1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2019 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 HEAL.Attic;
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23 | using HeuristicLab.Analysis;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.ExpressionGenerator;
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28 | using HeuristicLab.Optimization;
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29 | using HeuristicLab.Parameters;
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30 | using HeuristicLab.Problems.DataAnalysis;
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31 | using HeuristicLab.Random;
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32 | using System;
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33 | using System.Collections.Generic;
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34 | using System.Linq;
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35 | using CancellationToken = System.Threading.CancellationToken;
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36 |
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37 | namespace HeuristicLab.Algorithms.MOEAD {
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38 | [Item("MOEADAlgorithmBase", "Base class for all MOEA/D algorithm variants.")]
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39 | [StorableType("E00BAC79-C6F9-42B6-8468-DEEC7FFCE804")]
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40 | public abstract class MOEADAlgorithmBase : BasicAlgorithm {
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41 | #region data members
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42 | protected enum NeighborType { NEIGHBOR, POPULATION }
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43 | // TCHE = Chebyshev (Tchebyshev)
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44 | // PBI = Penalty-based boundary intersection
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45 | // AGG = Weighted sum
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46 | public enum FunctionType { TCHE, PBI, AGG }
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47 |
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48 | [Storable]
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49 | protected double[] IdealPoint { get; set; }
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50 | [Storable]
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51 | protected double[] NadirPoint { get; set; } // potentially useful for objective normalization
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52 |
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53 | [Storable]
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54 | protected double[][] lambda;
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55 |
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56 | [Storable]
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57 | protected int[][] neighbourhood;
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58 |
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59 | [Storable]
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60 | protected IList<IMOEADSolution> solutions;
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61 |
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62 | [Storable]
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63 | protected FunctionType functionType;
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64 |
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65 | [Storable]
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66 | protected List<IMOEADSolution> population;
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67 |
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68 | [Storable]
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69 | protected List<IMOEADSolution> offspringPopulation;
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70 |
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71 | [Storable]
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72 | protected List<IMOEADSolution> jointPopulation;
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73 |
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74 | [Storable]
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75 | protected int evaluatedSolutions;
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76 |
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77 | [Storable]
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78 | protected ExecutionContext executionContext;
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79 |
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80 | [Storable]
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81 | protected IScope globalScope;
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82 |
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83 | [Storable]
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84 | protected ExecutionState previousExecutionState;
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85 |
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86 | [Storable]
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87 | protected ExecutionState executionState;
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88 | #endregion
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89 |
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90 | #region parameters
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91 | private const string SeedParameterName = "Seed";
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92 | private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
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93 | private const string PopulationSizeParameterName = "PopulationSize";
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94 | private const string ResultPopulationSizeParameterName = "ResultPopulationSize";
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95 | private const string CrossoverProbabilityParameterName = "CrossoverProbability";
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96 | private const string CrossoverParameterName = "Crossover";
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97 | private const string MutationProbabilityParameterName = "MutationProbability";
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98 | private const string MutatorParameterName = "Mutator";
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99 | private const string MaximumEvaluatedSolutionsParameterName = "MaximumEvaluatedSolutions";
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100 | private const string RandomParameterName = "Random";
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101 | private const string AnalyzerParameterName = "Analyzer";
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102 | // MOEA-D parameters
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103 | private const string NeighbourSizeParameterName = "NeighbourSize";
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104 | private const string NeighbourhoodSelectionProbabilityParameterName = "NeighbourhoodSelectionProbability";
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105 | private const string MaximumNumberOfReplacedSolutionsParameterName = "MaximumNumberOfReplacedSolutions";
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106 | private const string FunctionTypeParameterName = "FunctionType";
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107 | private const string NormalizeObjectivesParameterName = "NormalizeObjectives";
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108 |
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109 | public IValueParameter<MultiAnalyzer> AnalyzerParameter {
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110 | get { return (ValueParameter<MultiAnalyzer>)Parameters[AnalyzerParameterName]; }
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111 | }
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112 |
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113 | public IConstrainedValueParameter<StringValue> FunctionTypeParameter {
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114 | get { return (IConstrainedValueParameter<StringValue>)Parameters[FunctionTypeParameterName]; }
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115 | }
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116 | public IFixedValueParameter<IntValue> NeighbourSizeParameter {
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117 | get { return (IFixedValueParameter<IntValue>)Parameters[NeighbourSizeParameterName]; }
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118 | }
