/// <summary>
/// Execute the algorithm
/// </summary>
/// <returns></returns>
public override SolutionSet Execute()
{
int populationSize = -1,
archiveSize = -1,
maxEvaluations = -1,
evaluations = -1,
feedBack = -1;
Operator mutationOperator, crossoverOperator, selectionOperator;
SolutionSet currentPopulation;
CrowdingArchive archive;
SolutionSet[] neighbors;
Neighborhood neighborhood;
IComparer <Solution> dominance = new DominanceComparator();
IComparer <Solution> crowdingComparator = new CrowdingComparator();
Distance distance = new Distance();
//Read the parameters
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "archiveSize", ref archiveSize);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "feedBack", ref feedBack);
//Read the operators
mutationOperator = Operators["mutation"];
crossoverOperator = Operators["crossover"];
selectionOperator = Operators["selection"];
//Initialize the variables
//Initialize the population and the archive
currentPopulation = new SolutionSet(populationSize);
archive = new CrowdingArchive(archiveSize, this.Problem.NumberOfObjectives);
evaluations = 0;
neighborhood = new Neighborhood(populationSize);
neighbors = new SolutionSet[populationSize];
//Create the comparator for check dominance
dominance = new DominanceComparator();
//Create the initial population
for (int i = 0; i < populationSize; i++)
{
Solution individual = new Solution(this.Problem);
this.Problem.Evaluate(individual);
this.Problem.EvaluateConstraints(individual);
currentPopulation.Add(individual);
individual.Location = i;
evaluations++;
}
while (evaluations < maxEvaluations)
{
for (int ind = 0; ind < currentPopulation.Size(); ind++)
{
Solution individual = new Solution(currentPopulation.Get(ind));
Solution[] parents = new Solution[2];
Solution[] offSpring;
neighbors[ind] = neighborhood.GetEightNeighbors(currentPopulation, ind);
neighbors[ind].Add(individual);
//parents
parents[0] = (Solution)selectionOperator.Execute(neighbors[ind]);
parents[1] = (Solution)selectionOperator.Execute(neighbors[ind]);
//Create a new individual, using genetic operators mutation and crossover
offSpring = (Solution[])crossoverOperator.Execute(parents);
mutationOperator.Execute(offSpring[0]);
//->Evaluate individual an his constraints
this.Problem.Evaluate(offSpring[0]);
this.Problem.EvaluateConstraints(offSpring[0]);
evaluations++;
//<-Individual evaluated
int flag = dominance.Compare(individual, offSpring[0]);
if (flag == 1)
{ //The new individuals dominate
offSpring[0].Location = individual.Location;
currentPopulation.Replace(offSpring[0].Location, offSpring[0]);
archive.Add(new Solution(offSpring[0]));
}
else if (flag == 0)
{ //The individuals are non-dominates
neighbors[ind].Add(offSpring[0]);
offSpring[0].Location = -1;
Ranking rank = new Ranking(neighbors[ind]);
for (int j = 0; j < rank.GetNumberOfSubfronts(); j++)
{
distance.CrowdingDistanceAssignment(rank.GetSubfront(j), this.Problem.NumberOfObjectives);
}
Solution worst = neighbors[ind].Worst(crowdingComparator);
if (worst.Location == -1)
{ //The worst is the offspring
archive.Add(new Solution(offSpring[0]));
}
else
{
offSpring[0].Location = worst.Location;
currentPopulation.Replace(offSpring[0].Location, offSpring[0]);
archive.Add(new Solution(offSpring[0]));
}
}
}
//Store a portion of the archive into the population
distance.CrowdingDistanceAssignment(archive, this.Problem.NumberOfObjectives);
for (int j = 0; j < feedBack; j++)
{
if (archive.Size() > j)
{
int r = JMetalRandom.Next(0, currentPopulation.Size() - 1);
if (r < currentPopulation.Size())
{
Solution individual = archive.Get(j);
individual.Location = r;
currentPopulation.Replace(r, new Solution(individual));
}
}
}
}
Result = archive;
return(archive);
}
/// <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);
}