1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2018 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.Collections.Generic;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Optimization;
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26 | using HeuristicLab.Parameters;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 | using HeuristicLab.Problems.VehicleRouting.Encodings.General;
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29 | using HeuristicLab.Problems.VehicleRouting.Interfaces;
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30 |
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31 | namespace HeuristicLab.Problems.VehicleRouting.Encodings.GVR {
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32 | [Item("GVRCrossover", "The GVR crossover operation. It is implemented as described in Pereira, F.B. et al (2002). GVR: a New Genetic Representation for the Vehicle Routing Problem. AICS 2002, LNAI 2464, pp. 95-102.")]
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33 | [StorableClass]
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34 | public sealed class GVRCrossover : VRPCrossover, IStochasticOperator, IGVROperator {
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35 | public ILookupParameter<IRandom> RandomParameter {
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36 | get { return (LookupParameter<IRandom>)Parameters["Random"]; }
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37 | }
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38 |
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39 | [StorableConstructor]
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40 | private GVRCrossover(bool deserializing) : base(deserializing) { }
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41 |
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42 | public GVRCrossover() {
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43 | Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used for stochastic manipulation operators."));
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44 | }
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45 |
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46 | public override IDeepCloneable Clone(Cloner cloner) {
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47 | return new GVRCrossover(this, cloner);
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48 | }
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49 |
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50 | private GVRCrossover(GVRCrossover original, Cloner cloner)
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51 | : base(original, cloner) {
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52 | }
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53 |
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54 | private GVREncoding Crossover(IRandom random, GVREncoding parent1, GVREncoding parent2) {
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55 | GVREncoding child = parent1.Clone() as GVREncoding;
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56 |
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57 | Tour tour = parent2.Tours[random.Next(parent2.Tours.Count)];
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58 | int breakPoint1 = random.Next(tour.Stops.Count);
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59 | int length = random.Next(1, tour.Stops.Count - breakPoint1 + 1);
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60 | List<int> subroute = tour.Stops.GetRange(breakPoint1, length);
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61 |
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62 | //remove duplicates
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63 | List<Tour> toBeRemoved = new List<Tour>();
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64 |
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65 | foreach (Tour route in child.Tours) {
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66 | foreach (int city in subroute) {
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67 | route.Stops.Remove(city);
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68 | }
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69 |
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70 | if (route.Stops.Count == 0)
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71 | toBeRemoved.Add(route);
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72 | }
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73 | foreach (Tour route in toBeRemoved) {
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74 | child.Tours.Remove(route);
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75 | }
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76 |
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77 | //choose nearest customer
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78 | double minDistance = -1;
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79 | int customer = -1;
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80 | for (int i = 1; i <= ProblemInstance.Cities.Value; i++) {
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81 | if (!subroute.Contains(i)) {
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82 | double distance = ProblemInstance.GetDistance(subroute[0], i, child);
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83 |
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84 | if (customer == -1 || distance < minDistance) {
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85 | customer = i;
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86 | minDistance = distance;
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87 | }
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88 | }
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89 | }
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90 |
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91 | //insert
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92 | if (customer != -1) {
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93 | Tour newTour;
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94 | int newPosition;
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95 | child.FindCustomer(customer, out newTour, out newPosition);
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96 | newTour.Stops.InsertRange(newPosition + 1, subroute);
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97 | } else {
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98 | //special case -> only one tour, whole tour has been chosen as subroute
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99 | child = parent1.Clone() as GVREncoding;
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100 | }
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101 |
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102 | return child;
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103 | }
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104 |
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105 | public override IOperation InstrumentedApply() {
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106 | ItemArray<IVRPEncoding> parents = new ItemArray<IVRPEncoding>(ParentsParameter.ActualValue.Length);
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107 | for (int i = 0; i < ParentsParameter.ActualValue.Length; i++) {
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108 | IVRPEncoding solution = ParentsParameter.ActualValue[i];
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109 | if (!(solution is GVREncoding)) {
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110 | parents[i] = GVREncoding.ConvertFrom(solution, ProblemInstance);
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111 | } else {
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112 | parents[i] = solution;
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113 | }
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114 | }
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115 | ParentsParameter.ActualValue = parents;
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116 |
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117 | ChildParameter.ActualValue = Crossover(RandomParameter.ActualValue, parents[0] as GVREncoding, parents[1] as GVREncoding);
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118 |
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119 | return base.InstrumentedApply();
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120 | }
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121 | }
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122 | }
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