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.Collections.Generic;
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24 | using System.Linq;
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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.Optimization;
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29 | using HeuristicLab.Optimization.Operators;
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30 | using HeuristicLab.Parameters;
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31 | using HEAL.Attic;
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32 | using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
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33 | using HeuristicLab.Problems.VehicleRouting.Interfaces;
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34 | using HeuristicLab.Problems.VehicleRouting.ProblemInstances;
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35 | using HeuristicLab.Problems.VehicleRouting.Variants;
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36 |
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37 | namespace HeuristicLab.Problems.VehicleRouting {
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38 | /// <summary>
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39 | /// An operator which relinks paths between VRP solutions.
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40 | /// </summary>
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41 | [Item("VRPPathRelinker", "An operator which relinks paths between VRP solutions.")]
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42 | [StorableType("C0C17982-BC36-4DF9-8C33-2B6F9A19CA53")]
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43 | public sealed class VRPPathRelinker : SingleObjectivePathRelinker, IGeneralVRPOperator, IStochasticOperator {
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44 | #region Parameter properties
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45 | public IValueParameter<IntValue> IterationsParameter {
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46 | get { return (IValueParameter<IntValue>)Parameters["Iterations"]; }
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47 | }
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48 | public ILookupParameter<IVRPProblemInstance> ProblemInstanceParameter {
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49 | get { return (ILookupParameter<IVRPProblemInstance>)Parameters["ProblemInstance"]; }
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50 | }
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51 | public ILookupParameter<IRandom> RandomParameter {
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52 | get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
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53 | }
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54 | public IValueParameter<IntValue> SampleSizeParameter {
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55 | get { return (IValueParameter<IntValue>)Parameters["SampleSize"]; }
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56 | }
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57 | #endregion
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58 |
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59 | [StorableConstructor]
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60 | private VRPPathRelinker(StorableConstructorFlag _) : base(_) { }
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61 | private VRPPathRelinker(VRPPathRelinker original, Cloner cloner) : base(original, cloner) { }
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62 | public VRPPathRelinker()
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63 | : base() {
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64 | Parameters.Add(new ValueParameter<IntValue>("Iterations", "The number of iterations the operator should perform.", new IntValue(50)));
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65 | Parameters.Add(new LookupParameter<IVRPProblemInstance>("ProblemInstance", "The VRP problem instance"));
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66 | Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used for stochastic manipulation operators."));
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67 | Parameters.Add(new ValueParameter<IntValue>("SampleSize", "The number of moves that should be executed.", new IntValue(10)));
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68 | }
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69 |
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70 | public override IDeepCloneable Clone(Cloner cloner) {
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71 | return new VRPPathRelinker(this, cloner);
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72 | }
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73 |
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74 | public static ItemArray<IItem> Apply(PotvinEncodedSolution initiator, PotvinEncodedSolution guide, PercentValue n, int sampleSize, int iterations, IRandom rand, IVRPProblemInstance problemInstance) {
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75 | if (initiator == null || guide == null)
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76 | throw new ArgumentException("Cannot relink path because one of the provided solutions or both are null.");
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77 |
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78 | double sigma = 1.5;
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79 | double minPenalty = 0.001;
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80 | double maxPenalty = 1000000000;
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81 |
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82 | var originalOverloadPenalty = new DoubleValue();
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83 | if (problemInstance is IHomogenousCapacitatedProblemInstance)
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84 | originalOverloadPenalty.Value = (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value;
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85 | var originalTardinessPenalty = new DoubleValue();
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86 | if (problemInstance is ITimeWindowedProblemInstance)
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87 | originalTardinessPenalty.Value = (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value;
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88 |
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89 | PotvinEncodedSolution current = MatchTours(initiator, guide, problemInstance);
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90 | double currentSimilarity = VRPSimilarityCalculator.CalculateSimilarity(current, guide);
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91 |
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92 | IList<PotvinEncodedSolution> solutions = new List<PotvinEncodedSolution>();
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93 | int i = 0;
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94 | while (i < iterations && !currentSimilarity.IsAlmost(1.0)) {
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95 | var currentEval = problemInstance.Evaluate(current);
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96 | currentSimilarity = VRPSimilarityCalculator.CalculateSimilarity(current, guide);
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97 |
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98 | if (currentSimilarity < 1.0) {
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99 | for (int sample = 0; sample < sampleSize; sample++) {
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100 | var next = current.Clone() as PotvinEncodedSolution;
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101 |
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102 | int neighborhood = rand.Next(3);
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103 | switch (neighborhood) {
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104 | case 0: next = RouteBasedXOver(next, guide, rand,
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105 | problemInstance);
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106 | break;
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107 | case 1: next = SequenceBasedXOver(next, guide, rand,
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108 | problemInstance);
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109 | break;
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110 | case 2: GuidedRelocateMove(next, guide, rand);
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111 | break;
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112 | }
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113 |
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114 | next = MatchTours(next, guide, problemInstance);
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115 |
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116 | var nextEval = problemInstance.Evaluate(next);
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117 | if ((nextEval.Quality < currentEval.Quality)) {
