1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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 | * Implementation based on the GDE3 implementation in jMetal Framework https://github.com/jMetal/jMetal
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8 | *
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9 | * HeuristicLab is free software: you can redistribute it and/or modify
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10 | * it under the terms of the GNU General Public License as published by
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11 | * the Free Software Foundation, either version 3 of the License, or
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12 | * (at your option) any later version.
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13 | *
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14 | * HeuristicLab is distributed in the hope that it will be useful,
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15 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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16 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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17 | * GNU General Public License for more details.
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18 | *
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19 | * You should have received a copy of the GNU General Public License
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20 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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21 | */
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22 | #endregion
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23 | using System;
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24 | using System.Linq;
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25 | using System.Collections.Generic;
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26 | using HeuristicLab.Analysis;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Core;
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29 | using HeuristicLab.Data;
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30 | using HeuristicLab.Encodings.RealVectorEncoding;
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31 | using HeuristicLab.Operators;
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32 | using HeuristicLab.Optimization;
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33 | using HeuristicLab.Optimization.Operators;
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34 | using HeuristicLab.Parameters;
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35 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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36 | using HeuristicLab.PluginInfrastructure;
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37 | using HeuristicLab.Problems.MultiObjectiveTestFunctions;
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38 | using HeuristicLab.Random;
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39 | using System.Threading;
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40 | using HeuristicLab.Algorithms.GDE3;
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41 |
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42 | namespace HeuristicLab.Algoritms.GDE3 {
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43 |
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44 | [Item("Generalized Differential Evolution (GDE3)", "A generalized differential evolution algorithm.")]
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45 | [StorableClass]
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46 | [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 400)]
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47 | public class GDE3 : BasicAlgorithm {
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48 | public override Type ProblemType {
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49 | get { return typeof(MultiObjectiveBasicProblem<RealVectorEncoding>); }
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50 | }
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51 | public new MultiObjectiveTestFunctionProblem Problem {
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52 | get { return (MultiObjectiveTestFunctionProblem)base.Problem; }
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53 | set { base.Problem = value; }
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54 | }
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55 |
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56 | public ILookupParameter<DoubleMatrix> BestKnownFrontParameter {
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57 | get {
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58 | return (ILookupParameter<DoubleMatrix>)Parameters["BestKnownFront"];
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59 | }
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60 | }
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61 |
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62 | private readonly IRandom _random = new MersenneTwister();
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63 | private int evals;
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64 | private double IGDSumm;
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65 |
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66 | #region ParameterNames
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67 | private const string MaximumGenerationsParameterName = "Maximum Generations";
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68 | private const string MaximumEvaluationsParameterName = "Maximum Evaluations";
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69 | private const string CrossoverProbabilityParameterName = "CrossoverProbability";
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70 | private const string PopulationSizeParameterName = "PopulationSize";
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71 | private const string ScalingFactorParameterName = "ScalingFactor";
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72 |
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73 | #endregion
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74 |
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75 | #region ParameterProperties
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76 | public IFixedValueParameter<IntValue> MaximumGenerationsParameter {
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77 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumGenerationsParameterName]; }
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78 | }
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79 | public IFixedValueParameter<IntValue> MaximumEvaluationsParameter {
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80 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumEvaluationsParameterName]; }
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81 | }
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82 | private ValueParameter<IntValue> PopulationSizeParameter {
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83 | get { return (ValueParameter<IntValue>)Parameters[PopulationSizeParameterName]; }
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84 | }
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85 | public ValueParameter<DoubleValue> CrossoverProbabilityParameter {
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86 | get { return (ValueParameter<DoubleValue>)Parameters[CrossoverProbabilityParameterName]; }
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87 | }
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88 | public ValueParameter<DoubleValue> ScalingFactorParameter {
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89 | get { return (ValueParameter<DoubleValue>)Parameters[ScalingFactorParameterName]; }
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90 | }
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91 | #endregion
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92 |
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93 | #region Properties
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94 | public int MaximumGenerations {
