1 | using HeuristicLab.Analysis;
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2 | using HeuristicLab.Common;
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3 | using HeuristicLab.Core;
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4 | using HeuristicLab.Data;
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5 | using HeuristicLab.Optimization;
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6 | using HeuristicLab.Parameters;
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7 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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8 | using HeuristicLab.Random;
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9 | using System;
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10 | using System.Collections.Generic;
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11 | using System.Linq;
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12 |
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13 | using CancellationToken = System.Threading.CancellationToken;
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14 |
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15 | namespace HeuristicLab.Algorithms.MOEAD {
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16 | [Item("MOEADAlgorithmBase", "Base class for all MOEA/D algorithm variants.")]
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17 | [StorableClass]
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18 | public abstract class MOEADAlgorithmBase : BasicAlgorithm {
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19 | #region data members
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20 | protected enum NeighborType { NEIGHBOR, POPULATION }
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21 | // TCHE = Chebyshev (Tchebyshev)
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22 | // PBI = Penalty-based boundary intersection
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23 | // AGG = Weighted sum
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24 | public enum FunctionType { TCHE, PBI, AGG }
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25 |
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26 | [Storable]
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27 | protected double[] IdealPoint { get; set; }
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28 | [Storable]
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29 | protected double[] NadirPoint { get; set; } // potentially useful for objective normalization
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30 |
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31 | [Storable]
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32 | protected double[][] lambda;
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33 |
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34 | [Storable]
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35 | protected int[][] neighbourhood;
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36 |
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37 | [Storable]
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38 | protected IList<IMOEADSolution> solutions;
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39 |
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40 | [Storable]
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41 | protected FunctionType functionType;
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42 |
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43 | [Storable]
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44 | protected List<IMOEADSolution> population;
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45 |
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46 | [Storable]
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47 | protected List<IMOEADSolution> offspringPopulation;
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48 |
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49 | [Storable]
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50 | protected List<IMOEADSolution> jointPopulation;
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51 |
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52 | [Storable]
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53 | protected int evaluatedSolutions;
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54 |
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55 | [Storable]
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56 | protected ExecutionContext executionContext;
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57 |
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58 | [Storable]
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59 | protected IScope globalScope;
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60 | #endregion
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61 |
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62 | #region parameters
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63 | private const string SeedParameterName = "Seed";
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64 | private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
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65 | private const string PopulationSizeParameterName = "PopulationSize";
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66 | private const string ResultPopulationSizeParameterName = "ResultPopulationSize";
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67 | private const string CrossoverProbabilityParameterName = "CrossoverProbability";
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68 | private const string CrossoverParameterName = "Crossover";
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69 | private const string MutationProbabilityParameterName = "MutationProbability";
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70 | private const string MutatorParameterName = "Mutator";
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71 | private const string MaximumEvaluatedSolutionsParameterName = "MaximumEvaluatedSolutions";
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72 | private const string RandomParameterName = "Random";
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73 | private const string AnalyzerParameterName = "Analyzer";
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74 | // MOEA-D parameters
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75 | private const string NeighbourSizeParameterName = "NeighbourSize";
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76 | private const string NeighbourhoodSelectionProbabilityParameterName = "NeighbourhoodSelectionProbability";
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77 | private const string MaximumNumberOfReplacedSolutionsParameterName = "MaximumNumberOfReplacedSolutions";