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118 | current = next;
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119 | solutions.Add(current);
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120 | break;
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121 | }
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122 | }
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123 |
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124 | if (problemInstance is IHomogenousCapacitatedProblemInstance) {
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125 | if (((CVRPEvaluation)currentEval).Overload > 0) {
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126 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value =
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127 | Math.Min(maxPenalty,
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128 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value * sigma);
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129 | } else {
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130 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value =
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131 | Math.Max(minPenalty,
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132 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value * sigma);
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133 | }
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134 | }
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135 |
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136 |
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137 | if (problemInstance is ITimeWindowedProblemInstance) {
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138 | if (((CVRPTWEvaluation)currentEval).Tardiness > 0) {
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139 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value =
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140 | Math.Min(maxPenalty,
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141 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value * sigma);
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142 | } else {
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143 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value =
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144 | Math.Max(minPenalty,
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145 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value / sigma);
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146 | }
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147 | }
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148 |
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149 | i++;
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150 | }
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151 | }
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152 |
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153 | if (problemInstance is IHomogenousCapacitatedProblemInstance)
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154 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value = originalOverloadPenalty.Value;
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155 | if (problemInstance is ITimeWindowedProblemInstance)
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156 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value = originalTardinessPenalty.Value;
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157 |
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158 | return new ItemArray<IItem>(ChooseSelection(solutions, n));
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159 | }
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160 |
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161 | private static IList<IItem> ChooseSelection(IList<PotvinEncodedSolution> solutions, PercentValue n) {
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162 | IList<IItem> selection = new List<IItem>();
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163 | if (solutions.Count > 0) {
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164 | int noSol = (int)(solutions.Count * n.Value);
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165 | if (noSol <= 0) noSol++;
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166 | double stepSize = (double)solutions.Count / (double)noSol;
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167 | for (int i = 0; i < noSol; i++)
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168 | selection.Add(solutions.ElementAt((int)((i + 1) * stepSize - stepSize * 0.5)));
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169 | }
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170 |
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171 | return selection;
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172 | }
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173 |
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174 | protected override ItemArray<IItem> Relink(ItemArray<IItem> parents, PercentValue n) {
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175 | if (parents.Length != 2)
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176 | throw new ArgumentException("The number of parents is not equal to 2.");
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177 |
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178 | if (!(parents[0] is PotvinEncodedSolution))
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179 | parents[0] = PotvinEncodedSolution.ConvertFrom(parents[0] as IVRPEncodedSolution, ProblemInstanceParameter.ActualValue);
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180 | if (!(parents[1] is PotvinEncodedSolution))
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181 | parents[1] = PotvinEncodedSolution.ConvertFrom(parents[1] as IVRPEncodedSolution, ProblemInstanceParameter.ActualValue);
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182 |
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183 | return Apply(parents[0] as PotvinEncodedSolution, parents[1] as PotvinEncodedSolution, n,
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184 | SampleSizeParameter.Value.Value, IterationsParameter.Value.Value, RandomParameter.ActualValue, ProblemInstanceParameter.ActualValue);
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185 | }
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186 |
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187 | private static int MatchingCities(Tour tour1, Tour tour2) {
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188 | return tour1.Stops.Intersect(tour2.Stops).Count();
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189 | }
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190 |
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191 | private static PotvinEncodedSolution MatchTours(PotvinEncodedSolution initiator, PotvinEncodedSolution guide, IVRPProblemInstance problemInstance) {
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192 | var result = new PotvinEncodedSolution(problemInstance);
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193 |
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194 | var used = new List<bool>();
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195 | for (int i = 0; i < initiator.Tours.Count; i++) {
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196 | used.Add(false);
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197 | }
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198 |
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199 | for (int i = 0; i < guide.Tours.Count; i++) {
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200 | int bestMatch = -1;
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201 | int bestTour = -1;
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202 |
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203 | for (int j = 0; j < initiator.Tours.Count; j++) {
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204 | if (!used[j]) {
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205 | int match = MatchingCities(guide.Tours[i], initiator.Tours[j]);
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206 | if (match > bestMatch) {
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207 | bestMatch = match;
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208 | bestTour = j;
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209 | }
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210 | }
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211 | }
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212 |
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213 | if (bestTour != -1) {
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214 | result.Tours.Add(initiator.Tours[bestTour]);
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215 | used[bestTour] = true;
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216 | }
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217 | }
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218 |
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219 | for (int i = 0; i < initiator.Tours.Count; i++) {
