[17260] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Linq;
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| 24 | using HEAL.Attic;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Encodings.PermutationEncoding;
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| 29 | using HeuristicLab.Optimization;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Problems.Instances;
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| 32 | using HeuristicLab.Problems.TravelingSalesman;
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| 33 | using HeuristicLab.Random;
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| 34 |
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| 35 | namespace HeuristicLab.Problems.PTSP {
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| 36 | [Item("Probabilistic TSP (pTSP)", "Represents a Probabilistic Traveling Salesman Problem.")]
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| 37 | [StorableType("86041a8c-14e6-46e1-b20f-566892c871f6")]
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| 38 | public abstract class ProbabilisticTSP : PermutationProblem,
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| 39 | IProblemInstanceConsumer<PTSPData> {
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| 40 | protected bool SuppressEvents { get; set; }
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| 41 |
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| 42 | public static int DistanceMatrixSizeLimit = 1000;
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| 43 |
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| 44 | #region Parameter Properties
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| 45 | [Storable] public ValueParameter<IProbabilisticTSPData> PTSPDataParameter { get; private set; }
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| 46 | [Storable] public OptionalValueParameter<IProbabilisticTSPSolution> BestKnownSolutionParameter { get; private set; }
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| 47 | #endregion
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| 48 |
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| 49 | #region Properties
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| 50 | public IProbabilisticTSPData ProbabilisticTSPData {
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| 51 | get { return PTSPDataParameter.Value; }
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| 52 | set { PTSPDataParameter.Value = value; }
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| 53 | }
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| 54 | public IProbabilisticTSPSolution BestKnownSolution {
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| 55 | get { return BestKnownSolutionParameter.Value; }
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| 56 | set { BestKnownSolutionParameter.Value = value; }
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| 57 | }
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| 58 | #endregion
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| 59 |
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| 60 | [StorableConstructor]
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| 61 | protected ProbabilisticTSP(StorableConstructorFlag _) : base(_) { }
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| 62 | protected ProbabilisticTSP(ProbabilisticTSP original, Cloner cloner)
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| 63 | : base(original, cloner) {
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| 64 | PTSPDataParameter = cloner.Clone(original.PTSPDataParameter);
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| 65 | BestKnownSolutionParameter = cloner.Clone(original.BestKnownSolutionParameter);
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| 66 | }
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| 67 | protected ProbabilisticTSP() : base(new PermutationEncoding("Tour")) {
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[17270] | 68 | Maximization = false;
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[17260] | 69 | Parameters.Add(PTSPDataParameter = new ValueParameter<IProbabilisticTSPData>("PTSP Data", "The main parameters for the pTSP."));
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| 70 | Parameters.Add(BestKnownSolutionParameter = new OptionalValueParameter<IProbabilisticTSPSolution>("BestKnownSolution", "The best known solution of this pTSP instance."));
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| 71 |
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| 72 | ProbabilisticTSPData = new MatrixPTSPData();
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| 73 | Encoding.Length = ProbabilisticTSPData.Cities;
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| 74 | }
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| 75 |
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| 76 | protected override void OnEncodingChanged() {
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| 77 | base.OnEncodingChanged();
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| 78 | Encoding.Length = ProbabilisticTSPData.Cities;
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| 79 | }
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| 80 |
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| 81 | public override void Analyze(Permutation[] solutions, double[] qualities, ResultCollection results, IRandom random) {
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| 82 | base.Analyze(solutions, qualities, results, random);
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| 83 | var max = Maximization;
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| 84 |
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| 85 | var i = !max ? qualities.Select((x, index) => new { index, Quality = x }).OrderBy(x => x.Quality).First().index
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| 86 | : qualities.Select((x, index) => new { index, Quality = x }).OrderByDescending(x => x.Quality).First().index;
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| 87 |
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| 88 | if (double.IsNaN(BestKnownQuality) ||
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| 89 | max && qualities[i] > BestKnownQuality ||
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| 90 | !max && qualities[i] < BestKnownQuality) {
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| 91 | BestKnownQuality = qualities[i];
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| 92 | BestKnownSolution = ProbabilisticTSPData.GetSolution((Permutation)solutions[i].Clone(), qualities[i]);
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| 93 | }
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| 94 |
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| 95 | IResult bestSolutionResult;
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| 96 | if (results.TryGetValue("Best pTSP Solution", out bestSolutionResult)) {
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| 97 | var bestSolution = bestSolutionResult.Value as ITSPSolution;
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| 98 | if (bestSolution == null || Maximization && bestSolution.TourLength.Value < qualities[i]
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| 99 | || !Maximization && bestSolution.TourLength.Value > qualities[i]) {
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| 100 | bestSolutionResult.Value = ProbabilisticTSPData.GetSolution(solutions[i], qualities[i]);
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| 101 | }
