Compute a list of quality indicators. The input data directory must met the following structure (this is generated automatically by the Experiment class):

    -
    -
    +
    +
    +jmetal.lab.experiment.logger = <Logger jmetal.lab.experiment (DEBUG)>
    +
    + + +
    @@ -346,8 +404,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Experiments
    • - - - - - - - + Extending algorithms — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
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  • Getting started »
    • +
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    • +
  • Getting started »
    • +
  • Extending algorithms
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -100,8 +98,47 @@

    Contents

    @@ -139,8 +176,8 @@

    Contents

    -
    -

    Extending algorithms

    +
    +

    Extending algorithms

    In jMetalPy, algorithms maintain a list of dependents or observers which are notified automatically after each iteration (think about event listeners). This is known as the observer pattern and can be used to extend the functionality of our algorithms by registering new observers.

    For example, a basic observer will log the current number of evaluations, the objective(s) from the best individual in the population and the current computing time:

    @@ -160,129 +197,129 @@

    Extending algorithms

    This will produce:

    -
    $ Progress:  50%|#####     | 12500/25000 [13:59<14:12, 14.66it/s]
+
    $ Progress:  50%|#####     | 12500/25000 [13:59<14:12, 14.66it/s]
 
    
 
    
 
    A full list of all available observers can be found at the jmetal.util.observer module.
    
-
    
-
    List of observers
    
-
    
-
    
-class jmetal.util.observer.BasicObserver(frequency: float = 1.0)[source]
    
-
    Bases: jmetal.core.observer.Observer
    
-
    
-
    
-update(*args, **kwargs)[source]
    
+
    
+
    List of observers
    
+
    
+
    
+class jmetal.util.observer.BasicObserver(frequency: int = 1)[source]
    
+
    Bases: Observer
    
+
    
+
    
+update(*args, **kwargs)[source]
    
 
    Update method.
    
 
    
 
 
    
 
-
    
-
    
-class jmetal.util.observer.PlotFrontToFileObserver(output_directory: str, step: int = 100, **kwargs)[source]
    
-
    Bases: jmetal.core.observer.Observer
    
-
    
-
    
-update(*args, **kwargs)[source]
    
+
    
+
    
+class jmetal.util.observer.PlotFrontToFileObserver(output_directory: str, step: int = 100, **kwargs)[source]
    
+
    Bases: Observer
    
+
    
+
    
+update(*args, **kwargs)[source]
    
 
    Update method.
    
 
    
 
 
    
 
-
    
-
    
-class jmetal.util.observer.PrintObjectivesObserver(frequency: float = 1.0)[source]
    
-
    Bases: jmetal.core.observer.Observer
    
-
    
-
    
-update(*args, **kwargs)[source]
    
+
    
+
    
+class jmetal.util.observer.PrintObjectivesObserver(frequency: int = 1)[source]
    
+
    Bases: Observer
    
+
    
+
    
+update(*args, **kwargs)[source]
    
 
    Update method.
    
 
    
 
 
    
 
-
    
-
    
-class jmetal.util.observer.ProgressBarObserver(max: int)[source]
    
-
    Bases: jmetal.core.observer.Observer
    
-
    
-
    
-update(*args, **kwargs)[source]
    
+
    
+
    
+class jmetal.util.observer.ProgressBarObserver(max: int)[source]
    
+
    Bases: Observer
    
+
    
+
    
+update(*args, **kwargs)[source]
    
 
    Update method.
    
 
    
 
 
    
 
-
    
-
    
-class jmetal.util.observer.VisualizerObserver(reference_front: List[S] = None, reference_point: list = None, display_frequency: float = 1.0)[source]
    
-
    Bases: jmetal.core.observer.Observer
    
-
    
-
    
-update(*args, **kwargs)[source]
    
+
    
+
    
+class jmetal.util.observer.VisualizerObserver(reference_front: List[S] = None, reference_point: list = None, display_frequency: int = 1)[source]
    
+
    Bases: Observer
    
+
    
+
    
+update(*args, **kwargs)[source]
    
 
    Update method.
    
