public HybridACOGA(int numberOfVariables, OptimizationType opType, GA <int> .ObjectiveFunctionDelegate objectFunction, double[,] distance) : base(numberOfVariables, opType, objectFunction)
{
getObjectiveValue = objectFunction;
numberOfCity = numberOfVariables;
FromToDistanceMatrix = distance;
// Allocate memory for pheromone matrix and heuristic value matrix
// Heuristic values in the matrix are computed and assigned via
// the specified function delegate
pheromone = new double[numberOfCity, numberOfCity];
indicesOfAnts = new int[populationSize];
solutions = new int[populationSize][];
soFarTheBestSoluiton = new int[numberOfCity];
for (int r = 0; r < numberOfCity; r++)
{
for (int c = 0; c < numberOfCity; c++)
{
pheromone[r, c] = 0.01;
}
}
for (int r = 0; r < numberOfCity; r++)
{
for (int c = 0; c < numberOfCity; c++)
{
AverageDistance += FromToDistanceMatrix[r, c];
}
}
AverageDistance = AverageDistance / (numberOfCity * numberOfCity);
heuristicValues = new double[numberOfCity, numberOfCity];
for (int r = 0; r < numberOfCity; r++)
{
for (int c = 0; c < numberOfCity; c++)
{
heuristicValues[r, c] = DistanceInverseHeuristic(r, c);
}
}
for (int i = 0; i < populationSize; i++)
{
indicesOfAnts[i] = i;
solutions[i] = new int[numberOfCity];
for (int j = 0; j < numberOfCity; j++)
{
solutions[i][j] = j;
}
KnuthShuffle(solutions[i]);
}
// Allocate memory for the arries used in ACO, whose length is
// depend on the number of variables. In contrast, those arraies
// with length depending on the number of ants are allocated in
// reset function.
probabilities = new double[numberOfCity];
candidateSet = new int[numberOfCity];
ObjectiveValues = new double[populationSize];
// Set drop amount multiplier; which is the avarage length
//dropMultiplier = dropPheromone * dropMultiplier / getObjectiveValue(objectiveValues);
}
/// <summary>
/// To employ a GA solver, user must provide the number of variables, the optimization type, and a function delegate
/// that compute and return the objective value for a given solution.
/// </summary>
/// <param name="numberOfVariables"> Number of variables of the problem</param>
/// <param name="opType"> The optimization problem type </param>
/// <param name="objectFunction"> The function delegate that computer the objective value for a given solution </param>
public GA(int numberOfVariables, OptimizationType opType, GA <T> .ObjectiveFunctionDelegate objectFunction)
{
numberOfGenes = numberOfVariables;
optimizationType = opType;
if (objectFunction == null)
{
throw new Exception("You must prepare an objective function.");
}
GetObjectiveValueFunction = objectFunction;
}
