[15553] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2017 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 System.Threading;
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| 25 | using HeuristicLab.Analysis;
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| 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Core;
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| 28 | using HeuristicLab.Data;
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| 29 | using HeuristicLab.Encodings.IntegerVectorEncoding;
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| 30 | using HeuristicLab.Optimization;
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| 31 | using HeuristicLab.Parameters;
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| 32 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 33 | using HeuristicLab.Random;
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| 34 |
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| 35 | namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms.GRASP {
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| 36 |
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| 37 | /// <summary>
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| 38 | /// This is an implementation of the algorithm described in Mateus, G.R., Resende, M.G.C. & Silva, R.M.A. J Heuristics (2011) 17: 527. https://doi.org/10.1007/s10732-010-9144-0
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| 39 | /// </summary>
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| 40 | [Item("GRASP+PR (GQAP)", "The algorithm implements the Greedy Randomized Adaptive Search Procedure (GRASP) with Path Relinking as described in Mateus, G., Resende, M., and Silva, R. 2011. GRASP with path-relinking for the generalized quadratic assignment problem. Journal of Heuristics 17, Springer Netherlands, pp. 527-565.")]
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| 41 | [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms)]
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| 42 | [StorableClass]
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| 43 | public class GRASP : BasicAlgorithm {
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| 44 |
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| 45 | public override bool SupportsPause {
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| 46 | get { return true; }
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| 47 | }
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| 48 |
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| 49 | public override Type ProblemType {
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| 50 | get { return typeof(GQAP); }
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| 51 | }
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| 52 |
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| 53 | public new GQAP Problem {
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| 54 | get { return (GQAP)base.Problem; }
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| 55 | set { base.Problem = value; }
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| 56 | }
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| 57 |
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| 58 | [Storable]
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| 59 | private ValueParameter<IAnalyzer> analyzerParameter;
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| 60 | public IValueParameter<IAnalyzer> AnalyzerParameter {
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| 61 | get { return analyzerParameter; }
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| 62 | }
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| 63 |
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| 64 | [Storable]
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| 65 | private FixedValueParameter<BoolValue> setSeedRandomlyParameter;
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| 66 | private IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
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| 67 | get { return setSeedRandomlyParameter; }
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| 68 | }
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| 69 | [Storable]
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| 70 | private FixedValueParameter<IntValue> seedParameter;
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| 71 | private IFixedValueParameter<IntValue> SeedParameter {
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| 72 | get { return seedParameter; }
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| 73 | }
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| 74 | [Storable]
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| 75 | private FixedValueParameter<IntValue> eliteSetSizeParameter;
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| 76 | private IFixedValueParameter<IntValue> EliteSetSizeParameter {
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| 77 | get { return eliteSetSizeParameter; }
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| 78 | }
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| 79 | [Storable]
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| 80 | private FixedValueParameter<IntValue> minimiumEliteSetSizeParameter;
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| 81 | public IFixedValueParameter<IntValue> MinimumEliteSetSizeParameter {
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| 82 | get { return minimiumEliteSetSizeParameter; }
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| 83 | }
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| 84 | [Storable]
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| 85 | private FixedValueParameter<IntValue> maximumIterationsParameter;
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| 86 | public IFixedValueParameter<IntValue> MaximumIterationsParameter {
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| 87 | get { return maximumIterationsParameter; }
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| 88 | }
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| 89 | [Storable]
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| 90 | private FixedValueParameter<IntValue> maximumLocalSearchIterationsParameter;
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| 91 | public IFixedValueParameter<IntValue> MaximumLocalSearchIterationsParameter {
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| 92 | get { return maximumIterationsParameter; }
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| 93 | }
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| 94 | [Storable]
