private Ant GetAntWithShortestDistance()
{
var bestDistance = _ants.First().CalculateTotalTrailDistance(_map);
Ant bestAnt = _ants.First();
for (int antId = 1; antId < _ants.Count; antId++)
{
var antDistance = _ants[antId].CalculateTotalTrailDistance(_map);
if (antDistance < bestDistance)
{
bestAnt = _ants[antId];
bestDistance = antDistance;
}
}
return(bestAnt);
}
private double[] CalculateProbabilities(Ant ant, int currentCity)
{
double sum = 0.0;
var tauSigmaProduct = new double[_numberOfCities];
for (int i = 0; i < tauSigmaProduct.Length; i++)
{
if (i == currentCity)
{
tauSigmaProduct[i] = 0.0;
}
else if (ant.VisitedCities[i])
{
tauSigmaProduct[i] = 0.0;
}
else
{
var tauPow = Math.Pow(_map.Pheromones[currentCity, i], _alpha);
var sigmaPow = Math.Pow(1.0 / _map.Distances[currentCity, i], _beta);
tauSigmaProduct[i] = tauPow * sigmaPow;
if (tauSigmaProduct[i] < 0.0001)
{
tauSigmaProduct[i] = 0.0001;
}
else if (tauSigmaProduct[i] > double.MaxValue / (_numberOfCities * 100))
{
tauSigmaProduct[i] = double.MaxValue / (_numberOfCities * 100);
}
}
sum += tauSigmaProduct[i];
}
var probabilities = new double[_numberOfCities];
for (int i = 0; i < probabilities.Length; i++)
{
probabilities[i] = tauSigmaProduct[i] / sum; // Houston we have problem when the sum equals zero :/
}
return(probabilities);
}
private int[] BuildTrail(Ant ant)
{
var trail = new int[_numberOfCities];
ant.ResetVisited();
var startingCity = _randomizer.Next(0, _numberOfCities);
const int lastCityOffset = 1;
trail[0] = startingCity;
ant.VisitedCities[startingCity] = true;
for (int cityId = 0; cityId < _numberOfCities - lastCityOffset; cityId++)
{
var currentCity = trail[cityId];
var nextCity = GetNextCity(ant, currentCity);
trail[cityId + 1] = nextCity;
ant.VisitedCities[nextCity] = true;
}
return(trail);
}
public double[] Run(ObjectiveFunction objectiveFunction,
int maxIterationCount, out int usedIterationCount, double acceptableEvaluation, out double achievedEvalation,
int antCount, int normalPDFCount, double requiredAccuracy
)
{
if (objectiveFunction == null)
{
throw new ArgumentNullException("objectiveFunction");
}
this.objectiveFunction = objectiveFunction;
// Pheromone Maintenance - Initialization
CreatePheromoneTrail(normalPDFCount);
CreateAntColony(antCount);
// Keep track of the global-best ant for elitist purposes.
globalBestAnt = antColony[0];
EvaluateAnt(globalBestAnt);
// Repeat while the computational budget is not spend and an acceptable solution is not found.
int iterationIndex = 0;
while ((iterationIndex < maxIterationCount) && !IsAcceptableSolutionFound(acceptableEvaluation))
{
// Solution Construction
EvaluateAntColony();
// Pheromone Maintenance - Update
UpdatePheromoneTrail(iterationIndex + 1, requiredAccuracy);
iterationIndex++;
}
// Return the (global-best) solution.
usedIterationCount = iterationIndex;
achievedEvalation = (objectiveFunction.Objective == Objective.MINIMIZE) ? globalBestAnt.Evaluation : (1 / globalBestAnt.Evaluation);
return(globalBestAnt.Steps);
}
public static string CheckIsBestRouteCorrect(Action <Ant, int> MoveAntToNextCity, List <int> bestRouteFound, float bestDistanceFound)
{
var checkingList = bestRouteFound;
checkingList.Add(checkingList.First());
checkingList.RemoveAt(0);
var ant = new Ant(checkingList.Last());
for (int i = 0; i < checkingList.Count; i++)
{
MoveAntToNextCity(ant, checkingList[i]);
}
if (bestDistanceFound - ant.distance == 0)
{
return("CORRECT");
}
else
{
throw new Exception("INCORRECT");
}
}
/// <summary>
/// Evalautes an ant.
/// </summary>
/// <param name="ant">The ant to evalaute.</param>
private void EvaluateAnt(Ant ant)
{
ant.Evaluation = (Objective == Objective.MINIMIZE) ? objectiveFunction.Evaluate(ant.Steps) : (1 / objectiveFunction.Evaluate(ant.Steps));
}