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119 | public IFixedValueParameter<BoolValue> NormalizeObjectivesParameter {
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120 | get { return (IFixedValueParameter<BoolValue>)Parameters[NormalizeObjectivesParameterName]; }
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121 | }
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122 | public IFixedValueParameter<IntValue> MaximumNumberOfReplacedSolutionsParameter {
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123 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumNumberOfReplacedSolutionsParameterName]; }
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124 | }
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125 | public IFixedValueParameter<DoubleValue> NeighbourhoodSelectionProbabilityParameter {
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126 | get { return (IFixedValueParameter<DoubleValue>)Parameters[NeighbourhoodSelectionProbabilityParameterName]; }
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127 | }
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128 | public IFixedValueParameter<IntValue> SeedParameter {
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129 | get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
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130 | }
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131 | public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
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132 | get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
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133 | }
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134 | private IValueParameter<IntValue> PopulationSizeParameter {
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135 | get { return (IValueParameter<IntValue>)Parameters[PopulationSizeParameterName]; }
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136 | }
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137 | private IValueParameter<IntValue> ResultPopulationSizeParameter {
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138 | get { return (IValueParameter<IntValue>)Parameters[ResultPopulationSizeParameterName]; }
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139 | }
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140 | public IValueParameter<PercentValue> CrossoverProbabilityParameter {
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141 | get { return (IValueParameter<PercentValue>)Parameters[CrossoverProbabilityParameterName]; }
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142 | }
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143 | public IConstrainedValueParameter<ICrossover> CrossoverParameter {
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144 | get { return (IConstrainedValueParameter<ICrossover>)Parameters[CrossoverParameterName]; }
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145 | }
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146 | public IValueParameter<PercentValue> MutationProbabilityParameter {
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147 | get { return (IValueParameter<PercentValue>)Parameters[MutationProbabilityParameterName]; }
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148 | }
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149 | public IConstrainedValueParameter<IManipulator> MutatorParameter {
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150 | get { return (IConstrainedValueParameter<IManipulator>)Parameters[MutatorParameterName]; }
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151 | }
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152 | public IValueParameter<IntValue> MaximumEvaluatedSolutionsParameter {
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153 | get { return (IValueParameter<IntValue>)Parameters[MaximumEvaluatedSolutionsParameterName]; }
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154 | }
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155 | public IValueParameter<IRandom> RandomParameter {
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156 | get { return (IValueParameter<IRandom>)Parameters[RandomParameterName]; }
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157 | }
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158 | #endregion
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159 |
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160 | #region parameter properties
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161 | public new IMultiObjectiveHeuristicOptimizationProblem Problem {
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162 | get { return (IMultiObjectiveHeuristicOptimizationProblem)base.Problem; }
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163 | }
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164 | public int Seed {
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165 | get { return SeedParameter.Value.Value; }
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166 | set { SeedParameter.Value.Value = value; }
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167 | }
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168 | public bool SetSeedRandomly {
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169 | get { return SetSeedRandomlyParameter.Value.Value; }
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170 | set { SetSeedRandomlyParameter.Value.Value = value; }
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171 | }
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172 | public bool NormalizeObjectives {
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173 | get { return NormalizeObjectivesParameter.Value.Value; }
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174 | set { NormalizeObjectivesParameter.Value.Value = value; }
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175 | }
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176 | public IntValue PopulationSize {
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177 | get { return PopulationSizeParameter.Value; }
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178 | set { PopulationSizeParameter.Value = value; }
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179 | }
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180 | public IntValue ResultPopulationSize {
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181 | get { return ResultPopulationSizeParameter.Value; }
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182 | set { ResultPopulationSizeParameter.Value = value; }
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183 | }
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184 | public PercentValue CrossoverProbability {
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185 | get { return CrossoverProbabilityParameter.Value; }
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186 | set { CrossoverProbabilityParameter.Value = value; }
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187 | }
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188 | public ICrossover Crossover {
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189 | get { return CrossoverParameter.Value; }
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190 | set { CrossoverParameter.Value = value; }
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191 | }
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192 | public PercentValue MutationProbability {
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193 | get { return MutationProbabilityParameter.Value; }
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194 | set { MutationProbabilityParameter.Value = value; }
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195 | }
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196 | public IManipulator Mutator {
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197 | get { return MutatorParameter.Value; }
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198 | set { MutatorParameter.Value = value; }
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199 | }
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200 | public MultiAnalyzer Analyzer {
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201 | get { return AnalyzerParameter.Value; }
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202 | set { AnalyzerParameter.Value = value; }
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203 | }
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204 | public IntValue MaximumEvaluatedSolutions {
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205 | get { return MaximumEvaluatedSolutionsParameter.Value; }
