/// <summary>
/// Joins two solution sets: the current one and the one passed as argument
/// </summary>
/// <param name="solutionSet">
/// A <see cref="SolutionSet"/>
/// </param>
/// <returns> A new solution set
/// A <see cref="SolutionSet"/>
/// </returns>
public SolutionSet union(SolutionSet solutionSet)
{
// <pex>
if (solutionSet == (SolutionSet)null)
{
throw new ArgumentNullException("solutionSet");
}
// </pex>
//Check the correct size
int newSize = this.size() + solutionSet.size();
if (newSize < capacity_)
{
//////////////////
newSize = capacity_;
}
//Create a new population
SolutionSet union = new SolutionSet(newSize);
for (int i = 0; i < this.size(); i++)
{
union.add(this.solutionList_[i]);
}
// for
for (int i = this.size(); i < (this.size() + solutionSet.size()); i++)
{
union.add(solutionSet.solutionList_[i - this.size()]);
}
// for
return(union);
}
//changes solution list to solution set
private static SolutionSet SolutionListToPop(List <List <double> > populationList, Algorithm algorithm, int numVar)
{
int popSize = populationList[0].Count;
int numObj = populationList.Count - numVar;
SolutionSet population = new SolutionSet(popSize);
Solution newSolution;
for (int j = 0; j < popSize; j++)
{
newSolution = new Solution(algorithm.problem_);
for (int i = 0; i < numVar; i++)
{
newSolution.variable_[i].value_ = populationList[i][j];
}
population.add(newSolution);
}
for (int j = 0; j < population.size(); j++)
{
for (int i = 0; i < numObj; i++)
{
population[j].objective_[i] = populationList[i + numVar][j];
}
}
return(population);
}
/// <summary>
/// Generated new the children population list based on the parents population list.
/// This node needs to be used in a loop along with Assign Fitness Function node and
/// Sorting node.
/// </summary>
/// <param name="populationList">Parents population list.</param>
/// <param name="lowerLimits">List of lower limits for new generation.</param>
/// <param name="upperLimits">List of upper limits for new generation.</param>
/// <returns>Generated population list.</returns>
public static List <List <double> > GenerationAlgorithm(List <List <double> > populationList, List <int> lowerLimits, List <int> upperLimits)
{
int numVar = lowerLimits.Count;
int numObj = populationList.Count - numVar;
//create the NSGA-II algorithm
Algorithm algorithm = CreateAlgorithm(numObj, lowerLimits, upperLimits);
//change solutions list to solutionSet
SolutionSet population = SolutionListToPop(populationList, algorithm, numVar);
Operator mutationOperator = algorithm.operators["mutation"];
Operator crossoverOperator = algorithm.operators["crossover"];
Operator selectionOperator = algorithm.operators["selection"];
int populationSize = population.size();
SolutionSet offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
// selection
parents[0] = (Solution)selectionOperator.execute(population);
parents[1] = (Solution)selectionOperator.execute(population);
// crossover
Solution[] offSpring = (Solution[])crossoverOperator.execute(parents);
// mutation
mutationOperator.execute(offSpring[0]);
mutationOperator.execute(offSpring[1]);
offspringPopulation.@add(offSpring[0]);
offspringPopulation.add(offSpring[1]);
}
return(PopToSolutionList(offspringPopulation));
}
/// <summary>
/// Creates the initial solution lists of variables and objectives.
/// The objectives will be assigned after evaluation using
/// NSGA_II Assigne Fitness Function Results node.