 
    
 
 
    
 
-
    
-
    
-class jmetal.util.observer.WriteFrontToFileObserver(output_directory: str)[source]
    
-
    Bases: jmetal.core.observer.Observer
    
-
    
-
    
-update(*args, **kwargs)[source]
    
+
    
+
    
+class jmetal.util.observer.WriteFrontToFileObserver(output_directory: str)[source]
    
+
    Bases: Observer
    
+
    
+
    
+update(*args, **kwargs)[source]
    
 
    Update method.
    
 
    
 
 
    
 
-
    
-
    
-
    API
    
-
    
-
    
-class jmetal.core.observer.Observable[source]
    
-
    Bases: abc.ABC
    
-
    
-
    
-abstract deregister(observer)[source]
    
+
+
    
+
    API
    
+
    
+
    
+class jmetal.core.observer.Observable[source]
    
+
    Bases: ABC
    
+
    
+
    
+abstract deregister(observer)[source]
    
 
    
 
-
    
-
    
-abstract deregister_all()[source]
    
+
    
+
    
+abstract deregister_all()[source]
    
 
    
 
-
    
-
    
-abstract notify_all(*args, **kwargs)[source]
    
+
    
+
    
+abstract notify_all(*args, **kwargs)[source]
    
 
    
 
-
    
-
    
-abstract register(observer)[source]
    
+
    
+
    
+abstract register(observer)[source]
    
 
    Register a virtual subclass of an ABC.
    
 
    Returns the subclass, to allow usage as a class decorator.
    
 
    
 
 
    
 
-
    
-
    
-class jmetal.core.observer.Observer[source]
    
-
    Bases: abc.ABC
    
-
    
-
    
-abstract update(*args, **kwargs)[source]
    
+
    
+
    
+class jmetal.core.observer.Observer[source]
    
+
    Bases: ABC
    
+
    
+
    
+abstract update(*args, **kwargs)[source]
    
 
    Update method.
    
 
    
 
 
    
 
-
    
-
    + +
    @@ -317,8 +354,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Extending algorithms
    • - - - - - - - + Defining new problems — jMetalPy 1.7.0 documentation + + + + + + + + + @@ -47,8 +46,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Defining new problems
    • @@ -59,7 +59,7 @@

    Navigation

    @@ -102,7 +102,52 @@

    Contents

  • Defining new problems
    • @@ -140,11 +185,11 @@

    Contents

    -
    -

    Defining new problems

    +
    +

    Defining new problems

    To include a problem in jMetalPy, it must implement the Problem interface from the jmetal.core.problem module.

    -
    -

    Use case: Subset Sum

    +
    +

    Use case: Subset Sum

    The goal is to find a subset S of W (list of non-negative integers) whose elements sum is closest to (without exceeding) C. For example, for the input \(W=\{3, 34, 4, 12, 5, 2\}\) and \(C=9\), one output could be \(S=\{4, 5\}\) (as it is a subset with sum 9).

    In jMetalPy, this problem can be encoded as a binary problem with one objective (to be maximized) and one variable @@ -209,9 +254,9 @@

    Use case: Subset Sumreturn new_solution

    -
    -
    -

    Use case: Multi-objective Subset Sum

    + +
    +

    Use case: Multi-objective Subset Sum

    The former problem can be formulated as a multi-objective binary problem whose objectives are as follows:

    1. Maximize the sum of subsets to be as close as possible to \(C\) and

      2. @@ -220,21 +265,21 @@

      Use case: Multi-objective Subset Sum
      class SubsetSum(BinaryProblem):
 
-   def __init__(self, C: int, W: list):
-      super(SubsetSum, self).__init__(reference_front=None)
-      self.C = C
-      self.W = W
+   def __init__(self, C: int, W: list):
+      super(SubsetSum, self).__init__(reference_front=None)
+      self.C = C
+      self.W = W
 
