/// <summary>
/// Performs the operation
/// </summary>
/// <param name="obj">Object representing a SolutionSet.</param>
/// <returns>An object representing a <code>SolutionSet<code> with the selected parents</returns>
public override object Execute(object obj)
{
SolutionSet population = (SolutionSet)obj;
int populationSize = (int)Parameters["populationSize"];
SolutionSet result = new SolutionSet(populationSize);
//->Ranking the union
Ranking ranking = new Ranking(population);
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()))
{
//Asign crowding distance to individuals
distance.CrowdingDistanceAssignment(front, problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
result.Add(front.Get(k));
}
//Decrement remaint
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 containt individuals to insert
distance.CrowdingDistanceAssignment(front, problem.NumberOfObjectives);
front.Sort(crowdingComparator);
for (int k = 0; k < remain; k++)
{
result.Add(front.Get(k));
}
remain = 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);
}
public override SolutionSet Execute()
{
double contrDE = 0;
double contrSBX = 0;
double contrBLXA = 0;
double contrPol = 0;
double[] contrReal = new double[3];
contrReal[0] = contrReal[1] = contrReal[2] = 0;
IComparer <Solution> dominance = new DominanceComparator();
IComparer <Solution> crowdingComparator = new CrowdingComparator();
Distance distance = new Distance();
Operator selectionOperator;
//Read parameter values
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);
//Init the variables
population = new SolutionSet(populationSize);
evaluations = 0;
selectionOperator = Operators["selection"];
Offspring[] getOffspring;
int N_O; // number of offpring objects
getOffspring = (Offspring[])GetInputParameter("offspringsCreators");
N_O = getOffspring.Length;
contribution = new double[N_O];
contributionCounter = new int[N_O];
contribution[0] = (double)(populationSize / (double)N_O) / (double)populationSize;
for (int i = 1; i < N_O; i++)
{
contribution[i] = (double)(populationSize / (double)N_O) / (double)populationSize + (double)contribution[i - 1];
}
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
newSolution.Location = i;
population.Add(newSolution);
}
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(2);
Solution[] parents = new Solution[2];
int selectedSolution = JMetalRandom.Next(0, populationSize - 1);
Solution individual = new Solution(population.Get(selectedSolution));
int selected = 0;
bool found = false;
Solution offSpring = null;
double rnd = JMetalRandom.NextDouble();
for (selected = 0; selected < N_O; selected++)
{
if (!found && (rnd <= contribution[selected]))
{
if ("DE" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population, selectedSolution);
contrDE++;
}
else if ("SBXCrossover" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population);
contrSBX++;
}
else if ("BLXAlphaCrossover" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population);
contrBLXA++;
}
else if ("PolynomialMutation" == getOffspring[selected].Id)
{
offSpring = ((PolynomialMutationOffspring)getOffspring[selected]).GetOffspring(individual);
contrPol++;
}
else
{
Console.Error.WriteLine("Error in NSGAIIARandom. Operator " + offSpring + " does not exist");
Logger.Log.Error("NSGAIIARandom. Operator " + offSpring + " does not exist");
}
offSpring.Fitness = (int)selected;
currentCrossover = selected;
found = true;
}
}
Problem.Evaluate(offSpring);
offspringPopulation.Add(offSpring);
evaluations += 1;
// 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;
}
}
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.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;
JMetalCSharp.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);
parallelEvaluator.StartParallelRunner(Problem);;
//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
IntergenSolution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new IntergenSolution((IntergenProblem)Problem);
parallelEvaluator.AddTaskForExecution(new object[] { newSolution, i });;
}
List <IntergenSolution> solutionList = (List <IntergenSolution>)parallelEvaluator.ParallelExecution();
foreach (IntergenSolution solution in solutionList)
{
population.Add(solution);
evaluations++;
}
// Generations
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
IntergenSolution[] parents = new IntergenSolution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
if (evaluations < maxEvaluations)
{
//obtain parents
parents[0] = (IntergenSolution)selectionOperator.Execute(population);
parents[1] = (IntergenSolution)selectionOperator.Execute(population);
IntergenSolution[] offSpring = (IntergenSolution[])crossoverOperator.Execute(parents);
mutationOperator.Execute(offSpring[0]);
mutationOperator.Execute(offSpring[1]);
parallelEvaluator.AddTaskForExecution(new object[] { offSpring[0], evaluations + i });
parallelEvaluator.AddTaskForExecution(new object[] { offSpring[1], evaluations + i });