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220 | if (!used[i])
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221 | result.Tours.Add(initiator.Tours[i]);
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222 | }
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223 |
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224 | return result;
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225 | }
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226 |
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227 | #region moves
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228 | public static PotvinEncodedSolution RouteBasedXOver(PotvinEncodedSolution initiator, PotvinEncodedSolution guide, IRandom random, IVRPProblemInstance problemInstance) {
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229 | return PotvinRouteBasedCrossover.Apply(random, initiator, guide, problemInstance, false);
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230 | }
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231 |
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232 | public static PotvinEncodedSolution SequenceBasedXOver(PotvinEncodedSolution initiator, PotvinEncodedSolution guide, IRandom random, IVRPProblemInstance problemInstance) {
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233 | return PotvinSequenceBasedCrossover.Apply(random, initiator, guide, problemInstance, false);
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234 | }
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235 |
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236 | public static void GuidedRelocateMove(PotvinEncodedSolution initiator, PotvinEncodedSolution guide, IRandom random) {
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237 | List<int> cities = new List<int>();
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238 | foreach (Tour tour in initiator.Tours) {
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239 | foreach (int city in tour.Stops) {
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240 | Tour guideTour = guide.Tours.First(t => t.Stops.Contains(city));
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241 | if (guide.Tours.IndexOf(guideTour) != initiator.Tours.IndexOf(tour)) {
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242 | cities.Add(city);
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243 | }
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244 | }
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245 | }
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246 |
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247 | if (cities.Count == 0) {
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248 | RelocateMove(initiator, random);
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249 | } else {
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250 | int city = cities[random.Next(cities.Count)];
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251 | Tour tour = initiator.Tours.First(t => t.Stops.Contains(city));
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252 | tour.Stops.Remove(city);
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253 |
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254 | Tour guideTour = guide.Tours.First(t => t.Stops.Contains(city));
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255 | int guideTourIndex = guide.Tours.IndexOf(guideTour);
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256 |
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257 | if (guideTourIndex < initiator.Tours.Count) {
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258 | Tour tour2 = initiator.Tours[guideTourIndex];
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259 |
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260 | int guideIndex = guideTour.Stops.IndexOf(city);
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261 | if (guideIndex == 0) {
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262 | tour2.Stops.Insert(0, city);
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263 | } else {
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264 | int predecessor = guideTour.Stops[guideIndex - 1];
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265 | int initIndex = tour2.Stops.IndexOf(predecessor);
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266 | if (initIndex != -1) {
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267 | tour2.Stops.Insert(initIndex + 1, city);
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268 | } else {
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269 | if (guideIndex == guideTour.Stops.Count - 1) {
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270 | tour2.Stops.Insert(tour2.Stops.Count, city);
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271 | } else {
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272 | int sucessor = guideTour.Stops[guideIndex + 1];
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273 | initIndex = tour2.Stops.IndexOf(sucessor);
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274 | if (initIndex != -1) {
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275 | tour2.Stops.Insert(initIndex, city);
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276 | } else {
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277 | tour2.Stops.Insert(random.Next(tour2.Stops.Count + 1), city);
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278 | }
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279 | }
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280 | }
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281 | }
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282 | } else {
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283 | Tour tour2 = new Tour();
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284 | tour2.Stops.Add(city);
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285 | initiator.Tours.Add(tour2);
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286 | }
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287 |
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288 | if (tour.Stops.Count == 0)
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289 | initiator.Tours.Remove(tour);
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290 | }
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291 | }
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292 |
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293 | public static void RelocateMove(PotvinEncodedSolution individual, IRandom random) {
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294 | int cities = individual.Cities;
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295 | int city = 1 + random.Next(cities);
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296 | Tour originalTour = individual.Tours.Find(t => t.Stops.Contains(city));
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297 | //consider creating new route
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298 | individual.Tours.Add(new Tour());
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299 |
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300 | int position = 1 + random.Next(cities + individual.Tours.Count - 1);
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301 | if (position >= city) {
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302 | position++;
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303 | }
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304 |
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305 | int originalPosition = originalTour.Stops.IndexOf(city);
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306 | originalTour.Stops.RemoveAt(originalPosition);
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307 |
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308 | Tour insertionTour;
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309 | int insertionPosition;
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310 | if (position <= cities) {
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311 | insertionTour = individual.Tours.Find(t => t.Stops.Contains(position));
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312 | insertionPosition = insertionTour.Stops.IndexOf(position) + 1;
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313 | } else {
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314 | insertionTour = individual.Tours[position - cities - 1];
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315 | insertionPosition = 0;
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316 | }
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317 |
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318 | insertionTour.Stops.Insert(insertionPosition, city);
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319 |
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320 | individual.Tours.RemoveAll(t => t.Stops.Count == 0);
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321 | }
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322 | #endregion
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323 | }
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324 | } |
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