public static void ReportSolution(int evaluation, Solution sol, BackgroundWorker worker)
{
var progress = new UserProgress
{
EvolutionStep = evaluation
};
var list = ObjectiveMapping.GetList(_model.ProblemType);
var objindex = 0;
if (list[0])
{
progress.FeatureP = sol.Objective[objindex];
objindex++;
}
if (list[1])
{
progress.InteracP = sol.Objective[objindex];
objindex++;
}
if (list[2])
{
progress.VariantP = sol.Objective[objindex];
}
worker.ReportProgress((int)100.0 * evaluation / _model.Setting.MaxEvaluations, progress);
}
/// <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;
JMetalCSharp.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);
parallelEvaluator.StartParallelRunner(Problem);;
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = Operators["mutation"];
crossoverOperator = Operators["crossover"];
selectionOperator = Operators["selection"];
// Create the initial solutionSet
IntergenSolution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new IntergenSolution((IntergenProblem)Problem);
parallelEvaluator.AddTaskForExecution(new object[] { newSolution, i });;
}
List <IntergenSolution> solutionList = (List <IntergenSolution>)parallelEvaluator.ParallelExecution();
foreach (IntergenSolution solution in solutionList)
{
population.Add(solution);
evaluations++;
}
// Generations
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
IntergenSolution[] parents = new IntergenSolution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
if (evaluations < maxEvaluations)
{
//obtain parents
parents[0] = (IntergenSolution)selectionOperator.Execute(population);
parents[1] = (IntergenSolution)selectionOperator.Execute(population);
IntergenSolution[] offSpring = (IntergenSolution[])crossoverOperator.Execute(parents);
mutationOperator.Execute(offSpring[0]);
mutationOperator.Execute(offSpring[1]);
parallelEvaluator.AddTaskForExecution(new object[] { offSpring[0], evaluations + i });
parallelEvaluator.AddTaskForExecution(new object[] { offSpring[1], evaluations + i });
}
}
List <IntergenSolution> solutions = (List <IntergenSolution>)parallelEvaluator.ParallelExecution();
foreach (IntergenSolution solution in solutions)
{
offspringPopulation.Add(solution);
evaluations++;
//solution.FoundAtEval = evaluations;
}
// 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;
}
}
//TODO
/*
* Ranking rank2 = new Ranking(population);
*
* Result = rank2.GetSubfront(0);
*/
/*Ranking forGraphicOutput = new Ranking(population);
* var currentBestResultSet = forGraphicOutput.GetSubfront(0);
*
* var firstBestResult = currentBestResultSet.Get(0);
*
* var myProblem = (IntergenProblem)Problem;
* //myProblem.calculated;
*
* //var variantValuesWithoutInteraction = Matrix.Multiply(myProblem.calculated, firstBestResult.);
* //var Model = myProblem.GetModel();
* int mycounter = 0;
* if (mycounter % 500 == 0)
* {
* //RIntegrator.PlotValues(currentBestResultSet, myProblem);
* }
* mycounter++;
* var progress = new UserProgress();
* progress.FeatureP = firstBestResult.Objective[0];
* if (!myProblem.Model.Setting.NoVariantCalculation) progress.VariantP = firstBestResult.Objective[1];
* myProblem.Worker.ReportProgress(evaluations * 100 / maxEvaluations, progress); ;
*
* //Model.CurrentBestImage = "CurrentBest.png"; */
front = ranking.GetSubfront(0);
var minmax = new MinMaxFitness
{
FeatMax = double.MinValue,
FeatMin = double.MaxValue,
VarMax = double.MinValue,
VarMin = double.MaxValue,
InterMax = double.MinValue,
InterMin = double.MaxValue
};
var prob = (IntergenProblem)Problem;
var list = ObjectiveMapping.GetList(prob.ProblemType);
for (var i = 0; i < populationSize; i++)
{
var sol = population.Get(i);
var objindex = 0;
if (list[0])
{
if (sol.Objective[objindex] < minmax.FeatMin)
{
minmax.FeatMin = sol.Objective[objindex];
}
if (sol.Objective[objindex] > minmax.FeatMax)
{
minmax.FeatMax = sol.Objective[objindex];
}
objindex++;
}
if (list[1])
{
if (sol.Objective[objindex] < minmax.InterMin)
{
minmax.InterMin = sol.Objective[objindex];
}
if (sol.Objective[objindex] > minmax.InterMax)
{
minmax.InterMax = sol.Objective[objindex];
}
objindex++;
}
if (list[2])
{
if (sol.Objective[objindex] < minmax.VarMin)
{
minmax.VarMin = sol.Objective[objindex];
}
if (sol.Objective[objindex] > minmax.VarMax)
{
minmax.VarMax = sol.Objective[objindex];
}
}
}
var sol0 = front.Best(new CrowdingDistanceComparator());
var done = FitnessTracker.AddFitn(minmax);
SolutionPlotter.Plot(sol0);
ProgressReporter.ReportSolution(evaluations, sol0, _worker);
if (done)
{
Ranking rank3 = new Ranking(population);
Result = rank3.GetSubfront(0);
SetOutputParameter("evaluations", evaluations);
return(this.Result);
}
}
// Return as output parameter the required evaluations
SetOutputParameter("evaluations", evaluations);
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(this.Result);
}