}
}
List <IntergenSolution> solutions = (List <IntergenSolution>)parallelEvaluator.ParallelExecution();
foreach (IntergenSolution solution in solutions)
{
offspringPopulation.Add(solution);
evaluations++;
//solution.FoundAtEval = evaluations;
}
// 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;
}
// 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;
}
}
//TODO
/*
* Ranking rank2 = new Ranking(population);
*
* Result = rank2.GetSubfront(0);
*/
/*Ranking forGraphicOutput = new Ranking(population);
* var currentBestResultSet = forGraphicOutput.GetSubfront(0);
*
* var firstBestResult = currentBestResultSet.Get(0);
*
* var myProblem = (IntergenProblem)Problem;
* //myProblem.calculated;
*
* //var variantValuesWithoutInteraction = Matrix.Multiply(myProblem.calculated, firstBestResult.);
* //var Model = myProblem.GetModel();
* int mycounter = 0;
* if (mycounter % 500 == 0)
* {
* //RIntegrator.PlotValues(currentBestResultSet, myProblem);
* }
* mycounter++;
* var progress = new UserProgress();
* progress.FeatureP = firstBestResult.Objective[0];
* if (!myProblem.Model.Setting.NoVariantCalculation) progress.VariantP = firstBestResult.Objective[1];
* myProblem.Worker.ReportProgress(evaluations * 100 / maxEvaluations, progress); ;
*
* //Model.CurrentBestImage = "CurrentBest.png"; */
front = ranking.GetSubfront(0);
var minmax = new MinMaxFitness
{
FeatMax = double.MinValue,
FeatMin = double.MaxValue,
VarMax = double.MinValue,
VarMin = double.MaxValue,
InterMax = double.MinValue,
InterMin = double.MaxValue
};
var prob = (IntergenProblem)Problem;
var list = ObjectiveMapping.GetList(prob.ProblemType);
for (var i = 0; i < populationSize; i++)
{
var sol = population.Get(i);
var objindex = 0;
if (list[0])
{
if (sol.Objective[objindex] < minmax.FeatMin)
{
minmax.FeatMin = sol.Objective[objindex];
}
if (sol.Objective[objindex] > minmax.FeatMax)
{
minmax.FeatMax = sol.Objective[objindex];
}
objindex++;
}
if (list[1])
{
if (sol.Objective[objindex] < minmax.InterMin)
{
minmax.InterMin = sol.Objective[objindex];
}
if (sol.Objective[objindex] > minmax.InterMax)
{
minmax.InterMax = sol.Objective[objindex];
}
objindex++;
}
if (list[2])
{
if (sol.Objective[objindex] < minmax.VarMin)
{
minmax.VarMin = sol.Objective[objindex];
}
if (sol.Objective[objindex] > minmax.VarMax)
{
minmax.VarMax = sol.Objective[objindex];
}
}
}
var sol0 = front.Best(new CrowdingDistanceComparator());
var done = FitnessTracker.AddFitn(minmax);
SolutionPlotter.Plot(sol0);
ProgressReporter.ReportSolution(evaluations, sol0, _worker);
if (done)
{
Ranking rank3 = new Ranking(population);
Result = rank3.GetSubfront(0);
SetOutputParameter("evaluations", evaluations);
return(this.Result);
}
}
// Return as output parameter the required evaluations
SetOutputParameter("evaluations", evaluations);
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(this.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;
JMetalCSharp.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);
parallelEvaluator.StartParallelRunner(Problem);;
//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);
parallelEvaluator.AddTaskForExecution(new object[] { newSolution });;
}
List <Solution> solutionList = (List <Solution>)parallelEvaluator.ParallelExecution();
foreach (Solution solution in solutionList)
{
population.Add(solution);
evaluations++;
}
// 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]);
parallelEvaluator.AddTaskForExecution(new object[] { offSpring[0] });;
parallelEvaluator.AddTaskForExecution(new object[] { offSpring[1] });;
}
}
List <Solution> solutions = (List <Solution>)parallelEvaluator.ParallelExecution();
foreach (Solution solution in solutions)
{
offspringPopulation.Add(solution);
evaluations++;
}
// 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;
}
// 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(this.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()
{
// !!!! NEEDED PARAMETES !!! start
//J* Parameters
int populationSize = -1; // J* store population size
int maxEvaluations = -1; // J* store number of max Evaluations
int evaluations; // J* number of current evaluations
// J* Objects needed to illustrate the use of quality indicators inside the algorithms
QualityIndicator indicators = null; // QualityIndicator object
int requiredEvaluations; // Use in the example of use of the
// indicators object (see below)
// J* populations needed to implement NSGA-II
SolutionSet population; //J* Current population
SolutionSet offspringPopulation; //J* offspring population
SolutionSet union; //J* population resultant from current and offpring population
//J* Genetic Operators
Operator mutationOperator;
Operator crossoverOperator;
Operator selectionOperator;
//J* Used to evaluate crowding distance
Distance distance = new Distance();
//J* !!!! NEEDED PARAMETES !!! end
//J* !!! INITIALIZING PARAMETERS - start !!!
