protected int[] AntActivity(double[,] pheromone, double[,] heuristic)
{
int[] tour = new int[tourLength];
bool[] visited = new bool[tourLength];
int selectedNeighbor;
double[] probabilities;
List <int> neighbors;
double denominator;
// Select the initial vertex randomly.
tour[0] = Statistics.RandomDiscreteUniform(0, tourLength - 1);
visited[tour[0]] = true;
// Complete the rest of the tour.
for (int i = 1; i < tourLength; i++)
{
neighbors = FactibleNeighbors(tour[i - 1], visited);
denominator = 0;
for (int j = 0; j < neighbors.Count; j++)
{
denominator += (Math.Pow(pheromone[tour[i - 1], neighbors[j]], alpha) *
Math.Pow(heuristic[tour[i - 1], neighbors[j]], beta));
}
probabilities = new double[neighbors.Count];
for (int j = 0; j < neighbors.Count; j++)
{
probabilities[j] = (Math.Pow(pheromone[tour[i - 1], neighbors[j]], alpha) *
Math.Pow(heuristic[tour[i - 1], neighbors[j]], beta)) / denominator;
}
selectedNeighbor = neighbors[Statistics.SampleRoulette(probabilities)];
visited[selectedNeighbor] = true;
tour[i] = selectedNeighbor;
}
return(tour);
}
public void Run(int timeLimit)
{
int startTime = Environment.TickCount;
int iterationStartTime = 0;
int iterationTime = 0;
int maxIterationTime = 0;
int numVariables = LowerBounds.Length;
int selectedSize = Math.Max(1, (int)Math.Round(TruncationFactor * PopulationSize));
int[][] population = new int[PopulationSize][];
double[] evaluation = new double[PopulationSize];
double[][] model = new double[numVariables][];
for (int i = 0; i < numVariables; i++)
{
model[i] = new double[(UpperBounds[i] - LowerBounds[i]) + 1];
}
// Generate the initial random population.
for (int k = 0; k < PopulationSize; k++)
{
population[k] = new int[numVariables];
for (int i = 0; i < numVariables; i++)
{
population[k][i] = Statistics.RandomDiscreteUniform(LowerBounds[i], UpperBounds[i]);
}
}
BestIndividual = null;
maxIterationTime = Environment.TickCount - startTime;
while (Environment.TickCount - startTime < timeLimit - maxIterationTime)
{
iterationStartTime = Environment.TickCount;
// Handle constraints using a repairing method.
if (RepairEnabled)
{
for (int k = 0; k < PopulationSize; k++)
{
Repair(population[k]);
}
}
// Run a local search method for each individual in the population.
if (LocalSearchEnabled)
{
for (int k = 0; k < PopulationSize; k++)
{
LocalSearch(population[k]);
}
}
// Evaluate the population.
for (int k = 0; k < PopulationSize; k++)
{
evaluation[k] = Fitness(population[k]);
}
// Apply the selection method.
Array.Sort(evaluation, population);
if (BestIndividual == null || evaluation[0] < BestFitness)
{
BestIndividual = population[0];
BestFitness = evaluation[0];
}
// Learn the probabilistic model from the selected population.
for (int i = 0; i < numVariables; i++)
{
// Set the counters to zero.
for (int k = 0; k <= UpperBounds[i] - LowerBounds[i]; k++)
{
model[i][k] = 0;
}
// Count the values of the variable.
for (int k = 0; k < selectedSize; k++)
{
model[i][population[k][i] - LowerBounds[i]] += 1;
}
// Calculate the frequency of each value of the variable.
for (int k = 0; k <= UpperBounds[i] - LowerBounds[i]; k++)
{
model[i][k] /= selectedSize;
}
}
// Sample the population for the next generation.
for (int k = 0; k < PopulationSize; k++)
{
population[k] = new int[numVariables];
for (int i = 0; i < numVariables; i++)
{
population[k][i] = LowerBounds[i] + Statistics.SampleRoulette(model[i]);
}
}
iterationTime = Environment.TickCount - iterationStartTime;
maxIterationTime = (maxIterationTime < iterationTime) ? iterationTime : maxIterationTime;
}
}
