/// <summary>
/// Adds a solution to be evaluated to a list of tasks
/// </summary>
/// <param name="taskParameters"></param>
public void AddTaskForExecution(object[] taskParameters)
{
IntergenSolution solution = (IntergenSolution)taskParameters[0];
solution.FoundAtEval = counter;
counter++;
if (taskList == null)
{
taskList = new List <Task <IntergenSolution> >();
}
taskList.Add(new Task <IntergenSolution>(() =>
{
problem.Evaluate(solution);
problem.EvaluateConstraints(solution);
return(solution);
}));
}
/// <summary>
/// Evaluates a list of solutions
/// </summary>
/// <returns>A list with the evaluated solutions</returns>
public object ParallelExecution()
{
try
{
foreach (var task in taskList)
{
task.Start();
}
}
catch (Exception ex)
{
Logger.Log.Error("Error in MultithreadedEvaluator.ParallelExecution", ex);
Console.Error.WriteLine(ex.StackTrace);
}
Task.WaitAll(taskList.ToArray());
List <IntergenSolution> solutionList = new List <IntergenSolution>();
foreach (Task <IntergenSolution> task in taskList)
{
IntergenSolution solution = null;
try
{
solution = task.Result;
solutionList.Add(solution);
}
catch (Exception ex)
{
Logger.Log.Error("Error in MultithreadedEvaluator.ParallelExecution", ex);
Console.Error.WriteLine(ex.StackTrace);
}
}
taskList = null;
return(solutionList);
}
private IntergenSolution[] DoCrossover(double probability, IntergenSolution parent1, IntergenSolution parent2)
{
IntergenSolution[] offSpring = new IntergenSolution[2];
offSpring[0] = new IntergenSolution(parent1);
offSpring[1] = new IntergenSolution(parent2);
int i;
double rand;
double y1, y2, yL, yu;
double c1, c2;
double alpha, beta, betaq;
double valueX1, valueX2;
XReal x1 = new XReal(parent1);
XReal x2 = new XReal(parent2);
XReal offs1 = new XReal(offSpring[0]);
XReal offs2 = new XReal(offSpring[1]);
int numberOfVariables = x1.GetNumberOfDecisionVariables();
if (JMetalRandom.NextDouble() <= probability)
{
for (i = 0; i < numberOfVariables; i++)
{
valueX1 = x1.GetValue(i);
valueX2 = x2.GetValue(i);
if (JMetalRandom.NextDouble() <= 0.5)
{
if (Math.Abs(valueX1 - valueX2) > EPS)
{
if (valueX1 < valueX2)
{
y1 = valueX1;
y2 = valueX2;
}
else
{
y1 = valueX2;
y2 = valueX1;
}
yL = x1.GetLowerBound(i);
yu = x1.GetUpperBound(i);
rand = JMetalRandom.NextDouble();
beta = 1.0 + (2.0 * (y1 - yL) / (y2 - y1));
//Console.WriteLine("Beta1" + beta);
alpha = 2.0 - Math.Pow(beta, -(distributionIndex + 1.0));
if (rand <= (1.0 / alpha))
{
betaq = Math.Pow((rand * alpha), (1.0 / (distributionIndex + 1.0)));
/* if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* } */
}
else
{
betaq = Math.Pow((1.0 / (2.0 - rand * alpha)), (1.0 / (distributionIndex + 1.0)));
/* var betax = Math.Pow((1.0 / (2.0 - rand * alpha)), (1.0 / (distributionIndex + 1.0)));
* if (double.IsNaN(betax))
* {
* Console.WriteLine("NAN BETAx");
* }
* if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* }
*/
}
/*
* if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* } */
c1 = 0.5 * ((y1 + y2) - betaq * (y2 - y1));
beta = 1.0 + (2.0 * (yu - y2) / (y2 - y1));
alpha = 2.0 - Math.Pow(beta, -(distributionIndex + 1.0));
if (rand <= (1.0 / alpha))
{
betaq = Math.Pow((rand * alpha), (1.0 / (distributionIndex + 1.0)));
/*if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* } */
}
else
{
betaq = Math.Pow((1.0 / (2.0 - rand * alpha)), (1.0 / (distributionIndex + 1.0)));
/*if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* }*/
}
c2 = 0.5 * ((y1 + y2) + betaq * (y2 - y1));
if (c1 < yL)
{
c1 = yL;
}
if (c2 < yL)
{
c2 = yL;
}
if (c1 > yu)
{
c1 = yu;
}
if (c2 > yu)
{
c2 = yu;
}
/*if (double.IsNaN(c2) || double.IsNaN(c1))
* {
* Console.WriteLine("Setting NAN");
* } */
if (JMetalRandom.NextDouble() <= 0.5)
{
offs1.SetValue(i, c2);
offs2.SetValue(i, c1);
}
else
{
offs1.SetValue(i, c1);
offs2.SetValue(i, c2);
}
}
else
{
offs1.SetValue(i, valueX1);
offs2.SetValue(i, valueX2);
}
}
else
{
offs1.SetValue(i, valueX2);
offs2.SetValue(i, valueX1);
}
}
}
/*
* var o1 = offSpring[0];
* XReal values = new XReal(o1);
* for (int u = 0; u < values.Size(); u++)
* {
*
* if (double.IsNaN(values.GetValue(u)))
* {
*
* Console.WriteLine("crossover NAN1");
* }
* }
*
* var s = offSpring[1];
* XReal values2 = new XReal(s);
* for (int u = 0; u < values2.Size(); u++)
* {
* if (double.IsNaN(values2.GetValue(u)))
* {
* Console.WriteLine("crossover NAN2");
* }
* } */
return(offSpring);
}
/// <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;
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);
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = Operators["mutation"];
crossoverOperator = Operators["crossover"];
selectionOperator = Operators["selection"];
var plotCounter = 0;
var plotModulo = 4;
Random random = new Random(2);
JMetalRandom.SetRandom(random);
// Create the initial solutionSet
IntergenSolution newSolution;
for (int i = 0; i < populationSize; i++)
{
//var test = (IntergenProblem) Problem;
newSolution = new IntergenSolution((IntergenProblem)Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
population.Add(newSolution);
}
// 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]);
Problem.Evaluate(offSpring[0]);
Problem.EvaluateConstraints(offSpring[0]);
Problem.Evaluate(offSpring[1]);
Problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
}
}
// 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;
}
}
var sol0 = front.Best(new CrowdingComparator());
//if (plotCounter%plotModulo == 0)
SolutionPlotter.Plot(sol0);
ProgressReporter.ReportSolution(evaluations, sol0, _worker);
}
// Return as output parameter the required evaluations
SetOutputParameter("evaluations", requiredEvaluations);
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}