//Read the parameters
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations); //J* required
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize); //J* required
JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators); //J* optional
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = Operators["mutation"];
crossoverOperator = Operators["crossover"];
selectionOperator = Operators["selection"];
//J* !!! INITIALIZING PARAMETERS - end !!!
//J* !!! Creating first population !!!
JMetalRandom.SetRandom(comp.MyRand);
comp.LogAddMessage("Random seed = " + comp.Seed);
// 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);
// 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);
comp.LogAddMessage("Evaluations = " + evaluations);
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}
/// <summary>
/// Runs of the AbYSS 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()
{
// STEP 1. Initialize parameters
InitParam();
// STEP 2. Build the initial solutionSet
Solution solution;
for (int i = 0; i < solutionSetSize; i++)
{
solution = DiversificationGeneration();
this.Problem.Evaluate(solution);
this.Problem.EvaluateConstraints(solution);
evaluations++;
solution = (Solution)improvementOperator.Execute(solution);
evaluations += improvementOperator.GetEvaluations();
solutionSet.Add(solution);
}
// STEP 3. Main loop
int newSolutions = 0;
while (evaluations < maxEvaluations)
{
ReferenceSetUpdate(true);
newSolutions = SubSetGeneration();
while (newSolutions > 0)
{ // New solutions are created
ReferenceSetUpdate(false);
if (evaluations >= maxEvaluations)
{
Result = archive;
return(archive);
}
newSolutions = SubSetGeneration();
} // while
// RE-START
if (evaluations < maxEvaluations)
{
solutionSet.Clear();
// Add refSet1 to SolutionSet
for (int i = 0; i < refSet1.Size(); i++)
{
solution = refSet1.Get(i);
solution.UnMarked();
solution = (Solution)improvementOperator.Execute(solution);
evaluations += improvementOperator.GetEvaluations();
solutionSet.Add(solution);
}
// Remove refSet1 and refSet2
refSet1.Clear();
refSet2.Clear();
// Sort the archive and insert the best solutions
distance.CrowdingDistanceAssignment(archive, this.Problem.NumberOfObjectives);
archive.Sort(crowdingDistance);
int insert = solutionSetSize / 2;
if (insert > archive.Size())
{
insert = archive.Size();
}
if (insert > (solutionSetSize - solutionSet.Size()))
{
insert = solutionSetSize - solutionSet.Size();
}
// Insert solutions
for (int i = 0; i < insert; i++)
{
solution = new Solution(archive.Get(i));
solution.UnMarked();
solutionSet.Add(solution);
}
// Create the rest of solutions randomly
while (solutionSet.Size() < solutionSetSize)
{
solution = DiversificationGeneration();
this.Problem.EvaluateConstraints(solution);
this.Problem.Evaluate(solution);
evaluations++;
solution = (Solution)improvementOperator.Execute(solution);
evaluations += improvementOperator.GetEvaluations();
solution.UnMarked();
solutionSet.Add(solution);
}
}
}
// STEP 4. Return the archive
Result = archive;
return(archive);
}
/// <summary>
/// Implements the referenceSetUpdate method.
/// </summary>
/// <param name="build">build if true, indicates that the reference has to be build for the
/// first time; if false, indicates that the reference set has to be
/// updated with new solutions</param>
public void ReferenceSetUpdate(bool build)
{
if (build)
{ // Build a new reference set
// STEP 1. Select the p best individuals of P, where p is refSet1Size.