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78 | private const string FunctionTypeParameterName = "FunctionType";
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79 |
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80 | public IValueParameter<MultiAnalyzer> AnalyzerParameter {
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81 | get { return (ValueParameter<MultiAnalyzer>)Parameters[AnalyzerParameterName]; }
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82 | }
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83 |
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84 | public IConstrainedValueParameter<StringValue> FunctionTypeParameter {
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85 | get { return (IConstrainedValueParameter<StringValue>)Parameters[FunctionTypeParameterName]; }
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86 | }
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87 | public IFixedValueParameter<IntValue> NeighbourSizeParameter {
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88 | get { return (IFixedValueParameter<IntValue>)Parameters[NeighbourSizeParameterName]; }
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89 | }
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90 | public IFixedValueParameter<IntValue> MaximumNumberOfReplacedSolutionsParameter {
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91 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumNumberOfReplacedSolutionsParameterName]; }
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92 | }
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93 | public IFixedValueParameter<DoubleValue> NeighbourhoodSelectionProbabilityParameter {
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94 | get { return (IFixedValueParameter<DoubleValue>)Parameters[NeighbourhoodSelectionProbabilityParameterName]; }
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95 | }
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96 | public IFixedValueParameter<IntValue> SeedParameter {
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97 | get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
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98 | }
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99 | public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
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100 | get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
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101 | }
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102 | private IValueParameter<IntValue> PopulationSizeParameter {
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103 | get { return (IValueParameter<IntValue>)Parameters[PopulationSizeParameterName]; }
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104 | }
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105 | private IValueParameter<IntValue> ResultPopulationSizeParameter {
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106 | get { return (IValueParameter<IntValue>)Parameters[ResultPopulationSizeParameterName]; }
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107 | }
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108 | public IValueParameter<PercentValue> CrossoverProbabilityParameter {
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109 | get { return (IValueParameter<PercentValue>)Parameters[CrossoverProbabilityParameterName]; }
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110 | }
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111 | public IConstrainedValueParameter<ICrossover> CrossoverParameter {
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112 | get { return (IConstrainedValueParameter<ICrossover>)Parameters[CrossoverParameterName]; }
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113 | }
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114 | public IValueParameter<PercentValue> MutationProbabilityParameter {
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115 | get { return (IValueParameter<PercentValue>)Parameters[MutationProbabilityParameterName]; }
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116 | }
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117 | public IConstrainedValueParameter<IManipulator> MutatorParameter {
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118 | get { return (IConstrainedValueParameter<IManipulator>)Parameters[MutatorParameterName]; }
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119 | }
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120 | public IValueParameter<IntValue> MaximumEvaluatedSolutionsParameter {
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121 | get { return (IValueParameter<IntValue>)Parameters[MaximumEvaluatedSolutionsParameterName]; }
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122 | }
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123 | public IValueParameter<IRandom> RandomParameter {
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124 | get { return (IValueParameter<IRandom>)Parameters[RandomParameterName]; }
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125 | }
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126 | #endregion
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127 |
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128 | #region parameter properties
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129 | public new IMultiObjectiveHeuristicOptimizationProblem Problem {
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130 | get { return (IMultiObjectiveHeuristicOptimizationProblem)base.Problem; }
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131 | }
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132 | public int Seed {
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133 | get { return SeedParameter.Value.Value; }
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134 | set { SeedParameter.Value.Value = value; }
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135 | }
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136 | public bool SetSeedRandomly {
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137 | get { return SetSeedRandomlyParameter.Value.Value; }
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138 | set { SetSeedRandomlyParameter.Value.Value = value; }
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139 | }
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140 | public IntValue PopulationSize {
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141 | get { return PopulationSizeParameter.Value; }
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142 | set { PopulationSizeParameter.Value = value; }
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143 | }