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95 | get { return MaximumGenerationsParameter.Value.Value; }
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96 | set { MaximumGenerationsParameter.Value.Value = value; }
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97 | }
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98 |
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99 | public int MaximumEvaluations {
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100 | get { return MaximumEvaluationsParameter.Value.Value; }
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101 | set { MaximumEvaluationsParameter.Value.Value = value; }
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102 | }
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103 |
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104 | public Double CrossoverProbability {
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105 | get { return CrossoverProbabilityParameter.Value.Value; }
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106 | set { CrossoverProbabilityParameter.Value.Value = value; }
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107 | }
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108 | public Double ScalingFactor {
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109 | get { return ScalingFactorParameter.Value.Value; }
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110 | set { ScalingFactorParameter.Value.Value = value; }
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111 | }
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112 | public IntValue PopulationSize {
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113 | get { return PopulationSizeParameter.Value; }
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114 | set { PopulationSizeParameter.Value = value; }
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115 | }
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116 | #endregion
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117 |
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118 | #region ResultsProperties
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119 | private double ResultsBestQuality {
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120 | get { return ((DoubleValue)Results["Best Quality"].Value).Value; }
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121 | set { ((DoubleValue)Results["Best Quality"].Value).Value = value; }
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122 | }
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123 |
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124 | private double ResultsIGDMean {
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125 | get { return ((DoubleValue)Results["IGDMeanValue"].Value).Value; }
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126 | set { ((DoubleValue)Results["IGDMeanValue"].Value).Value = value; }
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127 | }
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128 |
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129 | private double ResultsIGDBest {
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130 | get { return ((DoubleValue)Results["IGDBestValue"].Value).Value; }
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131 | set { ((DoubleValue)Results["IGDBestValue"].Value).Value = value; }
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132 | }
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133 |
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134 | private double ResultsIGDWorst {
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135 | get { return ((DoubleValue)Results["IGDWorstValue"].Value).Value; }
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136 | set { ((DoubleValue)Results["IGDWorstValue"].Value).Value = value; }
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137 | }
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138 |
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139 | private double ResultsInvertedGenerationalDistance {
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140 | get { return ((DoubleValue)Results["InvertedGenerationalDistance"].Value).Value; }
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141 | set { ((DoubleValue)Results["InvertedGenerationalDistance"].Value).Value = value; }
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142 | }
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143 |
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144 | private double ResultsHypervolume {
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145 | get { return ((DoubleValue)Results["HyperVolumeValue"].Value).Value; }
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146 | set { ((DoubleValue)Results["HyperVolumeValue"].Value).Value = value; }
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147 | }
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148 |
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149 | private DoubleMatrix ResultsBestFront {
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150 | get { return (DoubleMatrix)Results["Best Front"].Value; }
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151 | set { Results["Best Front"].Value = value; }
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152 | }
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153 |
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154 | private int ResultsEvaluations {
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155 | get { return ((IntValue)Results["Evaluations"].Value).Value; }
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156 | set { ((IntValue)Results["Evaluations"].Value).Value = value; }
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157 | }
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158 | private int ResultsGenerations {
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159 | get { return ((IntValue)Results["Generations"].Value).Value; }
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160 | set { ((IntValue)Results["Generations"].Value).Value = value; }
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161 | }
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162 | private double ResultsGenerationalDistance {
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163 | get { return ((DoubleValue)Results["GenerationalDistance"].Value).Value; }
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164 | set { ((DoubleValue)Results["GenerationalDistance"].Value).Value = value; }
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165 | }
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166 |
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167 | private double ResultsSpacing {
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168 | get { return ((DoubleValue)Results["Spacing"].Value).Value; }
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169 | set { ((DoubleValue)Results["Spacing"].Value).Value = value; }
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170 | }
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171 |
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172 | private double ResultsCrowding {
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173 | get { return ((DoubleValue)Results["Crowding"].Value).Value; }
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174 | set { ((DoubleValue)Results["Crowding"].Value).Value = value; }
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175 | }
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176 |
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177 | #endregion
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178 |
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179 | [StorableConstructor]
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180 | protected GDE3(bool deserializing) : base(deserializing) { }
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181 |
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182 | protected GDE3(GDE3 original, Cloner cloner)
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183 | : base(original, cloner) {
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184 | }
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185 |
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186 | public override IDeepCloneable Clone(Cloner cloner) {
