public override void Execute(OSSP theOSSP, Random theAleatory)
{
MyOSSP = theOSSP;
MyAleatory = theAleatory;
CurrentEFOs = 0;
Curve = new List <double>();
/*Generar e inicializar gatos*/
for (var i = 0; i < SwarmSize; i++)
{
var newCat = new Cat(this);
newCat.RandomInitialization();
Swarm.Add(newCat);
if (Math.Abs(newCat.Fitness - MyOSSP.OptimalKnown) < 1e-10)
{
break;
}
}
DistributeCats();
/*Escoger el mejor del enjambre*/
var maxFitness = Swarm.Min(x => x.Fitness);
var best = Swarm.Find(x => Math.Abs(x.Fitness - maxFitness) < 1e-10);
MyBestSolution = new Cat(best);
while (CurrentEFOs < MaxEFOs && Math.Abs(MyBestSolution.Fitness - MyOSSP.OptimalKnown) > 1e-10)
{
for (var i = 0; i < SwarmSize; i++)
{
if (!Swarm[i].TMFlag)
{
Swarm[i] = new Cat(SeekingMode(Swarm[i]));
}
else
{
Swarm[i] = new Cat(TracingMode(Swarm[i]));
}
}
/*Actualizar el mejor*/
maxFitness = Swarm.Min(x => x.Fitness);
best = Swarm.Find(x => Math.Abs(x.Fitness - maxFitness) < 1e-10);
if (maxFitness > MyBestSolution.Fitness)
{
MyBestSolution = new Cat(best);
}
/*Redistribuir los gatos*/
DistributeCats();
}
}
public override void Execute(OSSP theOSSP, Random theAleatory)
{
MyOSSP = theOSSP;
MyAleatory = theAleatory;
CurrentEFOs = 0;
Curve = new List <double>();
var swarm = new List <PSOSolution>();
for (var i = 0; i < SwarmSize; i++)
{
var newParticle = new PSOSolution(this);
newParticle.RandomInitialization();
swarm.Add(newParticle);
if (Math.Abs(newParticle.Fitness - MyOSSP.OptimalKnown) < 1e-10)
{
break;
}
}
var maxFitness = swarm.Min(x => x.Fitness);
var best = swarm.Find(x => Math.Abs(x.Fitness - maxFitness) < 1e-10);
MyBestSolution = new PSOSolution(best);
while (CurrentEFOs < MaxEFOs && Math.Abs(MyBestSolution.Fitness - MyOSSP.OptimalKnown) > 1e-10)
{
for (var i = 0; i < SwarmSize; i++)
{
swarm[i].UpdateVelocity((PSOSolution)MyBestSolution);
}
for (var i = 0; i < SwarmSize; i++)
{
swarm[i].UpdatePosition();
if (Math.Abs(swarm[i].Fitness - MyOSSP.OptimalKnown) < 1e-10)
{
break;
}
}
maxFitness = swarm.Min(x => x.Fitness);
best = swarm.Find(x => Math.Abs(x.Fitness - maxFitness) < 1e-10);
if (maxFitness > MyBestSolution.Fitness)
{
MyBestSolution = new PSOSolution(best);
}
}
}
public abstract void Execute(OSSP theOSSP, Random theAleatory);
// public override void Execute(OSSP theTsp, Random theAleatory)
// {
// // MyTsp = theTsp;
// MyAleatory = theAleatory;
// CurrentEFOs = 0;
// var s = new Solution(this);
// s.Evaluate();
// MyBestSolution = new Solution(s);
// for (var i = 1; i < Math.Pow(2, theTsp.TotalNodes); i++)
// {
// // ---
// if (s.Fitness > MyBestSolution.Fitness)
// MyBestSolution = new Solution(s);
// }
// }
// public override string ToString()
// {
// return "Exhaustive";
// }
public override void Execute(OSSP theOSSP, Random theAleatory)
{
throw new NotImplementedException();
}
public override void Execute(OSSP theOSSP, Random theAleatory)
{
MyOSSP = theOSSP;
MyAleatory = theAleatory;
CurrentEFOs = 0;
Curve = new List <double>();
var population = new List <Chromosome>();
for (var i = 0; i < PopulationSize; i++)
{
var s = new Chromosome(this);
s.RandomInitialization();
population.Add(s);
if (Math.Abs(s.Fitness - MyOSSP.OptimalKnown) < 1e-10)
{
break;
}
}
population.Sort((x, y) => x.Fitness.CompareTo(y.Fitness));
Curve.Add(population[0].Fitness);
while (CurrentEFOs < MaxEFOs && Math.Abs(population[0].Fitness - MyOSSP.OptimalKnown) > 1e-10)
{
var alloffspring = new List <Chromosome>();
for (var h = 0; h < PopulationSize / 2; h++)
{
// Parents Select
int p1;
int p2;
do
{
p1 = Tournament(population, TournamentSize);
p2 = Tournament(population, TournamentSize);
} while (p1 == p2);
// Generate Offspring (Crossover)
var offsoring = Croosover(population[p1], population[p2]);
// Mutate Offspring
offsoring[0].Tweak();
offsoring[1].Tweak();
alloffspring.AddRange(offsoring);
if (CurrentEFOs > MaxEFOs)
{
break;
}
}
// Replace - Define the new population
population.AddRange(alloffspring);
population.Sort((x, y) => x.Fitness.CompareTo(y.Fitness));
population.RemoveRange(PopulationSize, alloffspring.Count);
}
MyBestSolution = population[0];
}