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144 | public IntValue ResultPopulationSize {
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145 | get { return ResultPopulationSizeParameter.Value; }
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146 | set { ResultPopulationSizeParameter.Value = value; }
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147 | }
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148 | public PercentValue CrossoverProbability {
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149 | get { return CrossoverProbabilityParameter.Value; }
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150 | set { CrossoverProbabilityParameter.Value = value; }
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151 | }
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152 | public ICrossover Crossover {
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153 | get { return CrossoverParameter.Value; }
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154 | set { CrossoverParameter.Value = value; }
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155 | }
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156 | public PercentValue MutationProbability {
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157 | get { return MutationProbabilityParameter.Value; }
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158 | set { MutationProbabilityParameter.Value = value; }
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159 | }
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160 | public IManipulator Mutator {
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161 | get { return MutatorParameter.Value; }
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162 | set { MutatorParameter.Value = value; }
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163 | }
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164 | public MultiAnalyzer Analyzer {
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165 | get { return AnalyzerParameter.Value; }
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166 | set { AnalyzerParameter.Value = value; }
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167 | }
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168 | public IntValue MaximumEvaluatedSolutions {
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169 | get { return MaximumEvaluatedSolutionsParameter.Value; }
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170 | set { MaximumEvaluatedSolutionsParameter.Value = value; }
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171 | }
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172 | public int NeighbourSize {
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173 | get { return NeighbourSizeParameter.Value.Value; }
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174 | set { NeighbourSizeParameter.Value.Value = value; }
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175 | }
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176 | public int MaximumNumberOfReplacedSolutions {
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177 | get { return MaximumNumberOfReplacedSolutionsParameter.Value.Value; }
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178 | set { MaximumNumberOfReplacedSolutionsParameter.Value.Value = value; }
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179 | }
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180 | public double NeighbourhoodSelectionProbability {
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181 | get { return NeighbourhoodSelectionProbabilityParameter.Value.Value; }
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182 | set { NeighbourhoodSelectionProbabilityParameter.Value.Value = value; }
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183 | }
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184 | #endregion
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185 |
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186 | #region constructors
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187 | public MOEADAlgorithmBase() {
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188 | 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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189 | 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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190 | Parameters.Add(new ValueParameter<IntValue>(PopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
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191 | Parameters.Add(new ValueParameter<IntValue>(ResultPopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
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192 | Parameters.Add(new ValueParameter<PercentValue>(CrossoverProbabilityParameterName, "The probability that the crossover operator is applied.", new PercentValue(0.9)));
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193 | Parameters.Add(new ConstrainedValueParameter<ICrossover>(CrossoverParameterName, "The operator used to cross solutions."));
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194 | 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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195 | Parameters.Add(new ConstrainedValueParameter<IManipulator>(MutatorParameterName, "The operator used to mutate solutions."));
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196 | Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
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197 | Parameters.Add(new ValueParameter<IntValue>(MaximumEvaluatedSolutionsParameterName, "The maximum number of evaluated solutions (approximately).", new IntValue(100_000)));
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198 | Parameters.Add(new ValueParameter<IRandom>(RandomParameterName, new MersenneTwister()));
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199 | Parameters.Add(new FixedValueParameter<IntValue>(NeighbourSizeParameterName, new IntValue(20)));
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200 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumNumberOfReplacedSolutionsParameterName, new IntValue(2)));
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201 | Parameters.Add(new FixedValueParameter<DoubleValue>(NeighbourhoodSelectionProbabilityParameterName, new DoubleValue(0.1)));
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202 |
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203 | var functionTypeParameter = new ConstrainedValueParameter<StringValue>(FunctionTypeParameterName);
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204 | foreach (var s in new[] { "Chebyshev", "PBI", "Weighted Sum" }) {
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205 | functionTypeParameter.ValidValues.Add(new StringValue(s));
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206 | }
