本文整理汇总了C#中IVRPProblemInstance.Evaluate方法的典型用法代码示例。如果您正苦于以下问题：C# IVRPProblemInstance.Evaluate方法的具体用法？C# IVRPProblemInstance.Evaluate怎么用？C# IVRPProblemInstance.Evaluate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类IVRPProblemInstance的用法示例。
在下文中一共展示了IVRPProblemInstance.Evaluate方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Apply
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));
}示例2: Apply
public static void Apply(AlbaEncoding solution, int maxIterations,
int lambda, int samples, IRandom random, IVRPProblemInstance problemInstance, ref double quality, out int evaluatedSolutions) {
evaluatedSolutions = 0;
for (int i = 0; i < maxIterations; i++) {
AlbaLambdaInterchangeMove bestMove = null;
foreach (AlbaLambdaInterchangeMove move in AlbaStochasticLambdaInterchangeMultiMoveGenerator.GenerateAllMoves(solution, problemInstance, lambda, samples, random)) {
AlbaEncoding newSolution = solution.Clone() as AlbaEncoding;
AlbaLambdaInterchangeMoveMaker.Apply(newSolution, move);
double moveQuality =
problemInstance.Evaluate(newSolution).Quality;
evaluatedSolutions++;
if (moveQuality < quality || quality == -1) {
quality = moveQuality;
bestMove = move;
}
}
if (bestMove != null)
AlbaLambdaInterchangeMoveMaker.Apply(solution, bestMove);
}
}