/// </summary>
/// <param name="populationSize">Size that will be used to generate population.</param>
/// <param name="numObjectives">Number of objectives that you are trying to optimize. If your problem is single objective optimization, set this number to one.</param>
/// <param name="lowerLimits">List of lower limits for all variable. The number of lower limits should be the same as the number of variables.</param>
/// <param name="upperLimits">List of upper limits for all variable. The number of upper limits should be the same as the number of variables.</param>
/// <returns>An initial solution list.</returns>
public static List <List <double> > InitialSolutionList(double populationSize, double numObjectives, List <int> lowerLimits, List <int> upperLimits)
{
//number of varables plus number of objectives
int numObj = Convert.ToInt32(numObjectives);
int numVar = lowerLimits.Count;
Algorithm algorithm = CreateAlgorithm(numObjectives, lowerLimits, upperLimits);
//Initializing variables
SolutionSet population = new SolutionSet(Convert.ToInt32(populationSize));
// Creating the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(algorithm.problem_);
population.add(newSolution);
}
List <List <double> > test = PopToSolutionList(population);
return(PopToSolutionList(population));
}
// The solution set to be ranked
/*
* public double [][] distanceMatrix(SolutionSet solutionSet) {
* Solution solutionI, solutionJ;
*
* //The matrix of distances
* double [][] distance = new double [solutionSet.size()][solutionSet.size()];
* //-> Calculate the distances
* for (int i = 0; i < solutionSet.size(); i++){
* distance[i][i] = 0.0;
* solutionI = solutionSet.get(i);
* for (int j = i + 1; j < solutionSet.size(); j++){
* solutionJ = solutionSet.get(j);
* distance[i][j] = this.distanceBetweenObjectives(solutionI,solutionJ);
* distance[j][i] = distance[i][j];
* } // for
* } // for
*
* //->Return the matrix of distances
* return distance;
* } // distanceMatrix
*
* public double distanceToSolutionSetInObjectiveSpace(Solution solution,
* SolutionSet solutionSet) throws JMException{
* //At start point the distance is the max
* double distance = Double.MAX_VALUE;
*
* // found the min distance respect to population
* for (int i = 0; i < solutionSet.size();i++){
* double aux = this.distanceBetweenObjectives(solution,solutionSet.get(i));
* if (aux < distance)
* distance = aux;
* } // for
*
* //->Return the best distance
* return distance;
* } // distanceToSolutionSetinObjectiveSpace
*
* public double distanceToSolutionSetInSolutionSpace(Solution solution,
* SolutionSet solutionSet) throws JMException{
* //At start point the distance is the max
* double distance = Double.MAX_VALUE;
*
* // found the min distance respect to population
* for (int i = 0; i < solutionSet.size();i++){
* double aux = this.distanceBetweenSolutions(solution,solutionSet.get(i));
* if (aux < distance)
* distance = aux;
* } // for
*
* //->Return the best distance
* return distance;
* } // distanceToSolutionSetInSolutionSpace
*
*
* public double distanceBetweenSolutions(Solution solutionI, Solution solutionJ)
* throws JMException{
* //->Obtain his decision variables
* Variable[] decisionVariableI = solutionI.getDecisionVariables();
* Variable[] decisionVariableJ = solutionJ.getDecisionVariables();
*
* double diff; //Auxiliar var
* double distance = 0.0;
* //-> Calculate the Euclidean distance
* for (int i = 0; i < decisionVariableI.length; i++){
* diff = decisionVariableI[i].getValue() -
* decisionVariableJ[i].getValue();
* distance += Math.pow(diff,2.0);
* } // for
*
* //-> Return the euclidean distance
* return Math.sqrt(distance);
* } // distanceBetweenSolutions
*
*
* public double distanceBetweenObjectives(Solution solutionI, Solution solutionJ){
* double diff; //Auxiliar var
* double distance = 0.0;
* //-> Calculate the euclidean distance
* for (int nObj = 0; nObj < solutionI.numberOfObjectives();nObj++){
* diff = solutionI.getObjective(nObj) - solutionJ.getObjective(nObj);
* distance += Math.pow(diff,2.0);
* } // for
*
* //Return the euclidean distance
* return Math.sqrt(distance);
* } // distanceBetweenObjectives.
