public override SolutionSet Execute() { QualityIndicator.QualityIndicator indicators = null; // QualityIndicator object int requiredEvaluations = 0; // Use in the example of use of the // indicators object (see below) evaluations = 0; iteration = 0; JMetalCSharp.Utils.Utils.GetDoubleValueFromParameter(this.InputParameters, "q", ref q); JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize); JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "iterationsNumber", ref iterationsNumber); JMetalCSharp.Utils.Utils.GetStringValueFromParameter(this.InputParameters, "dataDirectory", ref dataDirectory); JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators); Logger.Log.Info("POPSIZE: " + populationSize); Console.WriteLine("POPSIZE: " + populationSize); population = new SolutionSet(populationSize); indArray = new Solution[Problem.NumberOfObjectives]; JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "T", ref t); JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "nr", ref nr); JMetalCSharp.Utils.Utils.GetDoubleValueFromParameter(this.InputParameters, "delta", ref delta); neighborhood = new int[populationSize][]; for (int i = 0; i < populationSize; i++) { neighborhood[i] = new int[t]; } z = new double[Problem.NumberOfObjectives]; //znad = new double[Problem.NumberOfObjectives]; lambda = new double[populationSize][]; for (int i = 0; i < populationSize; i++) { lambda[i] = new double[Problem.NumberOfObjectives]; } crossover = this.Operators["crossover"]; mutation = this.Operators["mutation"]; double[] pro_T = new double[t]; double[] pro_A = new double[populationSize]; pro_T = GerPro(t); pro_A = GerPro(populationSize); /*string dir = "Result/MOEAD_ACOR/ZDT4_Real/Record/Replace/3nd_Dynamic2_nr16"; * if (Directory.Exists(dir)) * { * Console.WriteLine("The directory {0} already exists.", dir); * } * else * { * Directory.CreateDirectory(dir); * Console.WriteLine("The directory {0} was created.", dir); * }*/ //Step 1. Initialization //Step 1.1 Compute euclidean distances between weight vectors and find T InitUniformWeight(); InitNeighborhood(); //Step 1.2 Initialize population InitPoputalion(); //Step 1.3 Initizlize z InitIdealPoint(); //Step 2 Update do { int[] permutation = new int[populationSize]; Utils.RandomPermutation(permutation, populationSize); if (crossover.ToString() == "JMetalCSharp.Operators.Crossover.DifferentialEvolutionCrossover") { for (int i = 0; i < populationSize; i++) { int n = permutation[i]; // or int n = i; int type; double rnd = JMetalRandom.NextDouble(); // STEP 2.1. Mating selection based on probability if (rnd < delta) // if (rnd < realb) { type = 1; // neighborhood } else { type = 2; // whole population } List <int> p = new List <int>(); MatingSelection(p, n, 2, type); // STEP 2.2. Reproduction Solution child; Solution[] parents = new Solution[3]; parents[0] = population.Get(p[0]); parents[1] = population.Get(p[1]); parents[2] = population.Get(n); // Apply DE crossover child = (Solution)crossover.Execute(new object[] { population.Get(n), parents }); // Apply mutation mutation.Execute(child); // Evaluation Problem.Evaluate(child); evaluations++; // STEP 2.3. Repair. Not necessary // STEP 2.4. Update z_ UpdateReference(child); // STEP 2.5. Update of solutions UpdateProblem(child, n, type); } } else if (crossover.ToString() == "JMetalCSharp.Operators.Crossover.ACOR") { Solution[] parents = new Solution[2]; int t = 0; for (int i = 0; i < populationSize; i++) { int n = permutation[i]; // or int n = i; int type; double rnd = JMetalRandom.NextDouble(); // STEP 2.1. ACOR selection based on probability if (rnd < delta) // if (rnd < realb) { type = 1; // minmum //parents[0] = population.Get(ACOrSelection2(n, type, pro_T)); } else { type = 2; // whole neighborhood probability //parents[0] = population.Get(ACOrSelection2(n, type, pro_A)); } GetStdDev(neighborhood); //GetStdDev1(neighborhood, type); //List<int> p = new List<int>(); //MatingSelection(p, n, 1, type); // STEP 2.2. Reproduction Solution child; parents[0] = population.Get(ACOrSelection(n, type)); parents[1] = population.Get(n); //parents[0] = population.Get(p[0]); // Apply ACOR crossover child = (Solution)crossover.Execute(parents); child.NumberofReplace = t; // // Apply mutation mutation.Execute(child); // Evaluation Problem.Evaluate(child); evaluations++; // STEP 2.3. Repair. Not necessary // STEP 2.4. Update z_ UpdateReference(child); // STEP 2.5. Update of solutions t = UpdateProblemWithReplace(child, n, 1); } } else { // Create the offSpring solutionSet SolutionSet offspringPopulation = new SolutionSet(populationSize); Solution[] parents = new Solution[2]; for (int i = 0; i < (populationSize / 2); i++) { int n = permutation[i]; // or int n = i; int type; double rnd = JMetalRandom.NextDouble(); // STEP 2.1. Mating selection based on probability if (rnd < delta) // if (rnd < realb) { type = 1; // neighborhood } else { type = 2; // whole population } List <int> p = new List <int>(); MatingSelection(p, n, 2, type); parents[0] = population.Get(p[0]); parents[1] = population.Get(p[1]); if (iteration < iterationsNumber) { //obtain parents Solution[] offSpring = (Solution[])crossover.Execute(parents); //Solution child; mutation.Execute(offSpring[0]); mutation.Execute(offSpring[1]); /*if(rnd < 0.5) * { * child = offSpring[0]; * } * else * { * child = offSpring[1]; * }*/ Problem.Evaluate(offSpring[0]); Problem.Evaluate(offSpring[1]); Problem.EvaluateConstraints(offSpring[0]); Problem.EvaluateConstraints(offSpring[1]); offspringPopulation.Add(offSpring[0]); offspringPopulation.Add(offSpring[1]); evaluations += 2; // STEP 2.3. Repair. Not necessary // STEP 2.4. Update z_ UpdateReference(offSpring[0]); UpdateReference(offSpring[1]); // STEP 2.5. Update of solutions UpdateProblem(offSpring[0], n, type); UpdateProblem(offSpring[1], n, type); } } } /*string filevar = dir + "/VAR" + iteration; * string filefun = dir + "/FUN" + iteration; * population.PrintVariablesToFile(filevar); * population.PrintObjectivesToFile(filefun);*/ iteration++; if ((indicators != null) && (requiredEvaluations == 0)) { double HV = indicators.GetHypervolume(population); if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume)) { requiredEvaluations = evaluations; } } } while (iteration < iterationsNumber); Logger.Log.Info("ITERATION: " + iteration); Console.WriteLine("ITERATION: " + iteration); // Return as output parameter the required evaluations SetOutputParameter("evaluations", requiredEvaluations); Result = population; //return population; // Return the first non-dominated front Ranking rank = new Ranking(population); //Result = rank.GetSubfront(0); return(Result); }
/// <summary> /// Runs the SMS-EMOA algorithm. /// </summary> /// <returns>A <code>SolutionSet</code> that is a set of non dominated solutions as a result of the algorithm execution</returns> public override SolutionSet Execute() { int populationSize = -1; int maxEvaluations = -1; int evaluations; double offset = 100.0; QualityIndicator.QualityIndicator indicators = null; // QualityIndicator object int requiredEvaluations; // Use in the example of use of the indicators object (see below) SolutionSet population; SolutionSet offspringPopulation; SolutionSet union; Operator mutationOperator; Operator crossoverOperator; Operator selectionOperator; //Read the parameters JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize); JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations); JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators); JMetalCSharp.Utils.Utils.GetDoubleValueFromParameter(this.InputParameters, "offset", ref offset); //Initialize the variables population = new SolutionSet(populationSize); evaluations = 0; requiredEvaluations = 0; //Read the operators mutationOperator = Operators["mutation"]; crossoverOperator = Operators["crossover"]; selectionOperator = Operators["selection"]; // Create the initial solutionSet Solution newSolution; for (int i = 0; i < populationSize; i++) { newSolution = new Solution(Problem); Problem.Evaluate(newSolution); Problem.EvaluateConstraints(newSolution); evaluations++; population.Add(newSolution); } // Generations ... while (evaluations < maxEvaluations) { // select parents offspringPopulation = new SolutionSet(populationSize); List <Solution> selectedParents = new List <Solution>(); Solution[] parents = new Solution[0]; while (selectedParents.Count < 2) { object selected = selectionOperator.Execute(population); try { Solution parent = (Solution)selected; selectedParents.Add(parent); } catch (InvalidCastException e) { parents = (Solution[])selected; selectedParents.AddRange(parents); } } parents = selectedParents.ToArray <Solution>(); // crossover Solution[] offSpring = (Solution[])crossoverOperator.Execute(parents); // mutation mutationOperator.Execute(offSpring[0]); // evaluation Problem.Evaluate(offSpring[0]); Problem.EvaluateConstraints(offSpring[0]); // insert child into the offspring population offspringPopulation.Add(offSpring[0]); evaluations++; // Create the solutionSet union of solutionSet and offSpring union = ((SolutionSet)population).Union(offspringPopulation); // Ranking the union (non-dominated sorting) Ranking ranking = new Ranking(union); // ensure crowding distance values are up to date // (may be important for parent selection) for (int j = 0; j < population.Size(); j++) { population.Get(j).CrowdingDistance = 0.0; } SolutionSet lastFront = ranking.GetSubfront(ranking.GetNumberOfSubfronts() - 1); if (lastFront.Size() > 1) { double[][] frontValues = lastFront.WriteObjectivesToMatrix(); int numberOfObjectives = Problem.NumberOfObjectives; // STEP 1. Obtain the maximum and minimum values of the Pareto front double[] maximumValues = utils.GetMaximumValues(union.WriteObjectivesToMatrix(), numberOfObjectives); double[] minimumValues = utils.GetMinimumValues(union.WriteObjectivesToMatrix(), numberOfObjectives); // STEP 2. Get the normalized front double[][] normalizedFront = utils.GetNormalizedFront(frontValues, maximumValues, minimumValues); // compute offsets for reference point in normalized space double[] offsets = new double[maximumValues.Length]; for (int i = 0; i < maximumValues.Length; i++) { offsets[i] = offset / (maximumValues[i] - minimumValues[i]); } // STEP 3. Inverse the pareto front. This is needed because the original //metric by Zitzler is for maximization problems double[][] invertedFront = utils.InvertedFront(normalizedFront); // shift away from origin, so that boundary points also get a contribution > 0 for (int j = 0, lj = invertedFront.Length; j < lj; j++) { var point = invertedFront[j]; for (int i = 0; i < point.Length; i++) { point[i] += offsets[i]; } } // calculate contributions and sort double[] contributions = HvContributions(invertedFront); for (int i = 0; i < contributions.Length; i++) { // contribution values are used analogously to crowding distance lastFront.Get(i).CrowdingDistance = contributions[i]; } lastFront.Sort(new CrowdingDistanceComparator()); } // all but the worst are carried over to the survivor population SolutionSet front = null; population.Clear(); for (int i = 0; i < ranking.GetNumberOfSubfronts() - 1; i++) { front = ranking.GetSubfront(i); for (int j = 0; j < front.Size(); j++) { population.Add(front.Get(j)); } } for (int i = 0; i < lastFront.Size() - 1; i++) { population.Add(lastFront.Get(i)); } // This piece of code shows how to use the indicator object into the code // of SMS-EMOA. In particular, it finds the number of evaluations required // by the algorithm to obtain a Pareto front with a hypervolume higher // than the hypervolume of the true Pareto front. if (indicators != null && requiredEvaluations == 0) { double HV = indicators.GetHypervolume(population); if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume)) { requiredEvaluations = evaluations; } } } // Return as output parameter the required evaluations SetOutputParameter("evaluations", requiredEvaluations); // Return the first non-dominated front Ranking rnk = new Ranking(population); Result = rnk.GetSubfront(0); return(Result); }