-      self.number_of_bits = len(self.W)
+      self.number_of_bits = len(self.W)
 +     self.number_of_objectives = 2
-      self.number_of_variables = 1
-      self.number_of_constraints = 0
+      self.number_of_variables = 1
+      self.number_of_constraints = 0
 
 +     self.obj_directions = [self.MAXIMIZE, self.MINIMIZE]
 +     self.obj_labels = ['Sum', 'No. of Objects']
 
-   def evaluate(self, solution: BinarySolution) -> BinarySolution:
-      total_sum = 0.0
+   def evaluate(self, solution: BinarySolution) -> BinarySolution:
+      total_sum = 0.0
 +     number_of_objects = 0
 
 +     for index, bits in enumerate(solution.variables[0]):
@@ -242,195 +287,240 @@ 
      Use case: Multi-objective Subset Sum+           total_sum += self.W[index]
 +              number_of_objects += 1
 
-      if total_sum > self.C:
-         total_sum = self.C - total_sum * 0.1
+      if total_sum > self.C:
+         total_sum = self.C - total_sum * 0.1
 
-         if total_sum < 0.0:
-             total_sum = 0.0
+         if total_sum < 0.0:
+             total_sum = 0.0
 
-      solution.objectives[0] = -1.0 * total_sum
+      solution.objectives[0] = -1.0 * total_sum
 +     solution.objectives[1] = number_of_objects
 
-      return solution
+      return solution

    -
    -
    -

    API

    -
    -
    -class jmetal.core.problem.BinaryProblem[source]
    -

    Bases: jmetal.core.problem.Problem, abc.ABC

    + +
    +

    API

    +
    +
    +class jmetal.core.problem.BinaryProblem[source]
    +

    Bases: Problem[BinarySolution], ABC

    Class representing binary problems.

    +
    +
    +number_of_bits_per_variable_list()[source]
    +
    + +
    +
    +total_number_of_bits()[source]
    +
    +
    -
    -
    -class jmetal.core.problem.DynamicProblem[source]
    -

    Bases: jmetal.core.problem.Problem, jmetal.core.observer.Observer, abc.ABC

    -
    -
    -abstract clear_changed() → None[source]
    +
    +
    +class jmetal.core.problem.DynamicProblem[source]
    +

    Bases: Problem[S], Observer, ABC

    +
    +
    +abstract clear_changed() → None[source]
    -
    -
    -abstract the_problem_has_changed() → bool[source]
    +
    +
    +abstract the_problem_has_changed() → bool[source]
    -
    -
    -class jmetal.core.problem.FloatProblem[source]
    -

    Bases: jmetal.core.problem.Problem, abc.ABC

    +
    +
    +class jmetal.core.problem.FloatProblem[source]
    +

    Bases: Problem[FloatSolution], ABC

    Class representing float problems.

    -
    -
    -create_solution() → jmetal.core.solution.FloatSolution[source]
    +
    +
    +create_solution() → FloatSolution[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    +
    +
    +number_of_variables() → int[source]
    +
    +
    -
    -
    -class jmetal.core.problem.IntegerProblem[source]
    -

    Bases: jmetal.core.problem.Problem, abc.ABC

    +
    +
    +class jmetal.core.problem.IntegerProblem[source]
    +

    Bases: Problem[IntegerSolution], ABC

    Class representing integer problems.

    -
    -
    -create_solution() → jmetal.core.solution.IntegerSolution[source]
    +
    +
    +create_solution() → IntegerSolution[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    +
    +
    +number_of_variables() → int[source]
    +
    +
    -
    -
    -class jmetal.core.problem.OnTheFlyFloatProblem[source]
    -

    Bases: jmetal.core.problem.FloatProblem

    +
    +
    +class jmetal.core.problem.OnTheFlyFloatProblem[source]
    +

    Bases: FloatProblem

    Class for defining float problems on the fly.