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207 | Parameters.Add(functionTypeParameter);
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208 | }
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209 |
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210 | protected MOEADAlgorithmBase(MOEADAlgorithmBase original, Cloner cloner) : base(original, cloner) {
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211 | functionType = original.functionType;
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212 | evaluatedSolutions = original.evaluatedSolutions;
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213 |
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214 | if (original.IdealPoint != null) {
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215 | IdealPoint = (double[])original.IdealPoint.Clone();
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216 | }
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217 |
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218 | if (original.NadirPoint != null) {
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219 | NadirPoint = (double[])original.NadirPoint.Clone();
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220 | }
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221 |
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222 | if (original.lambda != null) {
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223 | lambda = (double[][])original.lambda.Clone();
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224 | }
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225 |
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226 | if (original.neighbourhood != null) {
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227 | neighbourhood = (int[][])original.neighbourhood.Clone();
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228 | }
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229 |
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230 | if (original.solutions != null) {
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231 | solutions = original.solutions.Select(cloner.Clone).ToArray();
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232 | }
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233 |
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234 | if (original.population != null) {
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235 | population = original.population.Select(cloner.Clone).ToList();
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236 | }
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237 |
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238 | if (original.offspringPopulation != null) {
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239 | offspringPopulation = original.offspringPopulation.Select(cloner.Clone).ToList();
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240 | }
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241 |
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242 | if (original.jointPopulation != null) {
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243 | jointPopulation = original.jointPopulation.Select(x => cloner.Clone(x)).ToList();
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244 | }
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245 |
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246 | if (original.executionContext != null) {
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247 | executionContext = cloner.Clone(original.executionContext);
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248 | }
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249 |
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250 | if (original.globalScope != null) {
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251 | globalScope = cloner.Clone(original.globalScope);
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252 | }
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253 | }
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254 |
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255 | [StorableConstructor]
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256 | protected MOEADAlgorithmBase(bool deserializing) : base(deserializing) { }
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257 | #endregion
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258 |
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259 | private void InitializePopulation(ExecutionContext executionContext, CancellationToken cancellationToken, IRandom random, bool[] maximization) {
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260 | var creator = Problem.SolutionCreator;
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261 | var evaluator = Problem.Evaluator;
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262 |
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263 | var dimensions = maximization.Length;
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264 | var populationSize = PopulationSize.Value;
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265 | population = new List<IMOEADSolution>(populationSize);
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266 |
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267 | var parentScope = executionContext.Scope;
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268 | // first, create all individuals
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269 | for (int i = 0; i < populationSize; ++i) {
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270 | var childScope = new Scope(i.ToString()) { Parent = parentScope };
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271 | ExecuteOperation(executionContext, cancellationToken, executionContext.CreateChildOperation(creator, childScope));
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272 | parentScope.SubScopes.Add(childScope);
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273 | }
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274 |
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275 | // then, evaluate them and update qualities
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276 | for (int i = 0; i < populationSize; ++i) {
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277 | var childScope = parentScope.SubScopes[i];
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278 | ExecuteOperation(executionContext, cancellationToken, executionContext.CreateChildOperation(evaluator, childScope));
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279 |
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280 | var qualities = (DoubleArray)childScope.Variables["Qualities"].Value;
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281 | var solution = new MOEADSolution(childScope, dimensions, 0);
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282 | for (int j = 0; j < dimensions; ++j) {