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206 | set { MaximumEvaluatedSolutionsParameter.Value = value; }
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207 | }
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208 | public int NeighbourSize {
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209 | get { return NeighbourSizeParameter.Value.Value; }
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210 | set { NeighbourSizeParameter.Value.Value = value; }
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211 | }
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212 | public int MaximumNumberOfReplacedSolutions {
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213 | get { return MaximumNumberOfReplacedSolutionsParameter.Value.Value; }
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214 | set { MaximumNumberOfReplacedSolutionsParameter.Value.Value = value; }
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215 | }
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216 | public double NeighbourhoodSelectionProbability {
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217 | get { return NeighbourhoodSelectionProbabilityParameter.Value.Value; }
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218 | set { NeighbourhoodSelectionProbabilityParameter.Value.Value = value; }
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219 | }
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220 | #endregion
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221 |
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222 | #region constructors
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223 | public MOEADAlgorithmBase() {
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224 | Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
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225 | Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
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226 | Parameters.Add(new ValueParameter<IntValue>(PopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
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227 | Parameters.Add(new ValueParameter<IntValue>(ResultPopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
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228 | Parameters.Add(new ValueParameter<PercentValue>(CrossoverProbabilityParameterName, "The probability that the crossover operator is applied.", new PercentValue(0.9)));
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229 | Parameters.Add(new ConstrainedValueParameter<ICrossover>(CrossoverParameterName, "The operator used to cross solutions."));
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230 | Parameters.Add(new ValueParameter<PercentValue>(MutationProbabilityParameterName, "The probability that the mutation operator is applied on a solution.", new PercentValue(0.25)));
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231 | Parameters.Add(new ConstrainedValueParameter<IManipulator>(MutatorParameterName, "The operator used to mutate solutions."));
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232 | Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
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233 | Parameters.Add(new ValueParameter<IntValue>(MaximumEvaluatedSolutionsParameterName, "The maximum number of evaluated solutions (approximately).", new IntValue(100_000)));
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234 | Parameters.Add(new ValueParameter<IRandom>(RandomParameterName, new MersenneTwister()));
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235 | Parameters.Add(new FixedValueParameter<IntValue>(NeighbourSizeParameterName, new IntValue(20)));
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236 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumNumberOfReplacedSolutionsParameterName, new IntValue(2)));
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237 | Parameters.Add(new FixedValueParameter<DoubleValue>(NeighbourhoodSelectionProbabilityParameterName, new DoubleValue(0.1)));
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238 | Parameters.Add(new FixedValueParameter<BoolValue>(NormalizeObjectivesParameterName, new BoolValue(true)));
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239 |
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240 | var functionTypeParameter = new ConstrainedValueParameter<StringValue>(FunctionTypeParameterName);
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241 | foreach (var s in new[] { "Chebyshev", "PBI", "Weighted Sum" }) {
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242 | functionTypeParameter.ValidValues.Add(new StringValue(s));
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243 | }
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244 | Parameters.Add(functionTypeParameter);
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245 | }
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246 |
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247 | protected MOEADAlgorithmBase(MOEADAlgorithmBase original, Cloner cloner) : base(original, cloner) {
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248 | functionType = original.functionType;
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249 | evaluatedSolutions = original.evaluatedSolutions;
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250 | previousExecutionState = original.previousExecutionState;
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251 |
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252 | if (original.IdealPoint != null) {
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253 | IdealPoint = (double[])original.IdealPoint.Clone();
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254 | }
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255 |
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256 | if (original.NadirPoint != null) {
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257 | NadirPoint = (double[])original.NadirPoint.Clone();
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258 | }
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259 |
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260 | if (original.lambda != null) {
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261 | lambda = (double[][])original.lambda.Clone();
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262 | }
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263 |
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264 | if (original.neighbourhood != null) {
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265 | neighbourhood = (int[][])original.neighbourhood.Clone();
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266 | }
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267 |
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268 | if (original.solutions != null) {
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269 | solutions = original.solutions.Select(cloner.Clone).ToArray();
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270 | }
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271 |
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272 | if (original.population != null) {
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273 | population = original.population.Select(cloner.Clone).ToList();
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274 | }
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275 |
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276 | if (original.offspringPopulation != null) {
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277 | offspringPopulation = original.offspringPopulation.Select(cloner.Clone).ToList();
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278 | }
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279 |
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280 | if (original.jointPopulation != null) {
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281 | jointPopulation = original.jointPopulation.Select(x => cloner.Clone(x)).ToList();
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282 | }
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283 |
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284 | if (original.executionContext != null) {
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285 | executionContext = cloner.Clone(original.executionContext);
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286 | }
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287 |
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288 | if (original.globalScope != null) {
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289 | globalScope = cloner.Clone(original.globalScope);
