private QAPAssignment(QAPAssignment original, Cloner cloner) : base(original, cloner) { distances = cloner.Clone(original.distances); weights = cloner.Clone(original.weights); assignment = cloner.Clone(original.assignment); quality = cloner.Clone(original.quality); }
public override IOperation Apply() { DoubleMatrix distances = DistancesParameter.ActualValue; DoubleMatrix weights = WeightsParameter.ActualValue; ItemArray <Permutation> permutations = PermutationParameter.ActualValue; ItemArray <DoubleValue> qualities = QualityParameter.ActualValue; ResultCollection results = ResultsParameter.ActualValue; bool max = MaximizationParameter.ActualValue.Value; DoubleValue bestKnownQuality = BestKnownQualityParameter.ActualValue; var sorted = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).ToArray(); if (max) { sorted = sorted.Reverse().ToArray(); } int i = sorted.First().index; if (bestKnownQuality == null || max && qualities[i].Value > bestKnownQuality.Value || !max && qualities[i].Value < bestKnownQuality.Value) { // if there isn't a best-known quality or we improved the best-known quality we'll add the current solution as best-known BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[i].Value); BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone(); BestKnownSolutionsParameter.ActualValue = new ItemSet <Permutation>(new PermutationEqualityComparer()); BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[i].Clone()); } else if (bestKnownQuality.Value == qualities[i].Value) { // if we matched the best-known quality we'll try to set the best-known solution if it isn't null // and try to add it to the pool of best solutions if it is different if (BestKnownSolutionParameter.ActualValue == null) { BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone(); } if (BestKnownSolutionsParameter.ActualValue == null) { BestKnownSolutionsParameter.ActualValue = new ItemSet <Permutation>(new PermutationEqualityComparer()); } foreach (var k in sorted) // for each solution that we found check if it is in the pool of best-knowns { if (!max && k.Value > qualities[i].Value || max && k.Value < qualities[i].Value) { break; // stop when we reached a solution worse than the best-known quality } Permutation p = permutations[k.index]; if (!BestKnownSolutionsParameter.ActualValue.Contains(p)) { BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[k.index].Clone()); } } } QAPAssignment assignment = BestSolutionParameter.ActualValue; if (assignment == null) { assignment = new QAPAssignment(weights, (Permutation)permutations[i].Clone(), new DoubleValue(qualities[i].Value)); assignment.Distances = distances; BestSolutionParameter.ActualValue = assignment; results.Add(new Result("Best QAP Solution", assignment)); } else { if (max && assignment.Quality.Value < qualities[i].Value || !max && assignment.Quality.Value > qualities[i].Value) { assignment.Distances = distances; assignment.Weights = weights; assignment.Assignment = (Permutation)permutations[i].Clone(); assignment.Quality.Value = qualities[i].Value; } } return(base.Apply()); }
public override IOperation Apply() { DoubleMatrix distances = DistancesParameter.ActualValue; DoubleMatrix weights = WeightsParameter.ActualValue; ItemArray<Permutation> permutations = PermutationParameter.ActualValue; ItemArray<DoubleValue> qualities = QualityParameter.ActualValue; ResultCollection results = ResultsParameter.ActualValue; bool max = MaximizationParameter.ActualValue.Value; DoubleValue bestKnownQuality = BestKnownQualityParameter.ActualValue; var sorted = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).ToArray(); if (max) sorted = sorted.Reverse().ToArray(); int i = sorted.First().index; if (bestKnownQuality == null || max && qualities[i].Value > bestKnownQuality.Value || !max && qualities[i].Value < bestKnownQuality.Value) { // if there isn't a best-known quality or we improved the best-known quality we'll add the current solution as best-known BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[i].Value); BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone(); BestKnownSolutionsParameter.ActualValue = new ItemSet<Permutation>(new PermutationEqualityComparer()); BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[i].Clone()); } else if (bestKnownQuality.Value == qualities[i].Value) { // if we matched the best-known quality we'll try to set the best-known solution if it isn't null // and try to add it to the pool of best solutions if it is different if (BestKnownSolutionParameter.ActualValue == null) BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone(); if (BestKnownSolutionsParameter.ActualValue == null) BestKnownSolutionsParameter.ActualValue = new ItemSet<Permutation>(new PermutationEqualityComparer()); foreach (var k in sorted) { // for each solution that we found check if it is in the pool of best-knowns if (!max && k.Value > qualities[i].Value || max && k.Value < qualities[i].Value) break; // stop when we reached a solution worse than the best-known quality Permutation p = permutations[k.index]; if (!BestKnownSolutionsParameter.ActualValue.Contains(p)) BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[k.index].Clone()); } } QAPAssignment assignment = BestSolutionParameter.ActualValue; if (assignment == null) { assignment = new QAPAssignment(weights, (Permutation)permutations[i].Clone(), new DoubleValue(qualities[i].Value)); assignment.Distances = distances; BestSolutionParameter.ActualValue = assignment; results.Add(new Result("Best QAP Solution", assignment)); } else { if (max && assignment.Quality.Value < qualities[i].Value || !max && assignment.Quality.Value > qualities[i].Value) { assignment.Distances = distances; assignment.Weights = weights; assignment.Assignment = (Permutation)permutations[i].Clone(); assignment.Quality.Value = qualities[i].Value; } } return base.Apply(); }