static void Main(string[] args)
{
TravellingSalesmanMap TSP = new TravellingSalesmanMap(TravellingSalesmanMap.MapInstance.RAT99);
RunACO(TSP);
//RunPSO(TSP);
}
public PSO(TravellingSalesmanMap TSP, int particleCount, int? maxSteps = null, double? targetTravelDistance = null)
{
this.TSP = TSP;
this.Swarm = new TSPParticleSwarm(TSP, particleCount);
this.MaxSteps = maxSteps;
this.TargetTravelDistance = targetTravelDistance;
}
//Ha alguma garantia de que essas particulas aleatorias sejam circuitos hamiltonianos?
public static TSPParticle RandomGenerate(TSPParticleSwarm containingSwarm, TravellingSalesmanMap Map)
{
TSPParticle newParticle = new TSPParticle(containingSwarm, Map);
newParticle.Velocity = TSPVelocity.RandomGenerate(Map);
newParticle.Position = new TSPPosition(Aleatoriety.GetRandomIntegerSequencePermutation(1, Map.CityCount).ToList());
newParticle.EvaluateSelf();
return newParticle;
}
public AntProblem(TravellingSalesmanMap TSP)
: base(TSP.map)
{
PheromoneMatrix = new double[TSP.CityCount, TSP.CityCount];
for (int p1 = 0; p1 < TSP.CityCount; p1++)
for (int p2 = p1; p2 < TSP.CityCount; p2++)
PheromoneMatrix[p1, p2] = 1;
pheromoneWeight = 1;
distanceWeight = 5;
}
//construtor do ACO
//recebe os parâmetros a serem utilizados nas funções de movimento da formiga e de atualização de feromônio
public ACO(TravellingSalesmanMap TSP, int numAnts)
{
//inicializar variáveis
problem = new AntProblem(TSP);
bestSolutionFound = new TSPSolution();
taxaEvaporacao = 0.9;
//cria as formigas e coloca cada uma em um vértice aleatório do grafo
System.Random random = new Random();
ants = new List<Ant>();
for (int i = 0; i < numAnts; i++)
//ants.Add(new Ant(random.Next(problem.CityCount) + 1));
ants.Add(new Ant((i + 1)%(TSP.CityCount + 1)));
}
static void RunPSO(TravellingSalesmanMap TSP)
{
//cria novo PSO
PSO pso = new PSO(TSP, 100, 10000, TSP.OptimalTravelDistance);
//roda o PSO
TSPSolution s = (TSPSolution) pso.Run();
//imprime a solução
Console.WriteLine("Optimal Solution: " + TSP.OptimalTravelDistance);
Console.WriteLine("Solution Found: " + s.Fitness);
Console.ReadKey();
}
static void RunACO(TravellingSalesmanMap TSP)
{
//cria novo ACO
ACO aco = new ACO(TSP, TSP.CityCount);
//roda o ACO
TSPSolution s = aco.Run();
//imprime a solução
Console.WriteLine("Optimal Solution: " + TSP.OptimalTravelDistance);
Console.WriteLine("Solution Found: " + s.Fitness);
Console.Write("\nSteps: ");
foreach(int visitedCity in s.TravelPlan)
Console.Write(TSP.GetCityAlias(visitedCity) + " => ");
Console.Write(TSP.GetCityAlias(s.TravelPlan.First()));
Console.ReadKey();
}
protected TravellingSalesmanMap Map; // { get; set; }
#endregion Fields
#region Constructors
//Referencia a melhor particula atualmente
//public TSPParticle gBest;
/*
public TSPParticle(TSPParticle particle) : base() {
this.Position = particle.Position;
this.Velocity = particle.Velocity;
this.Fitness = particle.Fitness;
}
private TSPParticle() : base() {
}
*/
private TSPParticle(ParticleSwarm containingSwarm, TravellingSalesmanMap Map)
: base(containingSwarm)
{
this.Map = Map;
}
public TSPParticleSwarm(TravellingSalesmanMap TSP, int particleCount)
: base(particleCount)
{
this.Map = TSP;
}
public TSPSolution(List<int> steps, TravellingSalesmanMap problema)
{
this.TravelPlan = steps;
problem = problema;
Evaluate();
}
public TSPSolution(List <int> steps, TravellingSalesmanMap problema)
{
this.TravelPlan = steps;
problem = problema;
Evaluate();
}
public static TSPVelocity RandomGenerate(TravellingSalesmanMap Map)
{
// 0.0 <= Valor < 1.0
double speed = Aleatoriety.GetRandomDouble();
int permutationsCount = (int)(speed * (Map.CityCount / 2)) + 1;
List<Tuple<int, int>> permutations = new List<Tuple<int,int>>();
for(int i = 0; i < permutationsCount; ++i) {
Tuple<int, int> permutation = Aleatoriety.GetTwoDiferentValuesInRange(0, Map.CityCount);
permutations.Add(permutation);
}
return new TSPVelocity(permutations);
}
