1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2011 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 HeuristicLab.Common;
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23 | using HeuristicLab.Core;
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24 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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25 | using System.Collections.Generic;
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26 | using HeuristicLab.Data;
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27 | using System;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Problems.VehicleRouting.ProblemInstances;
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30 |
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31 | namespace HeuristicLab.Problems.VehicleRouting.Encodings.Potvin {
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32 | [Item("PotvinInsertionBasedCrossover", "The IBX crossover for VRP representations. It is implemented as described in Berger, J and Solois, M and Begin, R (1998). A hybrid genetic algorithm for the vehicle routing problem with time windows. LNCS 1418. Springer, London 114-127.")]
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33 | [StorableClass]
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34 | public sealed class PotvinInsertionBasedCrossover : PotvinCrossover {
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35 | public IValueParameter<IntValue> Length {
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36 | get { return (IValueParameter<IntValue>)Parameters["Length"]; }
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37 | }
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38 |
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39 | [StorableConstructor]
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40 | private PotvinInsertionBasedCrossover(bool deserializing) : base(deserializing) { }
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41 | private PotvinInsertionBasedCrossover(PotvinInsertionBasedCrossover original, Cloner cloner)
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42 | : base(original, cloner) {
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43 | }
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44 | public override IDeepCloneable Clone(Cloner cloner) {
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45 | return new PotvinInsertionBasedCrossover(this, cloner);
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46 | }
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47 | public PotvinInsertionBasedCrossover()
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48 | : base() {
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49 | Parameters.Add(new ValueParameter<IntValue>("Length", "The maximum length of the replaced route.", new IntValue(1)));
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50 | }
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51 |
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52 | private static int SelectRandomTourBiasedByLength(IRandom random, PotvinEncoding individual) {
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53 | int tourIndex = -1;
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54 |
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55 | double sum = 0.0;
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56 | double[] probabilities = new double[individual.Tours.Count];
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57 | for (int i = 0; i < individual.Tours.Count; i++) {
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58 | probabilities[i] = 1.0 / ((double)individual.Tours[i].Stops.Count / (double)individual.Cities);
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59 | sum += probabilities[i];
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60 | }
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61 |
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62 | double rand = random.NextDouble() * sum;
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63 | double cumulatedProbabilities = 0.0;
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64 | int index = 0;
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65 | while (tourIndex == -1 && index < probabilities.Length) {
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66 | if (cumulatedProbabilities <= rand && rand <= cumulatedProbabilities + probabilities[index])
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67 | tourIndex = index;
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68 |
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69 | cumulatedProbabilities += probabilities[index];
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70 | index++;
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71 | }
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72 |
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73 | return tourIndex;
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74 | }
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75 |
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76 | private double CalculateCentroidDistance(Tour t1, Tour t2, DoubleMatrix coordinates) {
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77 | double xSum = 0;
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78 | double ySum = 0;
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79 | double c1X, c1Y, c2X, c2Y;
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80 |
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81 | for (int i = 0; i < t1.Stops.Count; i++) {
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82 | xSum += coordinates[t1.Stops[i], 0];
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83 | ySum += coordinates[t1.Stops[i], 1];
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84 | }
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85 | c1X = xSum / t1.Stops.Count;
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86 | c1Y = ySum / t1.Stops.Count;
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87 |
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88 | for (int i = 0; i < t2.Stops.Count; i++) {
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89 | xSum += coordinates[t2.Stops[i], 0];
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90 | ySum += coordinates[t2.Stops[i], 1];
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91 | }
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92 | c2X = xSum / t1.Stops.Count;
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93 | c2Y = ySum / t1.Stops.Count;
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94 |
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95 | return Math.Sqrt(
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96 | (c1X - c2X) * (c1X - c2X) +
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97 | (c1Y - c2Y) * (c1Y - c2Y));
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98 | }
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99 |
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100 | private double CalculateMeanCentroidDistance(Tour t1, IList<Tour> tours, DoubleMatrix coordinates) {
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101 | double sum = 0;
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102 |
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103 | for (int i = 0; i < tours.Count; i++) {
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104 | sum += CalculateCentroidDistance(t1, tours[i], coordinates);
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105 | }
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106 |
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107 | return sum / tours.Count;
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108 | }
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109 |
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110 | private int SelectCityBiasedByNeighborDistance(IRandom random, Tour tour) {
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111 | int cityIndex = -1;
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112 |
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113 | double sum = 0.0;
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114 | double[] probabilities = new double[tour.Stops.Count];
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115 | for (int i = 0; i < tour.Stops.Count; i++) {
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116 | int next;
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117 | if (i + 1 >= tour.Stops.Count)
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118 | next = 0;
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119 | else
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120 | next = tour.Stops[i + 1];
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121 | double distance = ProblemInstance.GetDistance(
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122 | tour.Stops[i], next);
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123 |
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124 | int prev;
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125 | if (i - 1 < 0)
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126 | prev = 0;
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127 | else
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128 | prev = tour.Stops[i - 1];
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129 | distance += ProblemInstance.GetDistance(
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130 | tour.Stops[i], prev);
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131 |
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132 | probabilities[i] = distance;
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133 | sum += probabilities[i];
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134 | }
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135 |
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136 | double rand = random.NextDouble() * sum;
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137 | double cumulatedProbabilities = 0.0;
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138 | int index = 0;
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139 | while (cityIndex == -1 && index < probabilities.Length) {
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140 | if (cumulatedProbabilities <= rand && rand <= cumulatedProbabilities + probabilities[index])
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141 | cityIndex = index;
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142 |
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143 | cumulatedProbabilities += probabilities[index];
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144 | index++;
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145 | }
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146 |
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147 | return cityIndex;
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148 | }