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283 | solution.Qualities[j] = maximization[j] ? 1 - qualities[j] : qualities[j];
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284 | }
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285 | population.Add(solution);
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286 | }
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287 | }
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288 |
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289 | public override void Prepare() {
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290 | base.Prepare();
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291 | }
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292 |
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293 | protected override void Initialize(CancellationToken cancellationToken) {
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294 | globalScope = new Scope("Global Scope");
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295 | executionContext = new ExecutionContext(null, this, globalScope);
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296 |
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297 | // set the execution context for parameters to allow lookup
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298 | foreach (var parameter in Problem.Parameters.OfType<IValueParameter>()) {
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299 | // we need all of these in order for the wiring of the operators to work
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300 | globalScope.Variables.Add(new Variable(parameter.Name, parameter.Value));
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301 | }
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302 | globalScope.Variables.Add(new Variable("Results", Results)); // make results available as a parameter for analyzers etc.
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303 |
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304 | var rand = RandomParameter.Value;
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305 | if (SetSeedRandomly) Seed = RandomSeedGenerator.GetSeed();
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306 | rand.Reset(Seed);
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307 |
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308 | bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();
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309 | var dimensions = maximization.Length;
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310 |
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311 | var populationSize = PopulationSize.Value;
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312 |
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313 | InitializePopulation(executionContext, cancellationToken, rand, maximization);
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314 | InitializeUniformWeights(rand, populationSize, dimensions);
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315 | InitializeNeighbourHood(lambda, populationSize, NeighbourSize);
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316 |
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317 | IdealPoint = new double[dimensions];
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318 | IdealPoint.UpdateIdeal(population);
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319 |
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320 | NadirPoint = new double[dimensions];
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321 | NadirPoint.UpdateNadir(population);
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322 |
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323 | var functionTypeString = FunctionTypeParameter.Value.Value;
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324 | switch (functionTypeString) {
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325 | case "Chebyshev":
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326 | functionType = FunctionType.TCHE;
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327 | break;
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328 | case "PBI":
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329 | functionType = FunctionType.PBI;
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330 | break;
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331 | case "Weighted Sum":
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332 | functionType = FunctionType.AGG;
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333 | break;
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334 | }
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335 |
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336 | evaluatedSolutions = populationSize;
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337 |
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338 | base.Initialize(cancellationToken);
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339 | }
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340 |
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341 | public override bool SupportsPause => true;
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342 |
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343 | protected void InitializeUniformWeights(IRandom random, int populationSize, int dimensions) {
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344 | if (dimensions > 2) {
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345 | throw new ArgumentException("The current implementation doesn't support more than 2 dimensions.");
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346 | }
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347 |
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348 | lambda = new double[populationSize][];
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349 | var values = SequenceGenerator.GenerateSteps(0m, 1m, 1m / populationSize).ToArray();
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350 |
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351 | for (int i = 0; i < populationSize; ++i) {
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352 | var w = (double)values[i];
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353 | lambda[i] = new[] { w, 1 - w };
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354 | }
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355 | lambda.ShuffleInPlace(random);
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356 | }
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357 |
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358 | protected void InitializeNeighbourHood(double[][] lambda, int populationSize, int neighbourSize) {
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359 | neighbourhood = new int[populationSize][];