/// <summary> /// Runs the FastSMSEMOA algorithm. /// </summary> /// <returns> a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the algorithm execution</returns> public override SolutionSet Execute() { int populationSize = -1; int maxEvaluations = -1; int evaluations; double offset = -1; QualityIndicator.QualityIndicator indicators = null; // QualityIndicator object int requiredEvaluations; // Use in the example of use of the indicators object (see below) FastHypervolume fastHypervolume; SolutionSet population; SolutionSet offspringPopulation; SolutionSet union; Operator mutationOperator; Operator crossoverOperator; Operator selectionOperator; //Read the parameters JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize); JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations); JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators); JMetalCSharp.Utils.Utils.GetDoubleValueFromParameter(this.InputParameters, "offset", ref offset); //Initialize the variables population = new SolutionSet(populationSize); evaluations = 0; requiredEvaluations = 0; fastHypervolume = new FastHypervolume(offset); //Read the operators mutationOperator = Operators["mutation"]; crossoverOperator = Operators["crossover"]; selectionOperator = Operators["selection"]; // Create the initial solutionSet Solution newSolution; for (int i = 0; i < populationSize; i++) { newSolution = new Solution(Problem); Problem.Evaluate(newSolution); Problem.EvaluateConstraints(newSolution); evaluations++; population.Add(newSolution); } // Generations ... while (evaluations < maxEvaluations) { // select parents offspringPopulation = new SolutionSet(populationSize); List <Solution> selectedParents = new List <Solution>(); Solution[] parents = new Solution[0]; while (selectedParents.Count < 2) { object selected = selectionOperator.Execute(population); try { Solution parent = (Solution)selected; selectedParents.Add(parent); } catch (InvalidCastException e) { parents = (Solution[])selected; selectedParents.AddRange(parents); } } parents = selectedParents.ToArray <Solution>(); // crossover Solution[] offSpring = (Solution[])crossoverOperator.Execute(parents); // mutation mutationOperator.Execute(offSpring[0]); // evaluation Problem.Evaluate(offSpring[0]); Problem.EvaluateConstraints(offSpring[0]); // insert child into the offspring population offspringPopulation.Add(offSpring[0]); evaluations++; // Create the solutionSet union of solutionSet and offSpring union = ((SolutionSet)population).Union(offspringPopulation); // Ranking the union (non-dominated sorting) Ranking ranking = new Ranking(union); // ensure crowding distance values are up to date // (may be important for parent selection) for (int j = 0; j < population.Size(); j++) { population.Get(j).CrowdingDistance = 0.0; } SolutionSet lastFront = ranking.GetSubfront(ranking.GetNumberOfSubfronts() - 1); fastHypervolume.ComputeHVContributions(lastFront); lastFront.Sort(new CrowdingDistanceComparator()); // all but the worst are carried over to the survivor population SolutionSet front = null; population.Clear(); for (int i = 0; i < ranking.GetNumberOfSubfronts() - 1; i++) { front = ranking.GetSubfront(i); for (int j = 0; j < front.Size(); j++) { population.Add(front.Get(j)); } } for (int i = 0; i < lastFront.Size() - 1; i++) { population.Add(lastFront.Get(i)); } // This piece of code shows how to use the indicator object into the code // of SMS-EMOA. In particular, it finds the number of evaluations required // by the algorithm to obtain a Pareto front with a hypervolume higher // than the hypervolume of the true Pareto front. if (indicators != null && requiredEvaluations == 0) { double HV = indicators.GetHypervolume(population); if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume)) { requiredEvaluations = evaluations; } } } // Return as output parameter the required evaluations SetOutputParameter("evaluations", requiredEvaluations); // Return the first non-dominated front Ranking rnk = new Ranking(population); Result = rnk.GetSubfront(0); return(Result); }