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290 | }
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291 | }
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292 |
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293 |
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294 | [StorableHook(HookType.AfterDeserialization)]
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295 | private void AfterDeserialization() {
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296 | if (!Parameters.ContainsKey(NormalizeObjectivesParameterName)) {
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297 | Parameters.Add(new FixedValueParameter<BoolValue>(NormalizeObjectivesParameterName, new BoolValue(true)));
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298 | }
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299 | }
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300 |
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301 | [StorableConstructor]
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302 | protected MOEADAlgorithmBase(StorableConstructorFlag _) : base(_) { }
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303 | #endregion
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304 |
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305 | private void InitializePopulation(ExecutionContext executionContext, CancellationToken cancellationToken, IRandom random, bool[] maximization) {
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306 | var creator = Problem.SolutionCreator;
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307 | var evaluator = Problem.Evaluator;
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308 |
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309 | var dimensions = maximization.Length;
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310 | var populationSize = PopulationSize.Value;
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311 | population = new List<IMOEADSolution>(populationSize);
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312 |
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313 | var parentScope = executionContext.Scope;
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314 | // first, create all individuals
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315 | for (int i = 0; i < populationSize; ++i) {
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316 | var childScope = new Scope(i.ToString()) { Parent = parentScope };
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317 | ExecuteOperation(executionContext, cancellationToken, executionContext.CreateChildOperation(creator, childScope));
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318 | parentScope.SubScopes.Add(childScope);
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319 | }
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320 |
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321 | // then, evaluate them and update qualities
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322 | for (int i = 0; i < populationSize; ++i) {
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323 | var childScope = parentScope.SubScopes[i];
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324 | ExecuteOperation(executionContext, cancellationToken, executionContext.CreateChildOperation(evaluator, childScope));
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325 |
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326 | var qualities = (DoubleArray)childScope.Variables["Qualities"].Value;
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327 | var solution = new MOEADSolution(childScope, dimensions, 0);
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328 | for (int j = 0; j < dimensions; ++j) {
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329 | solution.Qualities[j] = maximization[j] ? 1 - qualities[j] : qualities[j];
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330 | }
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331 | population.Add(solution);
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332 | }
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333 | }
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334 |
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335 | protected void InitializeAlgorithm(CancellationToken cancellationToken) {
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336 | var rand = RandomParameter.Value;
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337 | if (SetSeedRandomly) Seed = RandomSeedGenerator.GetSeed();
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338 | rand.Reset(Seed);
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339 |
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340 | bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();
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341 | var dimensions = maximization.Length;
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342 |
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343 | var populationSize = PopulationSize.Value;
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344 |
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345 | InitializePopulation(executionContext, cancellationToken, rand, maximization);
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346 | InitializeUniformWeights(rand, populationSize, dimensions);
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347 | InitializeNeighbourHood(lambda, populationSize, NeighbourSize);
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348 |
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349 | //IdealPoint = Enumerable.Repeat(double.MaxValue, dimensions).ToArray();
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350 | IdealPoint = new double[dimensions];
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351 | IdealPoint.UpdateIdeal(population);
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352 |
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353 | //NadirPoint = Enumerable.Repeat(double.MinValue, dimensions).ToArray();
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354 | NadirPoint = new double[dimensions];
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355 | NadirPoint.UpdateNadir(population);
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356 |
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357 | var functionTypeString = FunctionTypeParameter.Value.Value;
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358 | switch (functionTypeString) {
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359 | case "Chebyshev":
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360 | functionType = FunctionType.TCHE;
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361 | break;
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362 | case "PBI":
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363 | functionType = FunctionType.PBI;
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364 | break;
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365 | case "Weighted Sum":
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366 | functionType = FunctionType.AGG;
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367 | break;
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368 | }
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369 |
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370 | evaluatedSolutions = populationSize;
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371 | }
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372 |
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373 | protected override void Initialize(CancellationToken cancellationToken) {
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374 | globalScope = new Scope("Global Scope");
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375 | executionContext = new ExecutionContext(null, this, globalScope);
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376 |
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377 | // set the execution context for parameters to allow lookup
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378 | foreach (var parameter in Problem.Parameters.OfType<IValueParameter>()) {
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379 | // we need all of these in order for the wiring of the operators to work
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380 | globalScope.Variables.Add(new Variable(parameter.Name, parameter.Value));
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381 | }
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382 | globalScope.Variables.Add(new Variable("Results", Results)); // make results available as a parameter for analyzers etc.