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| 95 | private FixedValueParameter<PercentValue> candidateSizeFactorParameter;
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| 96 | public IFixedValueParameter<PercentValue> CandidateSizeFactorParameter {
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| 97 | get { return candidateSizeFactorParameter; }
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| 98 | }
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| 99 | [Storable]
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| 100 | private FixedValueParameter<IntValue> maximumCandidateListSizeParameter;
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| 101 | public IFixedValueParameter<IntValue> MaximumCandidateListSizeParameter {
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| 102 | get { return maximumCandidateListSizeParameter; }
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| 103 | }
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| 104 | [Storable]
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| 105 | private FixedValueParameter<PercentValue> oneMoveProbabilityParameter;
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| 106 | public IFixedValueParameter<PercentValue> OneMoveProbabilityParameter {
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| 107 | get { return oneMoveProbabilityParameter; }
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| 108 | }
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| 109 | [Storable]
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| 110 | private FixedValueParameter<PercentValue> minimumDifferenceParameter;
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| 111 | public IFixedValueParameter<PercentValue> MinimumDifferenceParameter {
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| 112 | get { return minimumDifferenceParameter; }
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| 113 | }
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| 114 |
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| 115 | public bool SetSeedRandomly {
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| 116 | get { return setSeedRandomlyParameter.Value.Value; }
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| 117 | set { setSeedRandomlyParameter.Value.Value = value; }
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| 118 | }
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| 119 | public int Seed {
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| 120 | get { return seedParameter.Value.Value; }
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| 121 | set { seedParameter.Value.Value = value; }
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| 122 | }
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| 123 | public int MinimumEliteSetSize {
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| 124 | get { return minimiumEliteSetSizeParameter.Value.Value; }
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| 125 | set { minimiumEliteSetSizeParameter.Value.Value = value; }
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| 126 | }
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| 127 | public int EliteSetSize {
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| 128 | get { return eliteSetSizeParameter.Value.Value; }
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| 129 | set { eliteSetSizeParameter.Value.Value = value; }
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| 130 | }
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| 131 | public double CandidateSizeFactor {
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| 132 | get { return candidateSizeFactorParameter.Value.Value; }
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| 133 | set { candidateSizeFactorParameter.Value.Value = value; }
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| 134 | }
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| 135 | public int MaximumCandidateListSize {
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| 136 | get { return maximumCandidateListSizeParameter.Value.Value; }
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| 137 | set { maximumCandidateListSizeParameter.Value.Value = value; }
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| 138 | }
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| 139 | public double OneMoveProbability {
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| 140 | get { return oneMoveProbabilityParameter.Value.Value; }
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| 141 | set { oneMoveProbabilityParameter.Value.Value = value; }
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| 142 | }
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| 143 | public double MinimumDifference {
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| 144 | get { return minimumDifferenceParameter.Value.Value; }
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| 145 | set { minimumDifferenceParameter.Value.Value = value; }
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| 146 | }
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| 147 |
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| 148 | [StorableConstructor]
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| 149 | protected GRASP(bool deserializing) : base(deserializing) { }
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| 150 | protected GRASP(GRASP original, Cloner cloner)
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| 151 | : base(original, cloner) {
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| 152 | setSeedRandomlyParameter = cloner.Clone(original.setSeedRandomlyParameter);
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| 153 | seedParameter = cloner.Clone(original.seedParameter);
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| 154 | analyzerParameter = cloner.Clone(original.analyzerParameter);
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| 155 | eliteSetSizeParameter = cloner.Clone(original.eliteSetSizeParameter);
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| 156 | minimiumEliteSetSizeParameter = cloner.Clone(original.minimiumEliteSetSizeParameter);
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| 157 | maximumIterationsParameter = cloner.Clone(original.maximumIterationsParameter);
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| 158 | maximumLocalSearchIterationsParameter = cloner.Clone(original.maximumLocalSearchIterationsParameter);
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| 159 | candidateSizeFactorParameter = cloner.Clone(original.candidateSizeFactorParameter);
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| 160 | maximumCandidateListSizeParameter = cloner.Clone(original.maximumCandidateListSizeParameter);