/// <summary> /// Runs the NSGA-II algorithm. /// </summary> /// <returns>a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the algorithm execution</returns> public override SolutionSet Execute() { int populationSize = -1; int maxEvaluations = -1; int evaluations; QualityIndicator.QualityIndicator indicators = null; // QualityIndicator object int requiredEvaluations; // Use in the example of use of the // indicators object (see below) SolutionSet population; SolutionSet offspringPopulation; SolutionSet union; Operator mutationOperator; Operator crossoverOperator; Operator selectionOperator; Distance distance = new Distance(); //Read the parameters JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations); JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize); JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators); //Initialize the variables population = new SolutionSet(populationSize); evaluations = 0; requiredEvaluations = 0; //Read the operators mutationOperator = Operators["mutation"]; crossoverOperator = Operators["crossover"]; selectionOperator = Operators["selection"]; Random random = new Random(2); JMetalRandom.SetRandom(random); // Create the initial solutionSet Solution newSolution; for (int i = 0; i < populationSize; i++) { newSolution = new Solution(Problem); Problem.Evaluate(newSolution); Problem.EvaluateConstraints(newSolution); evaluations++; population.Add(newSolution); } // Generations while (evaluations < maxEvaluations) { // Create the offSpring solutionSet offspringPopulation = new SolutionSet(populationSize); Solution[] parents = new Solution[2]; for (int i = 0; i < (populationSize / 2); i++) { if (evaluations < maxEvaluations) { //obtain parents parents[0] = (Solution)selectionOperator.Execute(population); parents[1] = (Solution)selectionOperator.Execute(population); Solution[] offSpring = (Solution[])crossoverOperator.Execute(parents); mutationOperator.Execute(offSpring[0]); mutationOperator.Execute(offSpring[1]); Problem.Evaluate(offSpring[0]); Problem.EvaluateConstraints(offSpring[0]); Problem.Evaluate(offSpring[1]); Problem.EvaluateConstraints(offSpring[1]); offspringPopulation.Add(offSpring[0]); offspringPopulation.Add(offSpring[1]); evaluations += 2; } } // Create the solutionSet union of solutionSet and offSpring union = ((SolutionSet)population).Union(offspringPopulation); var list = union.SolutionsList; var unique = list.Distinct(new ComparerP()).ToList(); union = new SolutionSet(unique.Count); union.SolutionsList.AddRange(unique); //ConfigurationSolutionType.AllSolutions = new object (); // Ranking the union Ranking ranking = new Ranking(union); int remain = populationSize; int index = 0; SolutionSet front = null; population.Clear(); // Obtain the next front front = ranking.GetSubfront(index); while ((remain > 0) && (remain >= front.Size())) { //Assign crowding distance to individuals distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives); //Add the individuals of this front for (int k = 0; k < front.Size(); k++) { population.Add(front.Get(k)); } //Decrement remain remain = remain - front.Size(); //Obtain the next front index++; if (remain > 0) { front = ranking.GetSubfront(index); } } // Remain is less than front(index).size, insert only the best one if (remain > 0) { // front contains individuals to insert distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives); front.Sort(new CrowdingComparator()); for (int k = 0; k < remain; k++) { population.Add(front.Get(k)); } remain = 0; } // This piece of code shows how to use the indicator object into the code // of NSGA-II. In particular, it finds the number of evaluations required // by the algorithm to obtain a Pareto front with a hypervolume higher // than the hypervolume of the true Pareto front. if ((indicators != null) && (requiredEvaluations == 0)) { double HV = indicators.GetHypervolume(population); if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume)) { requiredEvaluations = evaluations; } } } // Return as output parameter the required evaluations SetOutputParameter("evaluations", requiredEvaluations); // Return the first non-dominated front Ranking rank = new Ranking(population); Result = rank.GetSubfront(0); return(population); }