*
*/
internal static void crowdingDistanceAssignment(SolutionSet solutionSet, int nObjs)
{
// <pex>
if (solutionSet == (SolutionSet)null)
{
throw new ArgumentNullException("solutionSet");
}
if (solutionSet[0] == (Solution)null)
{
throw new ArgumentNullException("solutionSet");
}
// </pex>
int size = solutionSet.size();
if (size == 0)
{
return;
}
if (size == 1)
{
solutionSet[0].crowdingDistance_ = Double.MaxValue;
//solutionSet.get(0).setCrowdingDistance(Double.POSITIVE_INFINITY);
return;
} // if
if (size == 2)
{
solutionSet[0].crowdingDistance_ = Double.MaxValue;
solutionSet[1].crowdingDistance_ = Double.MaxValue;
return;
} // if
//Use a new SolutionSet to evite alter original solutionSet
SolutionSet front = new SolutionSet(size);
for (int i = 0; i < size; i++)
{
front.add(solutionSet[i]);
}
for (int i = 0; i < size; i++)
{
front[i].crowdingDistance_ = 0.0;
}
double objetiveMaxn;
double objetiveMinn;
double distance;
for (int i = 0; i < nObjs; i++)
{
// Sort the population by Obj n
IComparer comp = new ObjectiveComparator(i);
front.solutionList_.Sort(comp.Compare);
//front.sort(new ObjectiveComparator(i));
objetiveMinn = front[0].objective_[i];
objetiveMaxn = front[front.size() - 1].objective_[i];
//Set de crowding distance
front[0].crowdingDistance_ = Double.MaxValue;
front[size - 1].crowdingDistance_ = Double.MaxValue;
for (int j = 1; j < size - 1; j++)
{
distance = front[j + 1].objective_[i] - front[j - 1].objective_[i];
distance = distance / (objetiveMaxn - objetiveMinn);
distance += front[j].crowdingDistance_;
front[j].crowdingDistance_ = distance;
} // for
} // for
} // crowdingDistanceAssing
/// <summary>
/// Generated new the children population list based on the parents population list.
/// This node needs to be used in a loop along with Assign Fitness Function node and
/// Sorting node.
/// </summary>
/// <param name="populationList">Parents population list.</param>
/// <param name="lowerLimits">List of lower limits for new generation.</param>
/// <param name="upperLimits">List of upper limits for new generation.</param>
/// <returns>Generated population list.</returns>
public static List<List<double>> GenerationAlgorithm(List<List<double>> populationList, List<double> lowerLimits, List<double> upperLimits)
{
int numVar = lowerLimits.Count;
int numObj = populationList.Count - numVar;
//create the NSGA-II algorithm
Algorithm algorithm = CreateAlgorithm(numObj, lowerLimits, upperLimits);
//change solutions list to solutionSet
SolutionSet population = SolutionListToPop(populationList, algorithm, numVar);
Operator mutationOperator = algorithm.operators["mutation"];
Operator crossoverOperator = algorithm.operators["crossover"];
Operator selectionOperator = algorithm.operators["selection"];
int populationSize = population.size();
SolutionSet offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
// selection
parents[0] = (Solution)selectionOperator.execute(population);
parents[1] = (Solution)selectionOperator.execute(population);
// crossover
Solution[] offSpring = (Solution[])crossoverOperator.execute(parents);
// mutation
mutationOperator.execute(offSpring[0]);
mutationOperator.execute(offSpring[1]);
offspringPopulation.@add(offSpring[0]);
offspringPopulation.add(offSpring[1]);
}
return PopToSolutionList(offspringPopulation);
}
/// <summary>
/// Creates the initial solution lists of variables and objectives.
/// The objectives will be assigned after evaluation using
/// NSGA_II Assigne Fitness Function Results node.
/// </summary>
/// <param name="populationSize">Size that will be used to generate population.</param>
/// <param name="numObjectives">Number of objectives that you are trying to optimize. If your problem is single objective optimization, set this number to one.</param>
/// <param name="lowerLimits">List of lower limits for all variable. The number of lower limits should be the same as the number of variables.</param>
/// <param name="upperLimits">List of upper limits for all variable. The number of upper limits should be the same as the number of variables.</param>
/// <returns>An initial solution list.</returns>