    Example:

    >>> # Defining problem Srinivas on the fly
 >>> def f1(x: [float]):
 >>>     return 2.0 + (x[0] - 2.0) * (x[0] - 2.0) + (x[1] - 1.0) * (x[1] - 1.0)
->>>
+>>>
 >>> def f2(x: [float]):
 >>>     return 9.0 * x[0] - (x[1] - 1.0) * (x[1] - 1.0)
->>>
+>>>
 >>> def c1(x: [float]):
 >>>     return 1.0 - (x[0] * x[0] + x[1] * x[1]) / 225.0
->>>
+>>>
 >>> def c2(x: [float]):
 >>>     return (3.0 * x[1] - x[0]) / 10.0 - 1.0
->>>
+>>>
 >>> problem = OnTheFlyFloatProblem()            .set_name("Srinivas")            .add_variable(-20.0, 20.0)            .add_variable(-20.0, 20.0)            .add_function(f1)            .add_function(f2)            .add_constraint(c1)            .add_constraint(c2)
    -
    -
    -add_constraint(constraint)[source]
    +
    +
    +add_constraint(constraint) → OnTheFlyFloatProblem[source]
    -
    -
    -add_function(function)[source]
    +
    +
    +add_function(function) → OnTheFlyFloatProblem[source]
    -
    -
    -add_variable(lower_bound, upper_bound)[source]
    +
    +
    +add_variable(lower_bound, upper_bound) → OnTheFlyFloatProblem[source]
    -
    -
    -evaluate(solution: jmetal.core.solution.FloatSolution)[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +evaluate(solution: FloatSolution) → None[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -get_name() → str[source]
    +
    +
    +name() → str[source]
    -
    -
    -set_name(name)[source]
    +
    +
    +number_of_constraints() → int[source]
    +
    + +
    +
    +number_of_objectives() → int[source]
    +
    + +
    +
    +set_name(name) → OnTheFlyFloatProblem[source]
    -
    -
    -class jmetal.core.problem.PermutationProblem[source]
    -

    Bases: jmetal.core.problem.Problem, abc.ABC

    +
    +
    +class jmetal.core.problem.PermutationProblem[source]
    +

    Bases: Problem[PermutationSolution], ABC

    Class representing permutation problems.

    -
    -
    -class jmetal.core.problem.Problem[source]
    -

    Bases: typing.Generic, abc.ABC

    +
    +
    +class jmetal.core.problem.Problem[source]
    +

    Bases: Generic[S], ABC

    Class representing problems.

    -
    -
    -MAXIMIZE = 1
    +
    +
    +MAXIMIZE = 1
    -
    -
    -MINIMIZE = -1
    +
    +
    +MINIMIZE = -1
    -
    -
    -abstract create_solution() → S[source]
    +
    +
    +abstract create_solution() → S[source]

    Creates a random_search solution to the problem.

    -
    Returns
    +
    Returns:

    Solution.

    -
    -
    -abstract evaluate(solution: S) → S[source]
    -

    Evaluate a solution. For any new problem inheriting from Problem, this method should be -replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    +
    +
    +abstract evaluate(solution: S) → S[source]
    +

    Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. +Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

    -
    Returns
    +
    Returns:

    Evaluated solution.

    -
    -
    -abstract get_name() → str[source]
    +
    +
    +abstract name() → str[source]
    +
    + +
    +
    +abstract number_of_constraints() → int[source]
    +
    + +
    +
    +abstract number_of_objectives() → int[source]
    +
    + +
    +
    +abstract number_of_variables() → int[source]
    -
    -
    + +
    @@ -465,8 +555,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Defining new problems
    • - - - - - - - + Statistical analysis — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Statistical analysis
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -124,10 +122,10 @@

    Table Of Contents

    -
    -

    Statistical analysis

    +
    +

    Statistical analysis

    ToDo

    -
    +
    @@ -162,8 +160,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Statistical analysis
    • - - - - - - - + Front visualization — jMetalPy 1.7.0 documentation + + + + + + + @@ -47,8 +44,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Front visualization
    • @@ -59,7 +57,7 @@

    Navigation

    @@ -101,23 +99,59 @@

    Contents

    • Front visualization @@ -157,11 +191,11 @@

      Contents

      -
      -

      Front visualization

      +
      +

      Front visualization

      The jmetal.lab.visualization submodule contains several classes useful for plotting solutions. jMetalPy includes three types of visualization charts: static, interactive and streaming.

