Add GA with local improvment

Author: ottoblep

include/simulation/RoutingSolver.hpp

@@ -73,11 +73,17 @@ class RoutingSolver {
                double             start_sampling_range_fraction,
                double abort_sampling_range_fraction, double contr_coeff);
 
+  std::tuple<SolverResult, ScoreHistory>
+  local_search(RoutingSolutionSet starting_solution,
+               double             start_sampling_range_fraction,
+               double abort_sampling_range_fraction, double contr_coeff,
+               const std::function<double(void)>& rng01);
+
   std::optional<SolverResult>
   greedy_solution(std::chrono::milliseconds per_train_stall_time);
 
   std::tuple<std::optional<SolverResult>, ScoreHistory>
-  genetic_search(GeneticParams params);
+  genetic_search(GeneticParams params, bool local_improv = false);
 
   // GA Helpers
   struct MiddleCost {
@@ -100,6 +106,11 @@ class RoutingSolver {
                                const RoutingSolutionSet&          X2,
                                const std::function<double(void)>& rnd01);
 
+  RoutingSolutionSet
+  crossover_local_improv(const RoutingSolutionSet&          X1,
+                         const RoutingSolutionSet&          X2,
+                         const std::function<double(void)>& rnd01);
+
   double calculate_SO_total_fitness(const GA_Type::thisChromosomeType& X);
 
   void SO_report_generation(

src/simulation/RoutingSolver.cpp

@@ -263,6 +263,41 @@ cda_rail::sim::RoutingSolver::local_search(
   return {last_result, hist};
 }
 
+std::tuple<cda_rail::sim::SolverResult, cda_rail::sim::ScoreHistory>
+cda_rail::sim::RoutingSolver::local_search(
+    cda_rail::sim::RoutingSolutionSet starting_solution,
+    double start_sampling_range_fraction, double abort_sampling_range_fraction,
+    double contr_coeff, const std::function<double(void)>& rng01) {
+  std::chrono::steady_clock::time_point initial_time =
+      std::chrono::steady_clock::now();
+
+  ScoreHistory hist;
+  double       sampling_range_fraction = start_sampling_range_fraction;
+  SolverResult last_result{instance, starting_solution};
+  double       last_score = last_result.get_score();
+  hist.push_back({std::chrono::duration_cast<std::chrono::milliseconds>(
+                      std::chrono::steady_clock::now() - initial_time),
+                  last_score});
+
+  while (sampling_range_fraction > abort_sampling_range_fraction) {
+    RoutingSolutionSet new_sol = last_result.get_solutions();
+    new_sol.perturb(instance, sampling_range_fraction, rng01);
+    SolverResult new_result{instance, new_sol};
+
+    if (double new_score = new_result.get_score(); new_score < last_score) {
+      last_result = new_result;
+      last_score  = new_score;
+      hist.push_back({std::chrono::duration_cast<std::chrono::milliseconds>(
+                          std::chrono::steady_clock::now() - initial_time),
+                      last_score});
+    } else {
+      sampling_range_fraction = sampling_range_fraction * contr_coeff;
+    }
+  }
+
+  return {last_result, hist};
+}
+
 std::optional<cda_rail::sim::SolverResult>
 cda_rail::sim::RoutingSolver::greedy_solution(
     std::chrono::milliseconds per_train_stall_time) {
@@ -320,7 +355,8 @@ cda_rail::sim::RoutingSolver::greedy_solution(
 
 std::tuple<std::optional<cda_rail::sim::SolverResult>,
            cda_rail::sim::ScoreHistory>
-cda_rail::sim::RoutingSolver::genetic_search(GeneticParams params) {
+cda_rail::sim::RoutingSolver::genetic_search(GeneticParams params,
+                                             bool          local_improv) {
   /**
    * Genetic algorithm for entire solution sets
    */
@@ -342,9 +378,16 @@ cda_rail::sim::RoutingSolver::genetic_search(GeneticParams params) {
                 std::placeholders::_2);
   ga_obj.mutate = std::bind(&RoutingSolver::mutate, this, std::placeholders::_1,
                             std::placeholders::_2, std::placeholders::_3);
-  ga_obj.crossover =
-      std::bind(&RoutingSolver::crossover, this, std::placeholders::_1,
-                std::placeholders::_2, std::placeholders::_3);
+
+  if (local_improv) {
+    ga_obj.crossover = std::bind(&RoutingSolver::crossover_local_improv, this,
+                                 std::placeholders::_1, std::placeholders::_2,
+                                 std::placeholders::_3);
+  } else {
+    ga_obj.crossover =
+        std::bind(&RoutingSolver::crossover, this, std::placeholders::_1,
+                  std::placeholders::_2, std::placeholders::_3);
+  }
 
   std::chrono::steady_clock::time_point starting_time =
       std::chrono::steady_clock::now();
@@ -406,6 +449,16 @@ cda_rail::sim::RoutingSolutionSet cda_rail::sim::RoutingSolver::crossover(
   return X_new;
 }
 
+cda_rail::sim::RoutingSolutionSet
+cda_rail::sim::RoutingSolver::crossover_local_improv(
+    const RoutingSolutionSet& X1, const RoutingSolutionSet& X2,
+    const std::function<double(void)>& rnd01) {
+  auto X_new = crossover(X1, X2, rnd01);
+  auto res   = local_search(X_new, 0.05, 0.01, 0.95, rnd01);
+
+  return std::get<0>(res).get_solutions();
+}
+
 double cda_rail::sim::RoutingSolver::calculate_SO_total_fitness(
     const GA_Type::thisChromosomeType& X) {
   return X.middle_costs.score;

test/test_simulation.cpp

@@ -331,7 +331,7 @@ TEST(Simulation, LocalSearch) {
   sim::RoutingSolver      solver{instance};
 
   sim::RoutingSolutionSet solution_set{instance, rng_engine};
-  auto res = solver.local_search(solution_set, 0.5, 1e-5, 0.95);
+  auto res = solver.local_search(solution_set, 0.1, 0.01, 0.95);
 }
 
 TEST(Simulation, RandomLocalSearch) {
@@ -378,11 +378,11 @@ TEST(Simulation, GeneticSearch) {
       "./example-networks-unidirec/SimpleNetwork/timetable/", network);
 
   cda_rail::sim::GeneticParams ga_params{
-      .is_multithread = false,
-      .population     = 1000,
+      .is_multithread = true,
+      .population     = 100,
       .gen_max        = 20,
       .stall_max      = 5,
-      .n_elite        = 100,
+      .n_elite        = 10,
       .xover_frac     = 0.7,
       .mut_rate       = 0.1,
   };
@@ -391,7 +391,8 @@ TEST(Simulation, GeneticSearch) {
   sim::RoutingSolver      solver{instance};
 
   sim::RoutingSolutionSet solution_set{instance, rng_engine};
-  auto                    res = solver.genetic_search(ga_params);
+  auto                    res  = solver.genetic_search(ga_params);
+  auto                    res2 = solver.genetic_search(ga_params, true);
 }
 
 // TODO: test for invariance of solution after being repaired and used again