// Selection Criterium: Spea2Fitness
Solution individual;
(new Spea2Fitness(solutionSet)).FitnessAssign();
solutionSet.Sort(fitness);
// STEP 2. Build the RefSet1 with these p individuals
for (int i = 0; i < refSet1Size; i++)
{
individual = solutionSet.Get(0);
solutionSet.Remove(0);
individual.UnMarked();
refSet1.Add(individual);
}
// STEP 3. Compute Euclidean distances in SolutionSet to obtain q
// individuals, where q is refSet2Size_
for (int i = 0; i < solutionSet.Size(); i++)
{
individual = solutionSet.Get(i);
individual.DistanceToSolutionSet = distance.DistanceToSolutionSetInSolutionSpace(individual, refSet1);
}
int size = refSet2Size;
if (solutionSet.Size() < refSet2Size)
{
size = solutionSet.Size();
}
// STEP 4. Build the RefSet2 with these q individuals
for (int i = 0; i < size; i++)
{
// Find the maximumMinimunDistanceToPopulation
double maxMinimum = 0.0;
int index = 0;
for (int j = 0; j < solutionSet.Size(); j++)
{
if (solutionSet.Get(j).DistanceToSolutionSet > maxMinimum)
{
maxMinimum = solutionSet.Get(j).DistanceToSolutionSet;
index = j;
}
}
individual = solutionSet.Get(index);
solutionSet.Remove(index);
// Update distances to REFSET in population
for (int j = 0; j < solutionSet.Size(); j++)
{
double aux = distance.DistanceBetweenSolutions(solutionSet.Get(j), individual);
if (aux < individual.DistanceToSolutionSet)
{
solutionSet.Get(j).DistanceToSolutionSet = aux;
}
}
// Insert the individual into REFSET2
refSet2.Add(individual);
// Update distances in REFSET2
for (int j = 0; j < refSet2.Size(); j++)
{
for (int k = 0; k < refSet2.Size(); k++)
{
if (i != j)
{
double aux = distance.DistanceBetweenSolutions(refSet2.Get(j), refSet2.Get(k));
if (aux < refSet2.Get(j).DistanceToSolutionSet)
{
refSet2.Get(j).DistanceToSolutionSet = aux;
}
}
}
}
}
}
else
{ // Update the reference set from the subset generation result
Solution individual;
for (int i = 0; i < subSet.Size(); i++)
{
individual = (Solution)improvementOperator.Execute(subSet.Get(i));
evaluations += improvementOperator.GetEvaluations();
if (RefSet1Test(individual))
{ //Update distance of RefSet2
for (int indSet2 = 0; indSet2 < refSet2.Size(); indSet2++)
{
double aux = distance.DistanceBetweenSolutions(individual, refSet2.Get(indSet2));
if (aux < refSet2.Get(indSet2).DistanceToSolutionSet)
{
refSet2.Get(indSet2).DistanceToSolutionSet = aux;
}
}
}
else
{
RefSet2Test(individual);
}
}
subSet.Clear();
}
}
public override SolutionSet Execute()
{
double contrDE = 0;
double contrSBX = 0;
double contrPol = 0;
double contrTotalDE = 0;
double contrTotalSBX = 0;
double contrTotalPol = 0;
double[] contrReal = new double[3];
contrReal[0] = contrReal[1] = contrReal[2] = 0;
IComparer <Solution> dominance = new DominanceComparator();
IComparer <Solution> crowdingComparator = new CrowdingComparator();
Distance distance = new Distance();
Operator selectionOperator;
//Read parameter values
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
//Init the variables
population = new SolutionSet(populationSize);
evaluations = 0;
selectionOperator = Operators["selection"];
Offspring[] getOffspring;
int N_O; // number of offpring objects
getOffspring = ((Offspring[])GetInputParameter("offspringsCreators"));
N_O = getOffspring.Length;
contribution = new double[N_O];
contributionCounter = new int[N_O];
contribution[0] = (double)(populationSize / (double)N_O) / (double)populationSize;
for (int i = 1; i < N_O; i++)
{
contribution[i] = (double)(populationSize / (double)N_O) / (double)populationSize + (double)contribution[i - 1];
}
for (int i = 0; i < N_O; i++)
{
Console.WriteLine(getOffspring[i].Configuration());
Console.WriteLine("Contribution: " + contribution[i]);
}
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
newSolution.Location = i;
population.Add(newSolution);
}
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 1); i++)
{
if (evaluations < maxEvaluations)
{
Solution individual = new Solution(population.Get(JMetalRandom.Next(0, populationSize - 1)));
int selected = 0;
bool found = false;
Solution offSpring = null;
double rnd = JMetalRandom.NextDouble();
for (selected = 0; selected < N_O; selected++)
{
if (!found && (rnd <= contribution[selected]))
{
if ("DE" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population, i);
contrDE++;
}
else if ("SBXCrossover" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population);
contrSBX++;
}
else if ("PolynomialMutation" == getOffspring[selected].Id)
{