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383 |
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384 | base.Initialize(cancellationToken);
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385 | }
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386 |
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387 | public override bool SupportsPause => true;
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388 |
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389 | protected void InitializeUniformWeights(IRandom random, int populationSize, int dimensions) {
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390 | lambda = Enumerable.Range(0, populationSize).Select(_ => GenerateSample(random, dimensions)).ToArray();
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391 | }
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392 |
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393 | // implements random number generation from https://en.wikipedia.org/wiki/Dirichlet_distribution#Random_number_generation
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394 | private double[] GenerateSample(IRandom random, int dim) {
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395 | var sum = 0d;
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396 | var sample = new double[dim];
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397 | for (int i = 0; i < dim; ++i) {
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398 | sample[i] = GammaDistributedRandom.NextDouble(random, 1, 1);
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399 | sum += sample[i];
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400 | }
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401 | for (int i = 0; i < dim; ++i) {
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402 | sample[i] /= sum;
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403 | }
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404 | return sample;
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405 | }
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406 |
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407 | protected void InitializeNeighbourHood(double[][] lambda, int populationSize, int neighbourSize) {
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408 | neighbourhood = new int[populationSize][];
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409 |
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410 | var x = new double[populationSize];
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411 | var idx = new int[populationSize];
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412 |
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413 | for (int i = 0; i < populationSize; ++i) {
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414 | for (int j = 0; j < populationSize; ++j) {
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415 | x[j] = MOEADUtil.EuclideanDistance(lambda[i], lambda[j]);
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416 | idx[j] = j;
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417 | }
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418 |
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419 | MOEADUtil.MinFastSort(x, idx, populationSize, neighbourSize);
|
---|
420 | neighbourhood[i] = (int[])idx.Clone();
|
---|
421 | }
|
---|
422 | }
|
---|
423 |
|
---|
424 | protected NeighborType ChooseNeighborType(IRandom random, double neighbourhoodSelectionProbability) {
|
---|
425 | return random.NextDouble() < neighbourhoodSelectionProbability
|
---|