public static List<List<double>> InitialSolutionList(double populationSize, double numObjectives, List<double> lowerLimits, List<double> upperLimits)
{
//number of varables plus number of objectives
int numObj = Convert.ToInt32(numObjectives);
int numVar = lowerLimits.Count;
Algorithm algorithm = CreateAlgorithm(numObjectives, lowerLimits, upperLimits);
//Initializing variables
SolutionSet population = new SolutionSet(Convert.ToInt32(populationSize));
// Creating the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(algorithm.problem_);
population.add(newSolution);
}
List<List<double>> test = PopToSolutionList(population);
return PopToSolutionList(population);
}
//changes solution list to solution set
private static SolutionSet SolutionListToPop(List<List<double>> populationList, Algorithm algorithm, int numVar)
{
int popSize = populationList[0].Count;
int numObj = populationList.Count - numVar;
SolutionSet population = new SolutionSet(popSize);
Solution newSolution;
for (int j = 0; j < popSize; j++)
{
newSolution = new Solution(algorithm.problem_);
for (int i = 0; i < numVar; i++)
{
newSolution.variable_[i].value_ = populationList[i][j];
}
population.add(newSolution);
}
for (int j = 0; j < population.size(); j++)
{
for (int i = 0; i < numObj; i++)
{
population[j].objective_[i] = populationList[i + numVar][j];
}
}
return population;
}
/// <summary>
/// Joins two solution sets: the current one and the one passed as argument
/// </summary>
/// <param name="solutionSet">
/// A <see cref="SolutionSet"/>
/// </param>
/// <returns> A new solution set
/// A <see cref="SolutionSet"/>
/// </returns>
public SolutionSet union (SolutionSet solutionSet)
{
// <pex>
if (solutionSet == (SolutionSet)null)
throw new ArgumentNullException("solutionSet");
// </pex>
//Check the correct size
int newSize = this.size () + solutionSet.size ();
if (newSize < capacity_)
//////////////////
newSize = capacity_;
//Create a new population
SolutionSet union = new SolutionSet (newSize);
for (int i = 0; i < this.size (); i++) {
union.add (this.solutionList_[i]);
}
// for
for (int i = this.size (); i < (this.size () + solutionSet.size ()); i++) {
union.add (solutionSet.solutionList_[i - this.size ()]);
}
// for
return union;
}
// NSGAII
public override SolutionSet execute()
{
int populationSize;
int maxEvaluations;
int evaluations;
SolutionSet population;
SolutionSet offspringPopulation;
SolutionSet union;
SolutionSet allTest = new SolutionSet();
Operator mutationOperator;
Operator crossoverOperator;
Operator selectionOperator;
populationSize = (int)inputParameters_["populationSize"];
maxEvaluations = (int)inputParameters_["maxEvaluations"];
// Initializing variables
population = new SolutionSet(populationSize);
evaluations = 0;
//System.Console.WriteLine("Solves Name:" + problem_.problemName_);
//System.Console.WriteLine("Pop size : " + populationSize);
//System.Console.WriteLine("Max Evals: " + maxEvaluations);
// Reading operators
mutationOperator = operators["mutation"];
crossoverOperator = operators["crossover"];
selectionOperator = operators["selection"];
//System.Console.WriteLine("Crossover parameters: " + crossoverOperator);
//System.Console.WriteLine("Mutation parameters: " + mutationOperator);
// Creating the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(problem_);
//newSolution.variable_[0] = 1;
problem_.evaluate(newSolution);
//newSolution.objective_[0] = 10;
//Mohammad
//slnLst.Add(newSolution);
//System.Console.WriteLine ("" + i + ": " + newSolution);
//problem_.evaluateConstraints(newSolution);
evaluations++;
population.add(newSolution);
}
//for
// Main loop
while (evaluations < maxEvaluations)
{
// Creating the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
if (evaluations < maxEvaluations)
{
//if ((evaluations % 1000) == 0)
// Console.WriteLine("Evals: " + evaluations);
// selection
parents[0] = (Solution)selectionOperator.execute(population);
parents[1] = (Solution)selectionOperator.execute(population);
// crossover
Solution[] offSpring = (Solution[])crossoverOperator.execute(parents);
// mutation
mutationOperator.execute(offSpring[0]);
mutationOperator.execute(offSpring[1]);