/// <summary> /// Runs the NSGA-II algorithm. /// </summary> /// <returns>a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the algorithm execution</returns> public override SolutionSet Execute() { int populationSize = -1; int maxEvaluations = -1; int iterationsNumber = -1; int evaluations; int iteration; QualityIndicator.QualityIndicator indicators = null; // QualityIndicator object int requiredEvaluations; // Use in the example of use of the // indicators object (see below) SolutionSet population; SolutionSet offspringPopulation; SolutionSet union; Operator mutationOperator; Operator crossoverOperator; Operator selectionOperator; Distance distance = new Distance(); //Read the parameters JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations); JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize); JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "iterationsNumber", ref iterationsNumber); JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators); //Initialize the variables population = new SolutionSet(populationSize); evaluations = 0; iteration = 0; requiredEvaluations = 0; //Read the operators mutationOperator = Operators["mutation"]; crossoverOperator = Operators["crossover"]; selectionOperator = Operators["selection"]; Random random = new Random(); JMetalRandom.SetRandom(random); // Create the initial solutionSet Solution newSolution; for (int i = 0; i < populationSize; i++) { newSolution = new Solution(Problem); Problem.Evaluate(newSolution); Problem.EvaluateConstraints(newSolution); evaluations++; population.Add(newSolution); } /* * string dir = "Result/MOEAD_ACOR/ZDT4_Real/Record/NSGAII_SBX_nr16"; * if (Directory.Exists(dir)) * { * Console.WriteLine("The directory {0} already exists.", dir); * } * else * { * Directory.CreateDirectory(dir); * Console.WriteLine("The directory {0} was created.", dir); * }*/ // Generations while (iteration < iterationsNumber) { // Create the offSpring solutionSet offspringPopulation = new SolutionSet(populationSize); Solution[] parents = new Solution[2]; for (int i = 0; i < (populationSize / 2); i++) { if (iteration < iterationsNumber) { //obtain parents parents[0] = (Solution)selectionOperator.Execute(population); parents[1] = (Solution)selectionOperator.Execute(population); Solution[] offSpring = (Solution[])crossoverOperator.Execute(parents); mutationOperator.Execute(offSpring[0]); mutationOperator.Execute(offSpring[1]); Problem.Evaluate(offSpring[0]); Problem.EvaluateConstraints(offSpring[0]); Problem.Evaluate(offSpring[1]); Problem.EvaluateConstraints(offSpring[1]); offspringPopulation.Add(offSpring[0]); offspringPopulation.Add(offSpring[1]); evaluations += 2; } } // Create the solutionSet union of solutionSet and offSpring union = ((SolutionSet)population).Union(offspringPopulation); // Ranking the union Ranking ranking = new Ranking(union); int remain = populationSize; int index = 0; SolutionSet front = null; population.Clear(); // Obtain the next front front = ranking.GetSubfront(index); while ((remain > 0) && (remain >= front.Size())) { //Assign crowding distance to individuals distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives); //Add the individuals of this front for (int k = 0; k < front.Size(); k++) { population.Add(front.Get(k)); } //Decrement remain remain = remain - front.Size(); //Obtain the next front index++; if (remain > 0) { front = ranking.GetSubfront(index); } } // Remain is less than front(index).size, insert only the best one if (remain > 0) { // front contains individuals to insert distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives); front.Sort(new CrowdingComparator()); for (int k = 0; k < remain; k++) { population.Add(front.Get(k)); } remain = 0; } /* * string filevar = dir + "/VAR" + iteration; * string filefun = dir + "/FUN" + iteration; * population.PrintVariablesToFile(filevar); * population.PrintObjectivesToFile(filefun);*/ iteration++; // This piece of code shows how to use the indicator object into the code // of NSGA-II. In particular, it finds the number of evaluations required // by the algorithm to obtain a Pareto front with a hypervolume higher // than the hypervolume of the true Pareto front. if ((indicators != null) && (requiredEvaluations == 0)) { double HV = indicators.GetHypervolume(population); if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume)) { requiredEvaluations = evaluations; } } } Logger.Log.Info("ITERATION: " + iteration); Console.WriteLine("ITERATION: " + iteration); // Return as output parameter the required evaluations SetOutputParameter("evaluations", requiredEvaluations); // Return the first non-dominated front Ranking rank = new Ranking(population); Result = rank.GetSubfront(0); return(Result); }