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360 |
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361 | var x = new double[populationSize];
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362 | var idx = new int[populationSize];
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363 |
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364 | for (int i = 0; i < populationSize; ++i) {
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365 | for (int j = 0; j < populationSize; ++j) {
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366 | x[j] = MOEADUtil.EuclideanDistance(lambda[i], lambda[j]);
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367 | idx[j] = j;
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368 | }
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369 |
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370 | MOEADUtil.MinFastSort(x, idx, populationSize, neighbourSize);
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371 | neighbourhood[i] = (int[])idx.Clone();
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372 | }
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373 | }
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374 |
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375 | protected NeighborType ChooseNeighborType(IRandom random, double neighbourhoodSelectionProbability) {
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376 | return random.NextDouble() < neighbourhoodSelectionProbability
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377 | ? NeighborType.NEIGHBOR
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378 | : NeighborType.POPULATION;
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379 | }
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380 |
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381 | protected IList<IMOEADSolution> ParentSelection(IRandom random, int subProblemId, NeighborType neighbourType) {
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382 | List<int> matingPool = MatingSelection(random, subProblemId, 2, neighbourType);
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383 |
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384 | var parents = new IMOEADSolution[3];
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385 |
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386 | parents[0] = population[matingPool[0]];
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387 | parents[1] = population[matingPool[1]];
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388 | parents[2] = population[subProblemId];
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389 |
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390 | return parents;
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391 | }
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392 |
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393 | protected List<int> MatingSelection(IRandom random, int subproblemId, int numberOfSolutionsToSelect, NeighborType neighbourType) {
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394 | int populationSize = PopulationSize.Value;
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395 |
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396 | var listOfSolutions = new List<int>(numberOfSolutionsToSelect);
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397 |
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398 | int neighbourSize = neighbourhood[subproblemId].Length;
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399 | while (listOfSolutions.Count < numberOfSolutionsToSelect) {
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400 | var selectedSolution = neighbourType == NeighborType.NEIGHBOR
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401 | ? neighbourhood[subproblemId][random.Next(neighbourSize)]
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402 | : random.Next(populationSize);
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403 |
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404 | bool flag = true;
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405 | foreach (int individualId in listOfSolutions) {
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406 | if (individualId == selectedSolution) {
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407 | flag = false;
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408 | break;
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409 | }
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410 | }
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411 |
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412 | if (flag) {
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413 | listOfSolutions.Add(selectedSolution);
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414 | }
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415 | }
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416 |
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417 | return listOfSolutions;
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418 | }
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419 |
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420 | protected void UpdateNeighbourHood(IRandom random, IMOEADSolution individual, int subProblemId, NeighborType neighbourType) {
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421 | int replacedSolutions = 0;
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422 | int size = neighbourType == NeighborType.NEIGHBOR ? NeighbourSize : population.Count;
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423 |
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424 | foreach (var i in Enumerable.Range(0, size).Shuffle(random)) {
|
---|
425 | int k = neighbourType == NeighborType.NEIGHBOR
|
---|
426 | ? neighbourhood[subProblemId][i]
|
---|
427 | : i;
|
---|
428 |
|
---|
429 | double f1 = CalculateFitness(population[k].Qualities, lambda[k]);
|
---|
430 | double f2 = CalculateFitness(individual.Qualities, lambda[k]);
|
---|
431 |
|
---|
432 | if (f2 < f1) {
|
---|
433 | population[k] = (IMOEADSolution)individual.Clone();
|
---|
434 | replacedSolutions++;
|
---|
435 | }
|
---|
436 |
|
---|
437 | if (replacedSolutions >= MaximumNumberOfReplacedSolutions) {
|
---|
438 | return;
|
---|
439 | }
|
---|
440 | }
|
---|
441 | }
|
---|
442 |
|
---|
443 | private double CalculateFitness(double[] qualities, double[] lambda) {
|
---|
444 | double fitness;
|
---|
445 | int dim = qualities.Length;
|
---|
446 | switch (functionType) {
|
---|
447 | case FunctionType.TCHE: {
|
---|
448 | double maxFun = -1.0e+30;
|
---|
449 |
|
---|
450 | for (int n = 0; n < dim; n++) {
|
---|
451 | double diff = Math.Abs(qualities[n] - IdealPoint[n]);
|
---|
452 |
|
---|
453 | double feval = lambda[n] == 0 ? 0.0001 * diff : diff * lambda[n];