offSpring = ((PolynomialMutationOffspring)getOffspring[selected]).GetOffspring(individual);
contrPol++;
}
else
{
Logger.Log.Error("Error in NSGAIIAdaptive. Operator " + offSpring + " does not exist");
Console.WriteLine("Error in NSGAIIAdaptive. Operator " + offSpring + " does not exist");
}
offSpring.Fitness = (int)selected;
found = true;
}
}
Problem.Evaluate(offSpring);
offspringPopulation.Add(offSpring);
evaluations += 1;
}
}
// 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;
}
// CONTRIBUTION CALCULATING PHASE
// First: reset contribution counter
for (int i = 0; i < N_O; i++)
{
contributionCounter[i] = 0;
}
// Second: determine the contribution of each operator
for (int i = 0; i < population.Size(); i++)
{
if ((int)population.Get(i).Fitness != -1)
{
contributionCounter[(int)population.Get(i).Fitness] += 1;
}
population.Get(i).Fitness = -1;
}
contrTotalDE += contributionCounter[0];
contrTotalSBX += contributionCounter[1];
contrTotalPol += contributionCounter[2];
int minimumContribution = 2;
int totalContributionCounter = 0;
for (int i = 0; i < N_O; i++)
{
if (contributionCounter[i] < minimumContribution)
{
contributionCounter[i] = minimumContribution;
}
totalContributionCounter += contributionCounter[i];
}
if (totalContributionCounter == 0)
{
for (int i = 0; i < N_O; i++)
{
contributionCounter[i] = 10;
}
}
// Third: calculating contribution
contribution[0] = contributionCounter[0] * 1.0
/ (double)totalContributionCounter;
for (int i = 1; i < N_O; i++)
{
contribution[i] = contribution[i - 1] + 1.0 * contributionCounter[i] / (double)totalContributionCounter;
}
}
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}
/**
* Runs of the GDE3 algorithm.
* @return a <code>SolutionSet</code> that is a set of non dominated solutions
* as a result of the algorithm execution
* @throws JMException
*/
public override SolutionSet Execute()
{
int populationSize = -1;
int maxIterations = -1;
int evaluations;
int iterations;
SolutionSet population;
SolutionSet offspringPopulation;
Distance distance;
IComparer <Solution> dominance;
Operator selectionOperator;
Operator crossoverOperator;
distance = new Distance();
dominance = new DominanceComparator();
Solution[] parent;
//Read the parameters
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxIterations", ref maxIterations);
selectionOperator = this.Operators["selection"];
crossoverOperator = this.Operators["crossover"];
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
iterations = 0;
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(this.Problem);
this.Problem.Evaluate(newSolution);
this.Problem.EvaluateConstraints(newSolution);
evaluations++;
population.Add(newSolution);
}
// Generations ...
while (iterations < maxIterations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize * 2);
for (int i = 0; i < populationSize; i++)
{
// Obtain parents. Two parameters are required: the population and the
// index of the current individual
parent = (Solution[])selectionOperator.Execute(new object[] { population, i });
Solution child;
// Crossover. Two parameters are required: the current individual and the
// array of parents
child = (Solution)crossoverOperator.Execute(new object[] { population.Get(i), parent });
this.Problem.Evaluate(child);
this.Problem.EvaluateConstraints(child);
evaluations++;
// Dominance test
int result;
result = dominance.Compare(population.Get(i), child);
if (result == -1)
{ // Solution i dominates child
offspringPopulation.Add(population.Get(i));
}
else if (result == 1)
{ // child dominates
offspringPopulation.Add(child);
}
else
{ // the two solutions are non-dominated
offspringPopulation.Add(child);
offspringPopulation.Add(population.Get(i));
}
}
// Ranking the offspring population
Ranking ranking = new Ranking(offspringPopulation);
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, this.Problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
} // for
//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
while (front.Size() > remain)
{
distance.CrowdingDistanceAssignment(front, this.Problem.NumberOfObjectives);
front.Remove(front.IndexWorst(new CrowdingComparator()));
}
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
}
remain = 0;
}
iterations++;
}
// Return the first non-dominated front
Ranking rnk = new Ranking(population);
this.Result = rnk.GetSubfront(0);
return(this.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 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);
}