/// <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 <double> lowerLimits, List <double> 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));
}
//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;
}
//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);
}