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149 |
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150 | private bool FindRouteInsertionPlace(
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151 | Tour tour,
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152 | int city, bool allowInfeasible, out int place) {
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153 | place = -1;
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154 |
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155 | if (tour.Stops.Contains(city))
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156 | return false;
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157 |
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158 | if (tour.Stops.Count == 0) {
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159 | place = 0;
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160 | return true;
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161 | }
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162 |
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163 | double minDetour = 0;
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164 | VRPEvaluation eval = ProblemInstance.Evaluate(tour);
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165 | bool originalFeasible = ProblemInstance.Feasible(eval);
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166 |
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167 | for (int i = 0; i <= tour.Stops.Count; i++) {
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168 | bool feasible;
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169 | double detour = ProblemInstance.GetInsertionCosts(eval, city, 0, i, out feasible);
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170 | if (feasible || allowInfeasible) {
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171 | if (place < 0 || detour < minDetour) {
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172 | place = i;
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173 | minDetour = detour;
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174 | }
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175 | }
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176 | }
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177 |
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178 | return place >= 0;
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179 | }
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180 |
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181 | private ICollection<int> GetUnrouted(PotvinEncoding solution, int cities) {
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182 | HashSet<int> undiscovered = new HashSet<int>();
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183 | for (int i = 1; i <= cities; i++) {
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184 | undiscovered.Add(i);
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185 | }
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186 |
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187 | foreach (Tour tour in solution.Tours) {
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188 | foreach (int city in tour.Stops)
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189 | undiscovered.Remove(city);
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190 | }
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191 |
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192 | return undiscovered;
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193 | }
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194 |
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195 | protected override PotvinEncoding Crossover(IRandom random, PotvinEncoding parent1, PotvinEncoding parent2) {
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196 | PotvinEncoding child = new PotvinEncoding(ProblemInstance);
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197 |
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198 | bool allowInfeasible = AllowInfeasibleSolutions.Value.Value;
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199 |
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200 | List<Tour> R1 = new List<Tour>();
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201 | PotvinEncoding p1Clone = parent1.Clone() as PotvinEncoding;
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202 |
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203 | int length = Math.Min(Length.Value.Value, parent1.Tours.Count) + 1;
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204 | int k = random.Next(1, length);
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205 | for (int i = 0; i < k; i++) {
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206 | int index = SelectRandomTourBiasedByLength(random, p1Clone);
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207 | R1.Add(p1Clone.Tours[index]);
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208 | p1Clone.Tours.RemoveAt(index);
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209 | }
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210 |
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211 | foreach (Tour r1 in R1) {
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212 | List<int> R2 = new List<int>();
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213 |
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214 | double r = CalculateMeanCentroidDistance(r1, parent2.Tours, ProblemInstance.Coordinates);
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215 | foreach (Tour tour in parent2.Tours) {
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216 | if (CalculateCentroidDistance(r1, tour, ProblemInstance.Coordinates) <= r) {
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217 | R2.AddRange(tour.Stops);
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218 | }
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219 | }
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220 |
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221 | Tour childTour = new Tour();
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222 | childTour.Stops.AddRange(r1.Stops);
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223 |
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224 | //DESTROY - remove cities from r1
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225 | int removed = random.Next(1, r1.Stops.Count + 1);
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226 | for (int i = 0; i < removed; i++) {
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227 | childTour.Stops.RemoveAt(SelectCityBiasedByNeighborDistance(random, childTour));
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228 | }
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229 |
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230 | //REPAIR - add cities from R2
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231 | int maxCount = random.Next(1, Math.Min(5, R2.Count));
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232 | int count = 0;
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233 |
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234 | while (count < maxCount && R2.Count != 0) {
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235 | PotvinEncoding newChild = child.Clone() as PotvinEncoding;
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236 | newChild.Tours.Add(childTour);
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237 |
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238 | int index = random.Next(R2.Count);
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239 | int city = R2[index];
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240 | R2.RemoveAt(index);
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241 |
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242 | int place = -1;
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243 | bool found = FindRouteInsertionPlace(childTour, city, allowInfeasible, out place);
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244 | if (found) {
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245 | childTour.Stops.Insert(place, city);
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246 |
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247 | if (!Repair(random, child, childTour, ProblemInstance, allowInfeasible)) {
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248 | childTour.Stops.RemoveAt(place);
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249 | } else {
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250 | count++;
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251 | }
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252 | }
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253 | }
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254 |
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255 | child.Tours.Add(childTour);
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256 | Repair(random, child, childTour, ProblemInstance, allowInfeasible);
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257 | }
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258 |
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259 | for (int i = 0; i < p1Clone.Tours.Count; i++) {
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260 | Tour childTour = p1Clone.Tours[i].Clone() as Tour;
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261 | child.Tours.Add(childTour);
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262 | Repair(random, child, childTour, ProblemInstance, allowInfeasible);
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263 | }
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264 |
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265 | //route unrouted customers
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266 | child.Unrouted.AddRange(GetUnrouted(child, ProblemInstance.Cities.Value));
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267 | bool success = RouteUnrouted(child, allowInfeasible);
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268 |
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269 | if (success || allowInfeasible)
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270 | return child;
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271 | else {
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272 | if (random.NextDouble() < 0.5)
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273 | return parent1.Clone() as PotvinEncoding;
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274 | else
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275 | return parent2.Clone() as PotvinEncoding;
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276 | }
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277 | }
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278 | }
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279 | }
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