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);
}
}
}
public override IOperation Apply()
{
IVRPProblemInstance problemInstance = ProblemInstanceParameter.ActualValue;
ItemArray <IVRPEncoding> solutions = VRPToursParameter.ActualValue;
ResultCollection results = ResultsParameter.ActualValue;
ItemArray <DoubleValue> qualities = QualityParameter.ActualValue;
ItemArray <IntValue> pickupViolations = PickupViolationsParameter.ActualValue;
int i = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).First().index;
IVRPEncoding best = solutions[i] as IVRPEncoding;
VRPSolution solution = BestSolutionParameter.ActualValue;
if (solution != null)
{
if (!results.ContainsKey("Best VRP Solution PickupViolations"))
{
results.Add(new Result("Best VRP Solution PickupViolations", new DoubleValue(pickupViolations[i].Value)));
}
else
{
VRPEvaluation eval = problemInstance.Evaluate(solution.Solution);
if (qualities[i].Value <= eval.Quality)
{
(results["Best VRP Solution PickupViolations"].Value as DoubleValue).Value = pickupViolations[i].Value;
}
}
}
return(base.Apply());
}
public override IOperation Apply()
{
IVRPProblemInstance problemInstance = ProblemInstanceParameter.ActualValue;
ItemArray <IVRPEncoding> solutions = VRPToursParameter.ActualValue;
ResultCollection results = ResultsParameter.ActualValue;
ItemArray <DoubleValue> qualities = QualityParameter.ActualValue;
ItemArray <DoubleValue> distances = DistanceParameter.ActualValue;
ItemArray <DoubleValue> vehiclesUtilizations = VehiclesUtilizedParameter.ActualValue;
int i = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).First().index;
IVRPEncoding best = solutions[i].Clone() as IVRPEncoding;
VRPSolution solution = BestSolutionParameter.ActualValue;
if (solution == null)
{
solution = new VRPSolution(problemInstance, best.Clone() as IVRPEncoding, new DoubleValue(qualities[i].Value));
BestSolutionParameter.ActualValue = solution;
results.Add(new Result("Best VRP Solution", solution));
results.Add(new Result("Best VRP Solution Distance", new DoubleValue(distances[i].Value)));
results.Add(new Result("Best VRP Solution VehicleUtilization", new DoubleValue(vehiclesUtilizations[i].Value)));
}
else
{
VRPEvaluation eval = problemInstance.Evaluate(solution.Solution);
if (qualities[i].Value <= eval.Quality)
{
solution.ProblemInstance = problemInstance;
solution.Solution = best.Clone() as IVRPEncoding;
solution.Quality.Value = qualities[i].Value;
(results["Best VRP Solution Distance"].Value as DoubleValue).Value = distances[i].Value;
(results["Best VRP Solution VehicleUtilization"].Value as DoubleValue).Value = vehiclesUtilizations[i].Value;
}
}
var idx = qualities.Select((x, index) => new { index, x.Value }).Where(index => problemInstance.Feasible(solutions[index.index])).OrderBy(x => x.Value).FirstOrDefault();
if (idx != null)
{
int j = idx.index;
IVRPEncoding bestFeasible = solutions[j].Clone() as IVRPEncoding;
VRPSolution validSolution = BestValidSolutionParameter.ActualValue;
if (validSolution == null)
{
validSolution = new VRPSolution(problemInstance, best.Clone() as IVRPEncoding, new DoubleValue(qualities[j].Value));
BestValidSolutionParameter.ActualValue = validSolution;
if (results.ContainsKey("Best valid VRP Solution"))
{
results["Best valid VRP Solution"].Value = validSolution;
}
else
{
results.Add(new Result("Best valid VRP Solution", validSolution));
}
results.Add(new Result("Best valid VRP Solution Distance", new DoubleValue(distances[j].Value)));
results.Add(new Result("Best valid VRP Solution VehicleUtilization", new DoubleValue(vehiclesUtilizations[j].Value)));
}
else
{
if (qualities[j].Value <= validSolution.Quality.Value)
{
if (ProblemInstanceParameter.ActualValue.Feasible(best))
{
validSolution.ProblemInstance = problemInstance;
validSolution.Solution = best.Clone() as IVRPEncoding;
validSolution.Quality.Value = qualities[j].Value;
(results["Best valid VRP Solution Distance"].Value as DoubleValue).Value = distances[j].Value;
(results["Best valid VRP Solution VehicleUtilization"].Value as DoubleValue).Value = vehiclesUtilizations[j].Value;
}
}
}
DoubleValue bestKnownQuality = BestKnownQualityParameter.ActualValue;
if (bestKnownQuality == null || qualities[j].Value < bestKnownQuality.Value)
{
BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[j].Value);
BestKnownSolutionParameter.ActualValue = (VRPSolution)validSolution.Clone();
}
}
return(base.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));
}
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)));
}
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);
}
}