private VRPSimilarityCalculator(VRPSimilarityCalculator original, Cloner cloner)
: base(original, cloner)
{
this.ProblemInstance = cloner.Clone(original.ProblemInstance);
}
private VRPSimilarityCalculator(VRPSimilarityCalculator original, Cloner cloner)
: base(original, cloner) {
this.ProblemInstance = cloner.Clone(original.ProblemInstance);
}
public static ItemArray <IItem> Apply(PotvinEncoding initiator, PotvinEncoding guide, PercentValue n, int sampleSize, int iterations, IRandom rand, IVRPProblemInstance problemInstance)
{
if (initiator == null || guide == null)
{
throw new ArgumentException("Cannot relink path because one of the provided solutions or both are null.");
}
double sigma = 1.5;
double minPenalty = 0.001;
double maxPenalty = 1000000000;
var originalOverloadPenalty = new DoubleValue();
if (problemInstance is IHomogenousCapacitatedProblemInstance)
{
originalOverloadPenalty.Value = (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value;
}
var originalTardinessPenalty = new DoubleValue();
if (problemInstance is ITimeWindowedProblemInstance)
{
originalTardinessPenalty.Value = (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value;
}
PotvinEncoding current = MatchTours(initiator, guide, problemInstance);
double currentSimilarity = VRPSimilarityCalculator.CalculateSimilarity(current, guide);
IList <PotvinEncoding> solutions = new List <PotvinEncoding>();
int i = 0;
while (i < iterations && !currentSimilarity.IsAlmost(1.0))
{
var currentEval = problemInstance.Evaluate(current);
currentSimilarity = VRPSimilarityCalculator.CalculateSimilarity(current, guide);
if (currentSimilarity < 1.0)
{
for (int sample = 0; sample < sampleSize; sample++)
{
var next = current.Clone() as PotvinEncoding;
int neighborhood = rand.Next(3);
switch (neighborhood)
{
case 0: next = RouteBasedXOver(next, guide, rand,
problemInstance);
break;
case 1: next = SequenceBasedXOver(next, guide, rand,
problemInstance);
break;
case 2: GuidedRelocateMove(next, guide, rand);
break;
}
next = MatchTours(next, guide, problemInstance);
var nextEval = problemInstance.Evaluate(next);
if ((nextEval.Quality < currentEval.Quality))
{
current = next;
solutions.Add(current);
break;
}
}
if (problemInstance is IHomogenousCapacitatedProblemInstance)
{
if (((CVRPEvaluation)currentEval).Overload > 0)
{
(problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value =
Math.Min(maxPenalty,
(problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value * sigma);
}
else
{
(problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value =
Math.Max(minPenalty,
(problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value * sigma);
}
}
if (problemInstance is ITimeWindowedProblemInstance)
{
if (((CVRPTWEvaluation)currentEval).Tardiness > 0)
{
(problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value =
Math.Min(maxPenalty,
(problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value * sigma);
}
else
{
(problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value =
Math.Max(minPenalty,
(problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value / sigma);
}
}
i++;
}
}
if (problemInstance is IHomogenousCapacitatedProblemInstance)
{
(problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value = originalOverloadPenalty.Value;
}
if (problemInstance is ITimeWindowedProblemInstance)
{
(problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value = originalTardinessPenalty.Value;
}
return(new ItemArray <IItem>(ChooseSelection(solutions, n)));
}
