public override object Execute(object obj)
{
SolutionSet population = (SolutionSet) obj;
int populationSize = (int) Parameters["populationSize"];
SolutionSet result = new SolutionSet(populationSize);
//->Ranking the union
Ranking ranking = new Ranking(population);
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()))
{
//Asign crowding distance to individuals
Distance.CrowdingDistanceAssignment(front, _problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
result.Add(front[k]);
}
//Decrement remaint
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 containt individuals to insert
Distance.CrowdingDistanceAssignment(front, _problem.NumberOfObjectives);
front.Sort(CrowdingComparator);
for (int k = 0; k < remain; k++)
{
result.Add(front[k]);
}
}
return result;
}
// NSGAII
public override SolutionSet Execute()
{
int populationSize;
int maxEvaluations;
QualityIndicator indicators;
Operator mutationOperator;
Operator crossoverOperator;
Operator selectionOperator;
object parameter;
if (InputParameters.TryGetValue("populationSize", out parameter))
{
populationSize = (int) parameter;
}
else
{
throw new Exception("populationSize does not exist");
}
if (InputParameters.TryGetValue("maxEvaluations", out parameter))
{
maxEvaluations = (int) parameter;
}
else
{
throw new Exception("maxEvaluations does not exist");
}
if (InputParameters.TryGetValue("indicators", out parameter))
{
indicators = (QualityIndicator) parameter;
}
else
{
throw new Exception("maxEvaluations does not exist");
}
// Initializing variables
var population = new SolutionSet(populationSize);
var evaluations = 0;
int requiredEvaluations = 0;
Operator unknownIOperator;
//Read the operators
if (UsedOperators.TryGetValue("mutation", out unknownIOperator))
{
mutationOperator = unknownIOperator;
}
else
{
throw new Exception("mutation does not exist");
}
if (UsedOperators.TryGetValue("crossover", out unknownIOperator))
{
crossoverOperator = unknownIOperator;
}
else
{
throw new Exception("crossover does not exist");
}
if (UsedOperators.TryGetValue("selection", out unknownIOperator))
{
selectionOperator = unknownIOperator;
}
else
{
throw new Exception("selection does not exist");
}
// Create the initial solutionSet
for (int i = 0; i < populationSize; i++)
{
var newSolution = new Solution(Problema);
Problema.Evaluate(newSolution);
Problema.EvaluateConstraints(newSolution);
evaluations++;
population.Add(newSolution);
} //for
// Generations
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
var offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize/2); i++)
{
if (evaluations < maxEvaluations)
{
//obtain parents
parents[0] = (Solution) selectionOperator.Execute(population);
parents[1] = (Solution) selectionOperator.Execute(population);
Solution[] offSpring = (Solution[]) crossoverOperator.Execute(parents);
mutationOperator.Execute(offSpring[0]);
mutationOperator.Execute(offSpring[1]);
Problema.Evaluate(offSpring[0]);
Problema.EvaluateConstraints(offSpring[0]);
Problema.Evaluate(offSpring[1]);
Problema.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
} // if
} // for
System.Console.WriteLine("evaluation #: " + evaluations);
// Create the solutionSet union of solutionSet and offSpring
SolutionSet union = population.Union(offspringPopulation);
// Ranking the union
Ranking localRanking = new Ranking(union);
int remain = populationSize;
int index = 0;
SolutionSet front = null;
population.Clear();
// Obtain the next front
front = localRanking.GetSubfront(index);
while ((remain > 0) && (remain >= front.Size()))
{
//Assign crowding distance to individuals
Distance.CrowdingDistanceAssignment(front, Problema.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 = localRanking.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, Problema.NumberOfObjectives);
front.Sort(new CrowdingComparator());
for (int k = 0; k < remain; k++)
{
population.Add(front[k]);
} // for
remain = 0;
} // if
// 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.GetTrueParetoFrontHypervolume()))
{
requiredEvaluations = evaluations;
} // if
} // if
} // while
// Return as output parameter the required evaluations
OutputParameters["evaluations"] = requiredEvaluations;
// Return the first non-dominated front
Ranking ranking = new Ranking(population);
ranking.GetSubfront(0).PrintFeasibleFUN("FUN_NSGAII");
return ranking.GetSubfront(0);
}
