public override void Ejecutar(Knapsack theProblem, Random myRandom)
{
EFOs = 0;
var population = new List <PSOSolution>();
for (var i = 0; i < PopulationSize; i++)
{
var s = new PSOSolution(theProblem, this);
s.RandomInitialization(myRandom);
population.Add(s);
}
population.Sort((x, y) => - 1 * x.Fitness.CompareTo(y.Fitness));
BestSolution = new PSOSolution(population[0]);
if (population[0].IsOptimalKnown())
{
return;
}
for (var g = 1; g < MaxGenerations; g++)
{
for (var i = 0; i < PopulationSize; i++)
{
population[i].UpdateVelocity((PSOSolution)BestSolution, W, C1, C2, myRandom);
population[i].UpdatePosition(myRandom);
population[i].Repare(myRandom);
population[i].Complete(myRandom, new List <int>());
population[i].Evaluate();
population[i].UpdateHistory();
}
population.Sort((x, y) => - 1 * x.Fitness.CompareTo(y.Fitness));
population[0].LocalOptimizer(LocalIterations, myRandom);
if (BestSolution.Fitness < population[0].Fitness)
{
BestSolution = new PSOSolution(population[0]);
}
if (EFOs >= MaxEFOs)
{
break;
}
if (population[0].IsOptimalKnown())
{
break;
}
}
}
public PSOSolution(PSOSolution original) : base(original)
{
LocalBestObjects = new int[MyProblem.TotalItems];
for (var i = 0; i < MyProblem.TotalItems; i++)
{
LocalBestObjects[i] = original.LocalBestObjects[i];
}
LocalBestFitness = original.LocalBestFitness;
Velocity = new double[MyProblem.TotalItems];
for (var i = 0; i < MyProblem.TotalItems; i++)
{
Velocity[i] = original.Velocity[i];
}
}
public void UpdateVelocity(PSOSolution bestSolution, double w,
double c1, double c2, Random myRandom)
{
for (var i = 0; i < MyProblem.TotalItems; i++)
{
Velocity[i] = w * Velocity[i] +
c1 * myRandom.NextDouble() * (LocalBestObjects[i] - Objects[i]) +
c2 * myRandom.NextDouble() * (bestSolution.Objects[i] - Objects[i]);
if (Velocity[i] > 4)
{
Velocity[i] = 4;
}
if (Velocity[i] < -4)
{
Velocity[i] = -4;
}
}
}