426 | ? NeighborType.NEIGHBOR
|
---|
427 | : NeighborType.POPULATION;
|
---|
428 | }
|
---|
429 |
|
---|
430 | protected IList<IMOEADSolution> ParentSelection(IRandom random, int subProblemId, NeighborType neighbourType) {
|
---|
431 | List<int> matingPool = MatingSelection(random, subProblemId, 2, neighbourType);
|
---|
432 |
|
---|
433 | var parents = new IMOEADSolution[3];
|
---|
434 |
|
---|
435 | parents[0] = population[matingPool[0]];
|
---|
436 | parents[1] = population[matingPool[1]];
|
---|
437 | parents[2] = population[subProblemId];
|
---|
438 |
|
---|
439 | return parents;
|
---|
440 | }
|
---|
441 |
|
---|
442 | protected List<int> MatingSelection(IRandom random, int subproblemId, int numberOfSolutionsToSelect, NeighborType neighbourType) {
|
---|
443 | int populationSize = PopulationSize.Value;
|
---|
444 |
|
---|
445 | var listOfSolutions = new List<int>(numberOfSolutionsToSelect);
|
---|
446 |
|
---|
447 | int neighbourSize = neighbourhood[subproblemId].Length;
|
---|
448 | while (listOfSolutions.Count < numberOfSolutionsToSelect) {
|
---|
449 | var selectedSolution = neighbourType == NeighborType.NEIGHBOR
|
---|
450 | ? neighbourhood[subproblemId][random.Next(neighbourSize)]
|
---|
451 | : random.Next(populationSize);
|
---|
452 |
|
---|
453 | bool flag = true;
|
---|
454 | foreach (int individualId in listOfSolutions) {
|
---|
455 | if (individualId == selectedSolution) {
|
---|
456 | flag = false;
|
---|
457 | break;
|
---|
458 | }
|
---|
459 | }
|
---|
460 |
|
---|
461 | if (flag) {
|
---|
462 | listOfSolutions.Add(selectedSolution);
|
---|
463 | }
|
---|
464 | }
|
---|
465 |
|
---|
466 | return listOfSolutions;
|
---|
467 | }
|
---|
468 |
|
---|
469 | protected void UpdateNeighbourHood(IRandom random, IMOEADSolution individual, int subProblemId, NeighborType neighbourType) {
|
---|
470 | int replacedSolutions = 0;
|
---|
471 | int size = neighbourType == NeighborType.NEIGHBOR ? NeighbourSize : population.Count;
|
---|
472 |
|
---|
473 | foreach (var i in Enumerable.Range(0, size).Shuffle(random)) {
|
---|
474 | int k = neighbourType == NeighborType.NEIGHBOR
|
---|
475 | ? neighbourhood[subProblemId][i]
|
---|
476 | : i;
|
---|
477 |
|
---|
478 | double f1 = CalculateFitness(population[k].Qualities, lambda[k]);
|
---|
479 | double f2 = CalculateFitness(individual.Qualities, lambda[k]);
|
---|
480 |
|
---|
481 | if (f2 < f1) {
|
---|
482 | population[k] = (IMOEADSolution)individual.Clone();
|
---|
483 | replacedSolutions++;
|
---|
484 | }
|
---|
485 |
|
---|
486 | if (replacedSolutions >= MaximumNumberOfReplacedSolutions) {
|
---|
487 | return;
|
---|
488 | }
|
---|
489 | }
|
---|
490 | }
|
---|
491 |
|
---|
492 | private double CalculateFitness(double[] qualities, double[] lambda) {
|
---|
493 | double fitness;
|
---|
494 | int dim = qualities.Length;
|
---|
495 | switch (functionType) {
|
---|
496 | case FunctionType.TCHE: {
|
---|
497 | double maxFun = double.MinValue;
|
---|
498 |
|
---|
499 | for (int n = 0; n < dim; n++) {
|
---|
500 | var diff = qualities[n] - IdealPoint[n];
|
---|
501 | if (NormalizeObjectives) {
|
---|
502 | diff /= NadirPoint[n] - IdealPoint[n];
|
---|
503 | }
|
---|
504 | var l = lambda[n].IsAlmost(0) ? 1e-4 : lambda[n];
|
---|
505 | var feval = l * Math.Abs(diff);
|
---|
506 |
|
---|
507 | if (feval > maxFun) {
|
---|
508 | maxFun = feval;
|
---|
509 | }
|
---|
510 | }
|
---|
511 |
|
---|
512 | fitness = maxFun;
|
---|
513 | return fitness;
|
---|
514 | }
|
---|
515 | case FunctionType.AGG: {
|
---|
516 | double sum = 0.0;
|
---|
517 | for (int n = 0; n < dim; n++) {
|
---|
