[4379] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 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.Core;
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| 23 | using HeuristicLab.Encodings.PermutationEncoding;
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| 24 | using HeuristicLab.Parameters;
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| 25 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 26 | using HeuristicLab.Data;
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| 27 |
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| 28 | namespace HeuristicLab.Problems.VehicleRouting.Encodings.Zhu {
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| 29 | [Item("ZhuHeuristicCrossover1", "The Zhu Heuristic Crossover (Version 1). It is implemented as described in Zhu, K.Q. (2000). A New Genetic Algorithm For VRPTW. Proceedings of the International Conference on Artificial Intelligence.")]
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| 30 | [StorableClass]
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| 31 | public sealed class ZhuHeuristicCrossover1 : ZhuCrossover {
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| 32 | [StorableConstructor]
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| 33 | private ZhuHeuristicCrossover1(bool deserializing) : base(deserializing) { }
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| 34 |
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| 35 | public ZhuHeuristicCrossover1()
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| 36 | : base() {
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| 37 | }
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| 38 |
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| 39 | private void Swap(ZhuEncoding individual, int city1, int city2) {
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| 40 | int index1 = individual.IndexOf(city1);
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| 41 | int index2 = individual.IndexOf(city2);
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| 42 |
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| 43 | int temp = individual[index1];
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| 44 | individual[index1] = individual[index2];
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| 45 | individual[index2] = temp;
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| 46 | }
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| 47 |
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| 48 | protected override ZhuEncoding Crossover(IRandom random, ZhuEncoding parent1, ZhuEncoding parent2) {
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| 49 | parent1 = parent1.Clone() as ZhuEncoding;
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| 50 | parent2 = parent2.Clone() as ZhuEncoding;
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| 51 |
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| 52 | ZhuEncoding child = parent2.Clone() as ZhuEncoding;
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| 53 |
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| 54 | int breakPoint = random.Next(child.Length);
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| 55 | int i = breakPoint;
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| 56 | int predecessor = breakPoint - 1;
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| 57 | if (predecessor < 0)
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| 58 | predecessor = predecessor + child.Length;
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| 59 |
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| 60 | while (i != predecessor) {
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| 61 | if (i == breakPoint) {
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| 62 | child[i] = parent1[i];
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| 63 | Swap(parent2, parent2[i], parent1[i]);
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| 64 | }
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| 65 |
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| 66 | if (ProblemInstance.GetDistance(
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| 67 | child[i] + 1, parent1[(i + 1) % child.Length] + 1)
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| 68 | <
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| 69 | ProblemInstance.GetDistance(
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| 70 | child[i] + 1, parent2[(i + 1) % child.Length] + 1)) {
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| 71 | child[(i + 1) % child.Length] = parent1[(i + 1) % child.Length];
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| 72 | Swap(parent2, parent2[(i + 1) % child.Length], parent1[(i + 1) % child.Length]);
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| 73 | } else {
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| 74 | child[(i + 1) % child.Length] = parent2[(i + 1) % child.Length];
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| 75 | Swap(parent1, parent1[(i + 1) % child.Length], parent2[(i + 1) % child.Length]);
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| 76 | }
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| 77 |
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| 78 | i = (i + 1) % child.Length;
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| 79 | }
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| 80 |
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| 81 | return child;
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| 82 | }
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| 83 | }
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| 84 | }
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