      -
      -

      Static plots

      +
      +

      Static plots

      +
      +

      Interactive plots

      • This kind of plots are interactive in such a way that every solution can be manipulated (e.g., actions such as zoom, selecting part of the graph, or clicking in a point to see its objective values are allowed).

        plot_front = InteractivePlot(title='Pareto front approximation')
@@ -278,49 +312,49 @@ 
        Interactive plots
-
        API
        
-
        
-
        
-class jmetal.lab.visualization.interactive.InteractivePlot(title: str = 'Pareto front approximation', reference_front: List[S] = None, reference_point: list = None, axis_labels: list = None)[source]
        
-
        Bases: jmetal.lab.visualization.plotting.Plot
        
-
        
-
        
-export_to_div(filename=None, include_plotlyjs: bool = False) → str[source]
        
+
        
+
        API
        
+
        
+
        
+class jmetal.lab.visualization.interactive.InteractivePlot(title: str = 'Pareto front approximation', reference_front: List[S] = None, reference_point: list = None, axis_labels: list = None)[source]
        
+
        Bases: Plot
        
+
        
+
        
+export_to_div(filename=None, include_plotlyjs: bool = False) → str[source]
        
 
        Export as a div for embedding the graph in an HTML file.
        
 
        
-
        Parameters
        
+
        Parameters:
        
 
          
 
        • filename – Output file name (if desired, default to None).
          • 
 
        • include_plotlyjs – If True, include plot.ly JS script (default to False).
          • 
 
        
 
        
-
        Returns
        
+
        Returns:
        
 
        Script as string.
        
 
        
 
        
 
        
 
-
        
-
        
-export_to_html(filename: str) → str[source]
        
+
        
+
        
+export_to_html(filename: str) → str[source]
        
 
        Export the graph to an interactive HTML (solutions can be selected to show some metadata).
        
 
        
-
        Parameters
        
+
        Parameters:
        
 
        filename – Output file name.
        
 
        
-
        Returns
        
+
        Returns:
        
 
        Script as string.
        
 
        
 
        
 
        
 
-
        
-
        
-plot(front, label=None, normalize: bool = False, filename: str = None, format: str = 'HTML')[source]
        
+
        
+
        
+plot(front, label=None, normalize: bool = False, filename: str = None, format: str = 'HTML')[source]
        
 
        Plot a front of solutions (2D, 3D or parallel coordinates).
        
 
        
-
        Parameters
        
+
        Parameters:
        
 
          
 
        • front – List of solutions.
          • 
 
        • label – Front name.
          • 
@@ -333,10 +367,10 @@ 
          API
        
 
-
        -
        -
      +
      +
      +

      Streaming plots

      • The visualizer observer displays the front in real-time (note it only works for problems with two and three objectives) during the execution of multi-objective algorithms; this can be useful to observe the evolution of the Pareto front approximation:

        from jmetal.util.observer import VisualizerObserver
@@ -346,69 +380,69 @@ 
        Streaming plots
-
        API
        
-
        
-
        
-class jmetal.lab.visualization.streaming.StreamingPlot(plot_title: str = 'Pareto front approximation', reference_front: List[S] = None, reference_point: list = None, axis_labels: list = None)[source]
        
-
        Bases: object
        
-
        
-
        
-create_layout(dimension: int) → None[source]
        
+
        
+
        API
        
+
        
+
        
+class jmetal.lab.visualization.streaming.StreamingPlot(plot_title: str = 'Pareto front approximation', reference_front: List[S] = None, reference_point: list = None, axis_labels: list = None)[source]
        