518 | sum += lambda[n] * qualities[n];
|
---|
519 | }
|
---|
520 |
|
---|
521 | fitness = sum;
|
---|
522 | return fitness;
|
---|
523 | }
|
---|
524 | case FunctionType.PBI: {
|
---|
525 | double d1, d2, nl;
|
---|
526 | double theta = 5.0;
|
---|
527 | int dimensions = dim;
|
---|
528 |
|
---|
529 | d1 = d2 = nl = 0.0;
|
---|
530 |
|
---|
531 | for (int i = 0; i < dimensions; i++) {
|
---|
532 | d1 += (qualities[i] - IdealPoint[i]) * lambda[i];
|
---|
533 | nl += Math.Pow(lambda[i], 2.0);
|
---|
534 | }
|
---|
535 | nl = Math.Sqrt(nl);
|
---|
536 | d1 = Math.Abs(d1) / nl;
|
---|
537 |
|
---|
538 | for (int i = 0; i < dimensions; i++) {
|
---|
539 | d2 += Math.Pow((qualities[i] - IdealPoint[i]) - d1 * (lambda[i] / nl), 2.0);
|
---|
540 | }
|
---|
541 | d2 = Math.Sqrt(d2);
|
---|
542 |
|
---|
543 | fitness = (d1 + theta * d2);
|
---|
544 | return fitness;
|
---|
545 | }
|
---|
546 | default: {
|
---|
547 | throw new ArgumentException($"Unknown function type: {functionType}");
|
---|
548 | }
|
---|
549 | }
|
---|
550 | }
|
---|
551 |
|
---|
552 | public IList<IMOEADSolution> GetResult(IRandom random) {
|
---|
553 | var populationSize = PopulationSize.Value;
|
---|
554 | var resultPopulationSize = ResultPopulationSize.Value;
|
---|
555 |
|
---|
556 | if (populationSize > resultPopulationSize) {
|
---|
557 | return MOEADUtil.GetSubsetOfEvenlyDistributedSolutions(random, population, resultPopulationSize);
|
---|
558 | } else {
|
---|
559 | return population;
|
---|
560 | }
|
---|
561 | }
|
---|
562 |
|
---|
563 | protected void UpdateParetoFronts() {
|
---|
564 | bool dominates(Point2D<double> x, Point2D<double> y) => x.X <= y.X && x.Y <= y.Y;
|
---|
565 | // get all non-dominated points
|
---|
566 | var points = population.Select(x => new Point2D<double>(Math.Round(x.Qualities[0], 6), Math.Round(x.Qualities[1], 6))).OrderBy(_ => _.X).ThenBy(_ => _.Y).ToArray();
|
---|
567 | var dominated = new bool[points.Length];
|
---|
568 |
|
---|
569 | for (int i = 0; i < points.Length; ++i) {
|
---|
570 | if (dominated[i]) { continue; }
|
---|
571 | for (int j = 0; j < points.Length; ++j) {
|
---|
572 | if (i == j) { continue; }
|
---|
573 | if (dominated[j]) { continue; }
|
---|
574 | dominated[j] = dominates(points[i], points[j]);
|
---|
575 | }
|
---|
576 | }
|
---|
577 |
|
---|
578 | var pf = Enumerable.Range(0, dominated.Length).Where(x => !dominated[x]).Select(x => points[x]);
|
---|
579 |
|
---|
580 | ScatterPlot sp;
|
---|
581 | if (!Results.ContainsKey("Pareto Front")) {
|
---|
582 | sp = new ScatterPlot();
|
---|
583 | sp.Rows.Add(new ScatterPlotDataRow("Pareto Front", "", pf) { VisualProperties = { PointSize = 5 } });
|
---|
584 | Results.AddOrUpdateResult("Pareto Front", sp);
|
---|
585 | } else {
|
---|
586 | sp = (ScatterPlot)Results["Pareto Front"].Value;
|
---|
587 | sp.Rows["Pareto Front"].Points.Replace(pf);
|
---|
588 | }
|
---|
589 | }
|
---|
590 |
|
---|
591 | #region operator wiring and events
|
---|
592 | protected void ExecuteOperation(ExecutionContext executionContext, CancellationToken cancellationToken, IOperation operation) {
|
---|
593 | Stack<IOperation> executionStack = new Stack<IOperation>();
|
---|
594 | executionStack.Push(operation);
|
---|
595 | while (executionStack.Count > 0) {
|
---|
596 | cancellationToken.ThrowIfCancellationRequested();
|
---|
597 | IOperation next = executionStack.Pop();
|
---|
598 | if (next is OperationCollection) {
|
---|
599 | OperationCollection coll = (OperationCollection)next;
|
---|
600 | for (int i = coll.Count - 1; i >= 0; i--)
|
---|
601 | if (coll[i] != null) executionStack.Push(coll[i]);