//Environment.Exit(0);
// evaluation
problem_.evaluate(offSpring[0]);
problem_.evaluate(offSpring[1]);
offspringPopulation.@add(offSpring[0]);
offspringPopulation.add(offSpring[1]);
evaluations += 2;
}
// if
}
// for
// Creating the solutionSet union of solutionSet and offSpring
union = ((SolutionSet)population).union(offspringPopulation);
//Mohammad
allTest = ((SolutionSet)allTest).union(union);
//System.Console.WriteLine ("Union size:" + union.size ());
// Ranking the union
Ranking ranking = new Ranking(union);
int remain = populationSize;
int index = 0;
SolutionSet front = null;
//Distance distance = new Distance ();
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[k]);
}
// for
//Decrement remain
remain = remain - front.size();
//Obtain the next front
index++;
if (remain > 0)
{
front = ranking.getSubfront(index);
}
// if
}
// while
// 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_);
IComparer comp = new CrowdingDistanceComparator();
front.solutionList_.Sort(comp.Compare);
for (int k = 0; k < remain; k++)
{
population.@add(front[k]);
}
// for
remain = 0;
}
// if
}
// while
// Return as output parameter the required evaluations
outputParameters_["evaluations"] = evaluations;
// Return the first non-dominated front
Ranking ranking2 = new Ranking(population);
//Console.WriteLine(slnLst[0].objective_.GetValue(0));
//File.WriteAllLines("C:/text.txt",slnLst.ConvertAll(Convert.ToString));
return ranking2.getSubfront(0);
}
// The solution set to be ranked
/*
public double [][] distanceMatrix(SolutionSet solutionSet) {
Solution solutionI, solutionJ;
//The matrix of distances
double [][] distance = new double [solutionSet.size()][solutionSet.size()];
//-> Calculate the distances
for (int i = 0; i < solutionSet.size(); i++){
distance[i][i] = 0.0;
solutionI = solutionSet.get(i);
for (int j = i + 1; j < solutionSet.size(); j++){
solutionJ = solutionSet.get(j);
distance[i][j] = this.distanceBetweenObjectives(solutionI,solutionJ);
distance[j][i] = distance[i][j];
} // for
} // for
//->Return the matrix of distances
return distance;
} // distanceMatrix
public double distanceToSolutionSetInObjectiveSpace(Solution solution,
SolutionSet solutionSet) throws JMException{
//At start point the distance is the max
double distance = Double.MAX_VALUE;
// found the min distance respect to population
for (int i = 0; i < solutionSet.size();i++){
double aux = this.distanceBetweenObjectives(solution,solutionSet.get(i));
if (aux < distance)
distance = aux;
} // for
//->Return the best distance
return distance;
} // distanceToSolutionSetinObjectiveSpace
public double distanceToSolutionSetInSolutionSpace(Solution solution,
SolutionSet solutionSet) throws JMException{
//At start point the distance is the max
double distance = Double.MAX_VALUE;
// found the min distance respect to population
for (int i = 0; i < solutionSet.size();i++){
double aux = this.distanceBetweenSolutions(solution,solutionSet.get(i));
if (aux < distance)
distance = aux;
} // for
//->Return the best distance
return distance;
} // distanceToSolutionSetInSolutionSpace
public double distanceBetweenSolutions(Solution solutionI, Solution solutionJ)
throws JMException{
//->Obtain his decision variables
Variable[] decisionVariableI = solutionI.getDecisionVariables();
Variable[] decisionVariableJ = solutionJ.getDecisionVariables();
double diff; //Auxiliar var
double distance = 0.0;
//-> Calculate the Euclidean distance
for (int i = 0; i < decisionVariableI.length; i++){
diff = decisionVariableI[i].getValue() -
decisionVariableJ[i].getValue();
distance += Math.pow(diff,2.0);
} // for
//-> Return the euclidean distance
return Math.sqrt(distance);
} // distanceBetweenSolutions
public double distanceBetweenObjectives(Solution solutionI, Solution solutionJ){
double diff; //Auxiliar var
double distance = 0.0;
//-> Calculate the euclidean distance
for (int nObj = 0; nObj < solutionI.numberOfObjectives();nObj++){
diff = solutionI.getObjective(nObj) - solutionJ.getObjective(nObj);
distance += Math.pow(diff,2.0);
} // for
//Return the euclidean distance
return Math.sqrt(distance);
} // distanceBetweenObjectives.