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| 161 | oneMoveProbabilityParameter = cloner.Clone(original.oneMoveProbabilityParameter);
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| 162 | minimumDifferenceParameter = cloner.Clone(original.minimumDifferenceParameter);
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| 163 | context = cloner.Clone(original.context);
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| 164 | }
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| 165 | public GRASP() {
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| 166 | Parameters.Add(setSeedRandomlyParameter = new FixedValueParameter<BoolValue>("SetSeedRandomly", "Whether to overwrite the seed with a random value each time the algorithm is run.", new BoolValue(true)));
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| 167 | Parameters.Add(seedParameter = new FixedValueParameter<IntValue>("Seed", "The random seed that is used in the stochastic algorithm", new IntValue(0)));
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| 168 | Parameters.Add(analyzerParameter = new ValueParameter<IAnalyzer>("Analyzer", "The analyzers that are used to perform an analysis of the solutions.", new MultiAnalyzer()));
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| 169 | Parameters.Add(eliteSetSizeParameter = new FixedValueParameter<IntValue>("EliteSetSize", "The (maximum) size of the elite set.", new IntValue(10)));
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| 170 | Parameters.Add(minimiumEliteSetSizeParameter = new FixedValueParameter<IntValue>("MinimumEliteSetSize", "(ρ) The minimal size of the elite set, before local search and path relinking are applied.", new IntValue(2)));
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| 171 | Parameters.Add(maximumIterationsParameter = new FixedValueParameter<IntValue>("MaximumIterations", "The number of iterations that the algorithm should run.", new IntValue(1000)));
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| 172 | Parameters.Add(maximumLocalSearchIterationsParameter = new FixedValueParameter<IntValue>("MaximumLocalSearchIteration", "The maximum number of iterations that the approximate local search should run", new IntValue(100)));
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| 173 | Parameters.Add(candidateSizeFactorParameter = new FixedValueParameter<PercentValue>("CandidateSizeFactor", "(η) Determines the size of the set of feasible moves in each path - relinking step relative to the maximum size.A value of 50 % means that only half of all possible moves are considered each step.", new PercentValue(0.5)));
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| 174 | Parameters.Add(maximumCandidateListSizeParameter = new FixedValueParameter<IntValue>("MaximumCandidateListSize", "The maximum number of candidates that should be found in each step.", new IntValue(10)));
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| 175 | Parameters.Add(oneMoveProbabilityParameter = new FixedValueParameter<PercentValue>("OneMoveProbability", "The probability for performing a 1-move, which is the opposite of performing a 2-move.", new PercentValue(.5)));
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| 176 | Parameters.Add(minimumDifferenceParameter = new FixedValueParameter<PercentValue>("MinimumDifference", "The minimum amount of difference between two solutions so that they are both accepted in the elite set.", new PercentValue(1e-7)));
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| 177 | Problem = new GQAP();
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| 178 | }
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| 179 |
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| 180 | public override IDeepCloneable Clone(Cloner cloner) {
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| 181 | return new GRASP(this, cloner);
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| 182 | }
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| 183 |
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| 184 | public override void Prepare() {
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| 185 | base.Prepare();
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| 186 | Results.Clear();
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| 187 | context = null;
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| 188 | }
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| 189 |
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| 190 | [Storable]
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| 191 | private GRASPContext context;
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| 192 |
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| 193 | protected override void Initialize(CancellationToken cancellationToken) {
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| 194 | base.Initialize(cancellationToken);
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| 195 | context = new GRASPContext();
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| 196 | context.Problem = Problem;
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| 197 | context.Scope.Variables.Add(new Variable("Results", Results));
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| 198 |
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| 199 | IExecutionContext ctxt = null;
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| 200 | foreach (var item in Problem.ExecutionContextItems)
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| 201 | ctxt = new Core.ExecutionContext(ctxt, item, context.Scope);
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| 202 | ctxt = new Core.ExecutionContext(ctxt, this, context.Scope);
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| 203 | context.Parent = ctxt;
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| 204 |
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| 205 | if (SetSeedRandomly) {
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| 206 | var rnd = new System.Random();
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| 207 | Seed = rnd.Next();
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| 208 | }
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| 209 | context.Random = new MersenneTwister((uint)Seed);
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| 210 | context.Iterations = 0;
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| 211 | context.EvaluatedSolutions = 0;
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| 212 | context.BestQuality = double.NaN;
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| 213 | context.BestSolution = null;
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| 214 |