|
---|
602 | } else if (next is IAtomicOperation) {
|
---|
603 | IAtomicOperation op = (IAtomicOperation)next;
|
---|
604 | next = op.Operator.Execute((IExecutionContext)op, cancellationToken);
|
---|
605 | if (next != null) executionStack.Push(next);
|
---|
606 | }
|
---|
607 | }
|
---|
608 | }
|
---|
609 |
|
---|
610 | private void UpdateAnalyzers() {
|
---|
611 | Analyzer.Operators.Clear();
|
---|
612 | if (Problem != null) {
|
---|
613 | foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
|
---|
614 | foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
|
---|
615 | param.Depth = 1;
|
---|
616 | Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
|
---|
617 | }
|
---|
618 | }
|
---|
619 | }
|
---|
620 |
|
---|
621 | private void UpdateCrossovers() {
|
---|
622 | ICrossover oldCrossover = CrossoverParameter.Value;
|
---|
623 | CrossoverParameter.ValidValues.Clear();
|
---|
624 | ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
|
---|
625 |
|
---|
626 | foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
|
---|
627 | CrossoverParameter.ValidValues.Add(crossover);
|
---|
628 |
|
---|
629 | if (oldCrossover != null) {
|
---|
630 | ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
|
---|
631 | if (crossover != null) CrossoverParameter.Value = crossover;
|
---|
632 | else oldCrossover = null;
|
---|
633 | }
|
---|
634 | if (oldCrossover == null && defaultCrossover != null)
|
---|
635 | CrossoverParameter.Value = defaultCrossover;
|
---|
636 | }
|
---|
637 |
|
---|
638 | private void UpdateMutators() {
|
---|
639 | IManipulator oldMutator = MutatorParameter.Value;
|
---|
640 | MutatorParameter.ValidValues.Clear();
|
---|
641 | IManipulator defaultMutator = Problem.Operators.OfType<IManipulator>().FirstOrDefault();
|
---|
642 |
|
---|
643 | foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
|
---|
644 | MutatorParameter.ValidValues.Add(mutator);
|
---|
645 |
|
---|
646 | if (oldMutator != null) {
|
---|
647 | IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
|
---|
648 | if (mutator != null) MutatorParameter.Value = mutator;
|
---|
649 | else oldMutator = null;
|
---|
650 | }
|
---|
651 |
|
---|
652 | if (oldMutator == null && defaultMutator != null)
|
---|
653 | MutatorParameter.Value = defaultMutator;
|
---|
654 | }
|
---|
655 |
|
---|
656 | protected override void OnProblemChanged() {
|
---|
657 | UpdateCrossovers();
|
---|
658 | UpdateMutators();
|
---|
659 | UpdateAnalyzers();
|
---|
660 | base.OnProblemChanged();
|
---|
661 | }
|
---|
662 |
|
---|
663 | protected override void OnExecutionStateChanged() {
|
---|
664 | previousExecutionState = executionState;
|
---|
665 | executionState = ExecutionState;
|
---|
666 | base.OnExecutionStateChanged();
|
---|
667 | }
|
---|
668 |
|
---|
669 | public void ClearState() {
|
---|
670 | if (solutions != null) {
|
---|
671 | solutions.Clear();
|
---|
672 | }
|
---|
673 | if (population != null) {
|
---|
674 | population.Clear();
|
---|
675 | }
|
---|
676 | if (offspringPopulation != null) {
|
---|
677 | offspringPopulation.Clear();
|
---|
678 | }
|
---|
679 | if (jointPopulation != null) {
|
---|
680 | jointPopulation.Clear();
|
---|
681 | }
|
---|
682 | lambda = null;
|
---|
683 | neighbourhood = null;
|
---|
684 | if (executionContext != null && executionContext.Scope != null) {
|
---|
685 | executionContext.Scope.SubScopes.Clear();
|
---|
686 | }
|
---|
687 | }
|
---|
688 |
|
---|
689 | protected override void OnStopped() {
|
---|
690 | ClearState();
|
---|
691 | base.OnStopped();
|
---|
692 | }
|
---|
693 | #endregion
|
---|
694 | }
|
---|
695 | }
|
---|