/// <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>
/// 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>
/// Sorts the combination of parents and child population set based on Pareto Fron Ranking.
/// </summary>
/// <param name="population">Parent solution set.</param>
/// <param name="offspringPop">Offspring solution set.</param>
/// <param name="lowerLimits">List of lower limits.</param>
/// <param name="upperLimits">List of upper limits.</param>
/// <returns></returns>
public static List <List <double> > Sorting(List <List <double> > populationList, List <List <double> > offspringList, List <int> lowerLimits, List <int> upperLimits)
{
int numVar = lowerLimits.Count;
int numObj = populationList.Count - numVar;
Algorithm algorithm = CreateAlgorithm(numObj, lowerLimits, upperLimits);
//change solutions list to solutionSet
SolutionSet population = SolutionListToPop(populationList, algorithm, numVar);
SolutionSet offspringPop = SolutionListToPop(offspringList, algorithm, numVar);
// Creating the solutionSet union of solutionSet and offSpring
SolutionSet union = ((SolutionSet)population).union(offspringPop);
// Ranking the union
Ranking ranking = new Ranking(union);
int remain = population.size();
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, algorithm.problem_.numberOfObjectives_);
//Add the individuals of this front
for (int k = 0; k < front.size(); k++)
{
population.@add(front[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, algorithm.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;
}
List <List <double> > pList = PopToSolutionList(population);
return(PopToSolutionList(population));
}
// 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);
}
// 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));
}