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| 215 | context.Initialized = true;
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| 216 |
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| 217 | Results.Add(new Result("Iterations", new IntValue(context.Iterations)));
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| 218 | Results.Add(new Result("EvaluatedSolutions", new IntValue(context.EvaluatedSolutions)));
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| 219 | Results.Add(new Result("BestQuality", new DoubleValue(context.BestQuality)));
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| 220 | Results.Add(new Result("BestSolution", typeof(GQAPSolution)));
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| 221 |
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| 222 | context.RunOperator(analyzerParameter.Value, context.Scope, cancellationToken);
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| 223 | }
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| 224 |
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| 225 | protected override void Run(CancellationToken cancellationToken) {
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| 226 | var eq = new IntegerVectorEqualityComparer();
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| 227 | while (!StoppingCriterion()) { // line 2 in Algorithm 1
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| 228 | // next: line 3 in Algorithm 1
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| 229 | var pi_prime_vec = GreedyRandomizedSolutionCreator.CreateSolution(context.Random, Problem.ProblemInstance, 1000, false, cancellationToken);
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| 230 | if (context.PopulationCount >= MinimumEliteSetSize) { // line 4 in Algorithm 1
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| 231 | GQAPSolution pi_prime;
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| 232 | if (!Problem.ProblemInstance.IsFeasible(pi_prime_vec)) // line 5 in Algorithm 1
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| 233 | pi_prime = context.AtPopulation(context.Random.Next(context.PopulationCount)).Solution; // line 6 in Algorithm 1
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| 234 | else {
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| 235 | // This is necessary, because pi_prime has not been evaluated yet and such details are not covered in Algorithm 1
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| 236 | pi_prime = Problem.ProblemInstance.ToEvaluatedSolution(pi_prime_vec);
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| 237 | context.EvaluatedSolutions++;
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| 238 | }
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| 239 |
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| 240 | ApproxLocalSearch(pi_prime); // line 8 in Algorithm 1
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| 241 | var pi_plus = context.AtPopulation(context.Random.Next(context.PopulationCount)); // line 9 in Algorithm 1
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| 242 | pi_prime = PathRelinking(pi_prime, pi_plus.Solution); // line 10 in Algorithm 1
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| 243 | ApproxLocalSearch(pi_prime); // line 11 in Algorithm 1
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| 244 | var fitness = Problem.ProblemInstance.ToSingleObjective(pi_prime.Evaluation);
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| 245 | // Book-keeping
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| 246 | if (context.BestQuality > fitness) {
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| 247 | context.BestQuality = fitness;
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| 248 | context.BestSolution = (GQAPSolution)pi_prime.Clone();
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| 249 | }
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| 250 |
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| 251 | if (context.PopulationCount == EliteSetSize) { // line 12 in Algorithm 1
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| 252 | var fit = Problem.ProblemInstance.ToSingleObjective(pi_prime.Evaluation);
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| 253 | double[] similarities = context.Population.Select(x => HammingSimilarityCalculator.CalculateSimilarity(x.Solution.Assignment, pi_prime.Assignment)).ToArray();
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| 254 | if (similarities.Max() <= 1.0 - MinimumDifference) { // cond. 2 of line 13 in Algorithm 1
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| 255 | var replacement = context.Population.Select((v, i) => new { V = v, Index = i })
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| 256 | .Where(x => x.V.Fitness >= fit).ToArray();
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| 257 | if (replacement.Length > 0) { // cond. 1 of line 13 in Algorithm 1
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| 258 | // next two lines: line 14 in Algorithm 1
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| 259 | replacement = replacement.OrderBy(x => similarities[x.Index]).ToArray();
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| 260 | context.ReplaceAtPopulation(replacement.Last().Index, context.ToScope(pi_prime, fit));
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| 261 | }
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| 262 | }
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| 263 | } else if (IsSufficientlyDifferent(pi_prime.Assignment)) { // line 17 in Algorithm 1
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| 264 | context.AddToPopulation(context.ToScope(pi_prime, Problem.ProblemInstance.ToSingleObjective(pi_prime.Evaluation))); // line 18 in Algorithm 1
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| 265 | }
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| 266 | } else if (Problem.ProblemInstance.IsFeasible(pi_prime_vec) /* cond. 1 of line 21 in Algorithm 1 */
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| 267 | && IsSufficientlyDifferent(pi_prime_vec)) /* cond. 2 of line 21 in Algorithm 1 */ {
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| 268 | var pi_prime = Problem.ProblemInstance.ToEvaluatedSolution(pi_prime_vec);
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| 269 | context.EvaluatedSolutions++;
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| 270 | var fitness = Problem.ProblemInstance.ToSingleObjective(pi_prime.Evaluation);
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| 271 | context.AddToPopulation(context.ToScope(pi_prime, fitness)); /* line 22 in Algorithm 1 */