+
        Bases: object
        
+
        
+
        
+create_layout(dimension: int) → None[source]
        
 
        
 
-
        
-
        
-plot(front)[source]
        
+
        
+
        
+plot(front)[source]
        
 
        
 
-
        
-
        
-update(front: List[S], reference_point: list = None) → None[source]
        
+
        
+
        
+update(front: List[S], reference_point: list = None) → None[source]
        
 
        
 
 
        
 
-
        -
        -
        -

        Chord plot

        -
        -

        API

        -
        -
        -jmetal.lab.visualization.chord_plot.chord_diagram(solutions: List[jmetal.core.solution.FloatSolution], nbins='auto', ax=None, obj_labels=None, prop_labels={'fontsize': 13, 'ha': 'center', 'va': 'center'}, pad=6)[source]
        +
      + +
      +

      Chord plot

      +
      +

      API

      +
      +
      +jmetal.lab.visualization.chord_plot.chord_diagram(solutions: List[FloatSolution], nbins='auto', ax=None, obj_labels=None, prop_labels={'fontsize': 13, 'ha': 'center', 'va': 'center'}, pad=6)[source]
      -
      -
      -jmetal.lab.visualization.chord_plot.draw_chord(start_angle1=0, end_angle1=60, start_angle2=180, end_angle2=240, radius=1.0, chord_width=0.7, ax=None, color=(1, 0, 0), z_order=1)[source]
      +
      +
      +jmetal.lab.visualization.chord_plot.draw_chord(start_angle1=0, end_angle1=60, start_angle2=180, end_angle2=240, radius=1.0, chord_width=0.7, ax=None, color=(1, 0, 0), z_order=1)[source]
      -
      -
      -jmetal.lab.visualization.chord_plot.draw_sector(start_angle=0, end_angle=60, radius=1.0, width=0.2, lw=2, ls='-', ax=None, fc=(1, 0, 0), ec=(0, 0, 0), z_order=1)[source]
      +
      +
      +jmetal.lab.visualization.chord_plot.draw_sector(start_angle=0, end_angle=60, radius=1.0, width=0.2, lw=2, ls='-', ax=None, fc=(1, 0, 0), ec=(0, 0, 0), z_order=1)[source]
      -
      -
      -jmetal.lab.visualization.chord_plot.hover_over_bin(event, handle_tickers, handle_plots, colors, fig)[source]
      +
      +
      +jmetal.lab.visualization.chord_plot.hover_over_bin(event, handle_tickers, handle_plots, colors, fig)[source]
      -
      -
      -jmetal.lab.visualization.chord_plot.polar_to_cartesian(r, theta)[source]
      +
      +
      +jmetal.lab.visualization.chord_plot.polar_to_cartesian(r, theta)[source]
      -
      -
      -
      -

      Posterior plot

      -
      -

      API

      -
      -
      -jmetal.lab.visualization.posterior.plot_posterior(sample, higher_is_better: bool = False, min_points_per_hexbin: int = 2, alg_names: list = None, filename: str = 'posterior.eps')[source]
      + + +
      +

      Posterior plot

      +
      +

      API

      +
      +
      +jmetal.lab.visualization.posterior.plot_posterior(sample, higher_is_better: bool = False, min_points_per_hexbin: int = 2, alg_names: list = None, filename: str = 'posterior.eps')[source]

      Plots the sample from posterior distribution of a Bayesian statistical test. Parameters: ———– @@ -417,12 +451,15 @@

      API

      Names of the algorithms under evaluation

      +
      +

      Return:

      Figure

      +

      -
      -
      -
    + + +
    @@ -457,8 +494,9 @@

    Navigation

  • previous |
    • -
  • jMetalPy 1.5.3 documentation »
    • -
  • Getting started »
    • +
  • jMetalPy 1.7.0 documentation »
    • +
  • Getting started »
    • +
  • Front visualization
  • modules |