|
---|
454 | if (feval > maxFun) {
|
---|
455 | maxFun = feval;
|
---|
456 | }
|
---|
457 | }
|
---|
458 |
|
---|
459 | fitness = maxFun;
|
---|
460 | return fitness;
|
---|
461 | }
|
---|
462 | case FunctionType.AGG: {
|
---|
463 | double sum = 0.0;
|
---|
464 | for (int n = 0; n < dim; n++) {
|
---|
465 | sum += lambda[n] * qualities[n];
|
---|
466 | }
|
---|
467 |
|
---|
468 | fitness = sum;
|
---|
469 | return fitness;
|
---|
470 | }
|
---|
471 | case FunctionType.PBI: {
|
---|
472 | double d1, d2, nl;
|
---|
473 | double theta = 5.0;
|
---|
474 | int dimensions = dim;
|
---|
475 |
|
---|
476 | d1 = d2 = nl = 0.0;
|
---|
477 |
|
---|
478 | for (int i = 0; i < dimensions; i++) {
|
---|
479 | d1 += (qualities[i] - IdealPoint[i]) * lambda[i];
|
---|
480 | nl += Math.Pow(lambda[i], 2.0);
|
---|
481 | }
|
---|
482 | nl = Math.Sqrt(nl);
|
---|
483 | d1 = Math.Abs(d1) / nl;
|
---|
484 |
|
---|
485 | for (int i = 0; i < dimensions; i++) {
|
---|
486 | d2 += Math.Pow((qualities[i] - IdealPoint[i]) - d1 * (lambda[i] / nl), 2.0);
|
---|
487 | }
|
---|
488 | d2 = Math.Sqrt(d2);
|
---|
489 |
|
---|
490 | fitness = (d1 + theta * d2);
|
---|
491 | return fitness;
|
---|
492 | }
|
---|
493 | default: {
|
---|
494 | throw new ArgumentException($"Unknown function type: {functionType}");
|
---|
495 | }
|
---|
496 | }
|
---|
497 | }
|
---|
498 |
|
---|
499 | public IList<IMOEADSolution> GetResult(IRandom random) {
|
---|
500 | var populationSize = PopulationSize.Value;
|
---|
501 | var resultPopulationSize = ResultPopulationSize.Value;
|
---|
502 |
|
---|
503 | if (populationSize > resultPopulationSize) {
|
---|
504 | return MOEADUtil.GetSubsetOfEvenlyDistributedSolutions(random, population, resultPopulationSize);
|
---|
505 | } else {
|
---|
506 | return population;
|
---|
507 | }
|
---|
508 | }
|
---|
509 |
|
---|
510 | protected void UpdateParetoFronts() {
|
---|
511 | bool dominates(Point2D<double> x, Point2D<double> y) => x.X <= y.X && x.Y <= y.Y;
|
---|
512 | // get all non-dominated points
|
---|
513 | 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();
|
---|
514 | var dominated = new bool[points.Length];
|
---|
515 |
|
---|
516 | for (int i = 0; i < points.Length; ++i) {
|
---|
517 | if (dominated[i]) { continue; }
|
---|
518 | for (int j = 0; j < points.Length; ++j) {
|
---|
519 | if (i == j) { continue; }
|
---|
520 | if (dominated[j]) { continue; }
|
---|
521 | dominated[j] = dominates(points[i], points[j]);
|
---|
522 | }
|
---|
523 | }
|
---|
524 |
|
---|
525 | var pf = Enumerable.Range(0, dominated.Length).Where(x => !dominated[x]).Select(x => points[x]);
|
---|
526 |
|
---|
527 | ScatterPlot sp;
|
---|
528 | if (!Results.ContainsKey("Pareto Front")) {
|
---|
529 | sp = new ScatterPlot();
|
---|
530 | sp.Rows.Add(new ScatterPlotDataRow("Pareto Front", "", pf) { VisualProperties = { PointSize = 5 } });
|
---|
531 | Results.AddOrUpdateResult("Pareto Front", sp);
|
---|
532 | } else {
|
---|
533 | sp = (ScatterPlot)Results["Pareto Front"].Value;
|
---|
534 | sp.Rows["Pareto Front"].Points.Replace(pf);
|
---|
535 | }
|
---|
536 | }
|
---|
537 |
|
---|
538 | #region operator wiring and events
|
---|
539 | protected void ExecuteOperation(ExecutionContext executionContext, CancellationToken cancellationToken, IOperation operation) {
|
---|
540 | Stack<IOperation> executionStack = new Stack<IOperation>();
|
---|
541 | executionStack.Push(operation);
|
---|
542 | while (executionStack.Count > 0) {
|
---|
543 | cancellationToken.ThrowIfCancellationRequested();
|
---|
544 | IOperation next = executionStack.Pop();
|
---|
545 | if (next is OperationCollection) {
|
---|
546 | OperationCollection coll = (OperationCollection)next;
|
---|
547 | for (int i = coll.Count - 1; i >= 0; i--)
|
---|
548 | if (coll[i] != null) executionStack.Push(coll[i]);
|
---|
549 | } else if (next is IAtomicOperation) {
|
---|
550 | IAtomicOperation op = (IAtomicOperation)next;
|
---|
551 | next = op.Operator.Execute((IExecutionContext)op, cancellationToken);
|
---|
552 | if (next != null) executionStack.Push(next);
|
---|
553 | }
|
---|
554 | }
|
---|
555 | }
|
---|
556 |
|
---|
557 | private void UpdateAnalyzers() {
|
---|
558 | Analyzer.Operators.Clear();
|
---|
559 | if (Problem != null) {
|
---|
560 | foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
|
---|
561 | foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
|
---|
562 | param.Depth = 1;
|
---|
563 | Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
|
---|
564 | }
|
---|
565 | }
|
---|
566 | }
|
---|
567 |
|
---|
568 | private void UpdateCrossovers() {
|
---|
569 | ICrossover oldCrossover = CrossoverParameter.Value;
|
---|
570 | CrossoverParameter.ValidValues.Clear();
|
---|
571 | ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
|
---|
572 |
|
---|
573 | foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
|
---|
574 | CrossoverParameter.ValidValues.Add(crossover);
|
---|
575 |
|
---|
576 | if (oldCrossover != null) {
|
---|
577 | ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
|
---|
578 | if (crossover != null) CrossoverParameter.Value = crossover;
|
---|
579 | else oldCrossover = null;
|
---|
580 | }
|
---|
581 | if (oldCrossover == null && defaultCrossover != null)
|
---|
582 | CrossoverParameter.Value = defaultCrossover;
|
---|
583 | }
|
---|
584 |
|
---|
585 | private void UpdateMutators() {
|
---|
586 | IManipulator oldMutator = MutatorParameter.Value;
|
---|
587 | MutatorParameter.ValidValues.Clear();
|
---|
588 | IManipulator defaultMutator = Problem.Operators.OfType<IManipulator>().FirstOrDefault();
|
---|
589 |
|
---|
590 | foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
|
---|
591 | MutatorParameter.ValidValues.Add(mutator);
|
---|
592 |
|
---|
593 | if (oldMutator != null) {
|
---|
594 | IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
|
---|
595 | if (mutator != null) MutatorParameter.Value = mutator;
|
---|
596 | else oldMutator = null;
|
---|
597 | }
|
---|
598 |
|
---|
599 | if (oldMutator == null && defaultMutator != null)
|
---|
600 | MutatorParameter.Value = defaultMutator;
|
---|
601 | }
|
---|
602 |
|
---|
603 | protected override void OnProblemChanged() {
|
---|
604 | UpdateCrossovers();
|
---|
605 | UpdateMutators();
|
---|
606 | UpdateAnalyzers();
|
---|
607 | base.OnProblemChanged();
|
---|
608 | }
|
---|
609 |
|
---|
610 | protected override void OnStopped() {
|
---|
611 | if (solutions != null) {
|
---|
612 | solutions.Clear();
|
---|
613 | }
|
---|
614 | if (population != null) {
|
---|
615 | population.Clear();
|
---|
616 | }
|
---|
617 | if (offspringPopulation != null) {
|
---|
618 | offspringPopulation.Clear();
|
---|
619 | }
|
---|
620 | if (jointPopulation != null) {
|
---|
621 | jointPopulation.Clear();
|
---|
622 | }
|
---|
623 | base.OnStopped();
|
---|
624 | }
|
---|
625 | #endregion
|
---|
626 | }
|
---|
627 | }
|
---|