/// <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 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);
}