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| 102 | } else results.Add(new Result("Best pTSP Solution", ProbabilisticTSPData.GetSolution(solutions[i], qualities[i])));
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| 103 | }
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| 104 |
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| 105 | public virtual void Load(PTSPData data) {
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| 106 | if (data.Coordinates == null && data.Distances == null)
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| 107 | throw new System.IO.InvalidDataException("The given instance specifies neither coordinates nor distances!");
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| 108 | if (data.Dimension > DistanceMatrixSizeLimit && (data.DistanceMeasure == DistanceMeasure.Att
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| 109 | || data.DistanceMeasure == DistanceMeasure.Manhattan
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| 110 | || data.DistanceMeasure == DistanceMeasure.Maximum))
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| 111 | throw new System.IO.InvalidDataException("The given instance uses an unsupported distance measure and is too large for using a distance matrix.");
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| 112 | if (data.Coordinates != null && data.Coordinates.GetLength(1) != 2)
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| 113 | throw new System.IO.InvalidDataException("The coordinates of the given instance are not in the right format, there need to be one row for each customer and two columns for the x and y coordinates.");
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| 114 |
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| 115 | Encoding.Length = data.Dimension;
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| 116 | Name = data.Name;
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| 117 | Description = data.Description;
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| 118 |
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| 119 | if (data.Dimension <= DistanceMatrixSizeLimit) {
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| 120 | ProbabilisticTSPData = new MatrixPTSPData(data.Name, data.GetDistanceMatrix(), data.Probabilities, data.Coordinates) { Description = data.Description };
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| 121 | } else if (data.DistanceMeasure == DistanceMeasure.Direct && data.Distances != null) {
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| 122 | ProbabilisticTSPData = new MatrixPTSPData(data.Name, data.Distances, data.Probabilities, data.Coordinates) { Description = data.Description };
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| 123 | } else {
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| 124 | switch (data.DistanceMeasure) {
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| 125 | case DistanceMeasure.Att:
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| 126 | ProbabilisticTSPData = new AttPTSPData(data.Name, data.Coordinates, data.Probabilities) { Description = data.Description };
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| 127 | break;
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| 128 | case DistanceMeasure.Euclidean:
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| 129 | ProbabilisticTSPData = new EuclideanPTSPData(data.Name, data.Coordinates, data.Probabilities, EuclideanTSPData.DistanceRounding.None) { Description = data.Description };
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| 130 | break;
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| 131 | case DistanceMeasure.RoundedEuclidean:
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| 132 | ProbabilisticTSPData = new EuclideanPTSPData(data.Name, data.Coordinates, data.Probabilities, EuclideanTSPData.DistanceRounding.Midpoint) { Description = data.Description };
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| 133 | break;
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| 134 | case DistanceMeasure.UpperEuclidean:
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| 135 | ProbabilisticTSPData = new EuclideanPTSPData(data.Name, data.Coordinates, data.Probabilities, EuclideanTSPData.DistanceRounding.Ceiling) { Description = data.Description };
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| 136 | break;
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| 137 | case DistanceMeasure.Geo:
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| 138 | ProbabilisticTSPData = new GeoPTSPData(data.Name, data.Coordinates, data.Probabilities) { Description = data.Description };
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| 139 | break;
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| 140 | case DistanceMeasure.Manhattan:
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| 141 | ProbabilisticTSPData = new ManhattanPTSPData(data.Name, data.Coordinates, data.Probabilities) { Description = data.Description };
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| 142 | break;
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| 143 | case DistanceMeasure.Maximum:
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| 144 | ProbabilisticTSPData = new MaximumPTSPData(data.Name, data.Coordinates, data.Probabilities) { Description = data.Description };
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| 145 | break;
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| 146 | default:
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| 147 | throw new System.IO.InvalidDataException("An unknown distance measure is given in the instance!");
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| 148 | }
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| 149 | }
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| 150 | BestKnownSolution = null;
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| 151 | BestKnownQuality = double.NaN;
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| 152 |
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| 153 | if (data.BestKnownTour != null) {
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| 154 | try {
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| 155 | var tour = new Permutation(PermutationTypes.RelativeUndirected, data.BestKnownTour);
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[17264] | 156 | var tourLength = Evaluate(tour, new MersenneTwister(1));
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[17260] | 157 | BestKnownSolution = new ProbabilisticTSPSolution(data.Coordinates != null ? new DoubleMatrix(data.Coordinates) : null, new PercentArray(data.Probabilities), tour, new DoubleValue(tourLength));
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| 158 | BestKnownQuality = tourLength;
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| 159 | } catch (InvalidOperationException) {
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| 160 | if (data.BestKnownQuality.HasValue)
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| 161 | BestKnownQuality = data.BestKnownQuality.Value;
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| 162 | }
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| 163 | } else if (data.BestKnownQuality.HasValue) {
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| 164 | BestKnownQuality = data.BestKnownQuality.Value;
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| 165 | }
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| 166 | OnReset();
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| 167 | }
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| 168 | }
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| 169 | }
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