diff --git a/examples/NSGAIIAsAnEvolutionaryAlgorithm.jl b/examples/NSGAIIAsAnEvolutionaryAlgorithm.jl
index 10958af..1955e81 100644
--- a/examples/NSGAIIAsAnEvolutionaryAlgorithm.jl
+++ b/examples/NSGAIIAsAnEvolutionaryAlgorithm.jl
@@ -3,7 +3,7 @@ using Dates
 
 # NSGA-II algorithm configured from the evolutionary algorithm template
 function main()
-    problem = ZDT1()
+    problem = ZDT4()
 
     solver::EvolutionaryAlgorithm = EvolutionaryAlgorithm()
     solver.name = "NSGA-II"
@@ -12,24 +12,19 @@ function main()
     offspringPopulationSize = 100
 
     solver.solutionsCreation = DefaultSolutionsCreation(problem, populationSize)
-
     solver.evaluation = SequentialEvaluation(problem)
-
     solver.termination = TerminationByEvaluations(25000)
 
     mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
+    crossover = SBXCrossover(0.9, 20.0, problem.bounds)
 
     """
     mutation = UniformMutation(1.0/numberOfVariables(problem), 20.0, problem.bounds)
-
     crossover = BLXAlphaCrossover(1.0, 0.5, problem.bounds)
     """
-    crossover = SBXCrossover(0.9, 20.0, problem.bounds)
 
     solver.variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
-
     solver.selection = BinaryTournamentSelection(solver.variation.matingPoolSize, DefaultDominanceComparator())
-
     solver.replacement = RankingAndDensityEstimatorReplacement(DominanceRanking(DefaultDominanceComparator()), CrowdingDistanceDensityEstimator())
 
     startingTime = Dates.now()
diff --git a/examples/SMPSOAsPSO.jl b/examples/SMPSOAsPSO.jl
index 8656d72..db8c063 100644
--- a/examples/SMPSOAsPSO.jl
+++ b/examples/SMPSOAsPSO.jl
@@ -7,7 +7,7 @@ function main()
     solver = ParticleSwarmOptimization()
     solver.name = "SMPSO"
 
-    problem = ZDT4()
+    problem = kursawe()
     swarmSize = 100
 
     solver.solutionsCreation = DefaultSolutionsCreation(problem, swarmSize)
@@ -24,7 +24,8 @@ function main()
 
     solver.inertiaWeightComputingStrategy = ConstantValueStrategy(0.1)
     
-    mutationOperarator = PolynomialMutation(1.0/numberOfVariables(problem), 2.0, bounds(problem))
+    mutationOperarator = PolynomialMutation(1.0/numberOfVariables(problem), 20.0, bounds(problem))
+    
     mutationFrequency = 6
     solver.perturbation = FrequencySelectionMutationBasedPerturbation(mutationFrequency, mutationOperarator)    
 
diff --git a/src/operator/mutation.jl b/src/operator/mutation.jl
index 9961f69..2c5f319 100644
--- a/src/operator/mutation.jl
+++ b/src/operator/mutation.jl
@@ -58,7 +58,6 @@ struct PolynomialMutation <: MutationOperator
   variableBounds::Vector{Bounds{Float64}}
 end
 
-
 function polynomialMutation(x::Vector{T}, mutationOperator::PolynomialMutation)::Vector{T} where {T<:Real}
   probability::Real = mutationOperator.probability
   distributionIndex::Real = mutationOperator.distributionIndex
diff --git a/src/util/comparator.jl b/src/util/comparator.jl
index 8d3f0b1..deb042b 100644
--- a/src/util/comparator.jl
+++ b/src/util/comparator.jl
@@ -46,6 +46,14 @@ end
 function compare(comparator::DefaultDominanceComparator, x::Vector{T}, y::Vector{T})::Int where {T<:Number}
   @assert length(x) == length(y) "The vectors have a different length"
 
+  """
+  x==y && return 0
+
+  all(t->(t[1]≤t[2]), zip(x, y)) && return -1
+  all(t->(t[1]≤t[2]), zip(y, x)) && return  1
+  return 0
+  """
+  
   bestIsSolution1 = 0
   bestIsSolution2 = 0