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| 272 | // Book-keeping
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| 273 | if (context.PopulationCount == 1 || context.BestQuality > fitness) {
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| 274 | context.BestQuality = fitness;
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| 275 | context.BestSolution = (GQAPSolution)pi_prime.Clone();
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| 276 | }
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| 277 | }
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| 278 |
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| 279 | context.Iterations++;
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| 280 |
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| 281 | IResult result;
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| 282 | if (Results.TryGetValue("Iterations", out result))
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| 283 | ((IntValue)result.Value).Value = context.Iterations;
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| 284 | else Results.Add(new Result("Iterations", new IntValue(context.Iterations)));
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| 285 | if (Results.TryGetValue("EvaluatedSolutions", out result))
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| 286 | ((IntValue)result.Value).Value = context.EvaluatedSolutions;
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| 287 | else Results.Add(new Result("EvaluatedSolutions", new IntValue(context.EvaluatedSolutions)));
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| 288 | if (Results.TryGetValue("BestQuality", out result))
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| 289 | ((DoubleValue)result.Value).Value = context.BestQuality;
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| 290 | else Results.Add(new Result("BestQuality", new DoubleValue(context.BestQuality)));
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| 291 | if (Results.TryGetValue("BestSolution", out result))
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| 292 | result.Value = context.BestSolution;
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| 293 | else Results.Add(new Result("BestSolution", context.BestSolution));
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| 294 |
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| 295 | context.RunOperator(analyzerParameter.Value, context.Scope, cancellationToken);
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| 296 | }
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| 297 | }
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| 298 |
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| 299 | private bool IsSufficientlyDifferent(IntegerVector vec) {
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| 300 | return context.Population.All(x =>
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| 301 | HammingSimilarityCalculator.CalculateSimilarity(x.Solution.Assignment, vec) <= 1.0 - MinimumDifference
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| 302 | );
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| 303 | }
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| 304 |
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| 305 | private GQAPSolution PathRelinking(GQAPSolution pi_prime, GQAPSolution pi_plus) {
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| 306 | // Following code represents line 1 of Algorithm 4
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| 307 | IntegerVector source = pi_prime.Assignment, target = pi_plus.Assignment;
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| 308 | Evaluation sourceEval = pi_prime.Evaluation, targetEval = pi_plus.Evaluation;
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| 309 | var sourceFit = Problem.ProblemInstance.ToSingleObjective(sourceEval);
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| 310 | var targetFit = Problem.ProblemInstance.ToSingleObjective(targetEval);
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| 311 | if (targetFit < sourceFit) {
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| 312 | var h = source;
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| 313 | source = target;
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| 314 | target = h;
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| 315 | var hh = sourceEval;
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| 316 | sourceEval = targetEval;
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| 317 | targetEval = hh;
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| 318 | }
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| 319 | int evaluatedSolutions;
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| 320 | // lines 2-36 of Algorithm 4 are implemented in the following call
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| 321 | var pi_star = GQAPPathRelinking.Apply(context.Random, source, sourceEval,
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| 322 | target, targetEval, Problem.ProblemInstance, CandidateSizeFactor,
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| 323 | out evaluatedSolutions);
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| 324 | context.EvaluatedSolutions += evaluatedSolutions;
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| 325 | return pi_star;
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| 326 | }
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| 327 |
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| 328 | private void ApproxLocalSearch(GQAPSolution pi_prime) {
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| 329 | var localSearchEvaluations = 0;
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| 330 | ApproximateLocalSearch.Apply(context.Random, pi_prime, MaximumCandidateListSize,
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| 331 | OneMoveProbability, 1000, Problem.ProblemInstance, out localSearchEvaluations);
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| 332 | context.EvaluatedSolutions += localSearchEvaluations;
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| 333 | }
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| 334 |
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| 335 | private bool StoppingCriterion() {
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| 336 | return context.Iterations > MaximumIterationsParameter.Value.Value;
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| 337 | }
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| 338 | }
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| 339 | }
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