*/
internal static void crowdingDistanceAssignment (SolutionSet solutionSet, int nObjs)
{
// <pex>
if (solutionSet == (SolutionSet)null)
throw new ArgumentNullException("solutionSet");
if (solutionSet[0] == (Solution)null)
throw new ArgumentNullException("solutionSet");
// </pex>
int size = solutionSet.size ();
if (size == 0)
return;
if (size == 1) {
solutionSet[0].crowdingDistance_ = Double.MaxValue ;
//solutionSet.get(0).setCrowdingDistance(Double.POSITIVE_INFINITY);
return;
} // if
if (size == 2) {
solutionSet[0].crowdingDistance_ = Double.MaxValue;
solutionSet[1].crowdingDistance_ = Double.MaxValue;
return;
} // if
//Use a new SolutionSet to evite alter original solutionSet
SolutionSet front = new SolutionSet(size);
for (int i = 0; i < size; i++){
front.add(solutionSet[i]);
}
for (int i = 0; i < size; i++)
front[i].crowdingDistance_ = 0.0 ;
double objetiveMaxn;
double objetiveMinn;
double distance;
for (int i = 0; i<nObjs; i++) {
// Sort the population by Obj n
IComparer comp = new ObjectiveComparator(i) ;
front.solutionList_.Sort(comp.Compare) ;
//front.sort(new ObjectiveComparator(i));
objetiveMinn = front[0].objective_[i] ;
objetiveMaxn = front[front.size()-1].objective_[i] ;
//Set de crowding distance
front[0].crowdingDistance_ = Double.MaxValue ;
front[size -1].crowdingDistance_ = Double.MaxValue ;
for (int j = 1; j < size-1; j++) {
distance = front[j+1].objective_[i] - front[j-1].objective_[i];
distance = distance / (objetiveMaxn - objetiveMinn);
distance += front[j].crowdingDistance_ ;
front[j].crowdingDistance_ = distance;
} // for
} // for
} // crowdingDistanceAssing
// NSGAII
public override SolutionSet execute()
{
int populationSize;
int maxEvaluations;
int evaluations;
SolutionSet population;
SolutionSet offspringPopulation;
SolutionSet union;
SolutionSet allTest = new SolutionSet();
Operator mutationOperator;
Operator crossoverOperator;
Operator selectionOperator;
populationSize = (int)inputParameters_["populationSize"];
maxEvaluations = (int)inputParameters_["maxEvaluations"];
// Initializing variables
population = new SolutionSet(populationSize);
evaluations = 0;
//System.Console.WriteLine("Solves Name:" + problem_.problemName_);
//System.Console.WriteLine("Pop size : " + populationSize);
//System.Console.WriteLine("Max Evals: " + maxEvaluations);
// Reading operators
mutationOperator = operators["mutation"];
crossoverOperator = operators["crossover"];
selectionOperator = operators["selection"];
//System.Console.WriteLine("Crossover parameters: " + crossoverOperator);
//System.Console.WriteLine("Mutation parameters: " + mutationOperator);
// Creating the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(problem_);
//newSolution.variable_[0] = 1;
problem_.evaluate(newSolution);
//newSolution.objective_[0] = 10;
//Mohammad
//slnLst.Add(newSolution);
//System.Console.WriteLine ("" + i + ": " + newSolution);
//problem_.evaluateConstraints(newSolution);
evaluations++;
population.add(newSolution);
}
//for
// Main loop
while (evaluations < maxEvaluations)
{
// Creating the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
if (evaluations < maxEvaluations)
{
//if ((evaluations % 1000) == 0)
// Console.WriteLine("Evals: " + evaluations);
// selection
parents[0] = (Solution)selectionOperator.execute(population);
parents[1] = (Solution)selectionOperator.execute(population);
// crossover
Solution[] offSpring = (Solution[])crossoverOperator.execute(parents);
// mutation
mutationOperator.execute(offSpring[0]);
mutationOperator.execute(offSpring[1]);
//Environment.Exit(0);
// evaluation
problem_.evaluate(offSpring[0]);
problem_.evaluate(offSpring[1]);
offspringPopulation.@add(offSpring[0]);
offspringPopulation.add(offSpring[1]);
evaluations += 2;
}
// if
}
// for
// Creating the solutionSet union of solutionSet and offSpring
union = ((SolutionSet)population).union(offspringPopulation);
//Mohammad
allTest = ((SolutionSet)allTest).union(union);
//System.Console.WriteLine ("Union size:" + union.size ());
// Ranking the union
Ranking ranking = new Ranking(union);
int remain = populationSize;
int index = 0;
SolutionSet front = null;
//Distance distance = new Distance ();
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[k]);
}
// for
//Decrement remain
remain = remain - front.size();
//Obtain the next front
index++;
if (remain > 0)
{
front = ranking.getSubfront(index);
}
// if
}
// while
// 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_);
IComparer comp = new CrowdingDistanceComparator();
front.solutionList_.Sort(comp.Compare);
for (int k = 0; k < remain; k++)
{
population.@add(front[k]);
}
// for
remain = 0;
}
// if
}
// while
// Return as output parameter the required evaluations
outputParameters_["evaluations"] = evaluations;
// Return the first non-dominated front
Ranking ranking2 = new Ranking(population);
//Console.WriteLine(slnLst[0].objective_.GetValue(0));
//File.WriteAllLines("C:/text.txt",slnLst.ConvertAll(Convert.ToString));
return(ranking2.getSubfront(0));
}