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187 | return new GDE3(this, cloner);
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188 | }
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189 |
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190 | public GDE3() {
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191 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumGenerationsParameterName, "", new IntValue(1000)));
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192 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumEvaluationsParameterName, "", new IntValue(Int32.MaxValue)));
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193 | Parameters.Add(new ValueParameter<IntValue>(PopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
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194 | Parameters.Add(new ValueParameter<DoubleValue>(CrossoverProbabilityParameterName, "The value for crossover rate", new DoubleValue(0.5)));
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195 | Parameters.Add(new ValueParameter<DoubleValue>(ScalingFactorParameterName, "The value for scaling factor", new DoubleValue(0.5)));
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196 | Parameters.Add(new LookupParameter<DoubleMatrix>("BestKnownFront", "The currently best known Pareto front"));
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197 | }
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198 |
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199 | protected override void Run(CancellationToken cancellationToken) {
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200 | // Set up the results display
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201 | Results.Add(new Result("Generations", new IntValue(0)));
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202 | Results.Add(new Result("Evaluations", new IntValue(0)));
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203 | Results.Add(new Result("Best Front", new DoubleMatrix()));
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204 | Results.Add(new Result("Crowding", new DoubleValue(0)));
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205 | Results.Add(new Result("InvertedGenerationalDistance", new DoubleValue(0)));
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206 | Results.Add(new Result("GenerationalDistance", new DoubleValue(0)));
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207 | Results.Add(new Result("HyperVolumeValue", new DoubleValue(0)));
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208 | Results.Add(new Result("IGDMeanValue", new DoubleValue(0)));
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209 | Results.Add(new Result("IGDBestValue", new DoubleValue(Int32.MaxValue)));
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210 | Results.Add(new Result("IGDWorstValue", new DoubleValue(0)));
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211 |
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212 | Results.Add(new Result("Spacing", new DoubleValue(0)));
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213 | Results.Add(new Result("Scatterplot", typeof(IMOFrontModel)));
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214 | var table = new DataTable("Qualities");
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215 | table.Rows.Add(new DataRow("Best Quality"));
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216 | Results.Add(new Result("Qualities", table));
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217 |
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218 | //setup the variables
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219 | List<SolutionSet> population;
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220 | List<SolutionSet> offspringPopulation;
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221 | SolutionSet[] parent;
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222 | double IGDSumm = 0;
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223 |
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224 | //initialize population
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225 | population = new List<SolutionSet>(PopulationSizeParameter.Value.Value);
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226 |
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227 | for(int i = 0; i < PopulationSizeParameter.Value.Value; ++i) {
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228 | var m = createIndividual();
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229 | m.Quality = Problem.Evaluate(m.Population, _random);
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230 | //the test function is constrained
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231 | if(m.Quality.Length > Problem.Objectives) {
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232 | m.OverallConstrainViolation = m.Quality[Problem.Objectives];
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233 | } else {
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234 | m.OverallConstrainViolation = 0;
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235 | }
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236 | population.Add(m);
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237 | }
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238 |
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239 | this.initProgress();
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240 | int generations = 1;
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241 |
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242 | while(ResultsEvaluations < MaximumEvaluationsParameter.Value.Value
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243 | && !cancellationToken.IsCancellationRequested) {
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244 | var populationSize = PopulationSizeParameter.Value.Value;
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245 |
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246 | // Create the offSpring solutionSet
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247 | offspringPopulation = new List<SolutionSet>(PopulationSizeParameter.Value.Value * 2);
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248 |
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249 | for(int i = 0; i < populationSize; i++) {
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250 | // Obtain parents. Two parameters are required: the population and the
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251 | // index of the current individual
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252 | parent = selection(population, i);
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253 |
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254 | SolutionSet child;
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255 | // Crossover. The parameters are the current individual and the index of the array of parents
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256 | child = reproduction(population[i], parent);
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257 |
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258 | child.Quality = Problem.Evaluate(child.Population, _random);
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259 |
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260 | this.updateProgres();
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261 |
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262 | //the test function is constrained
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263 | if(child.Quality.Length > Problem.Objectives) {
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264 | child.OverallConstrainViolation = child.Quality[Problem.Objectives];
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265 | } else {
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266 | child.OverallConstrainViolation = 0;
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267 | }
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268 |
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269 | // Dominance test
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270 | int result;
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271 | result = compareDomination(population[i], child);
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272 |
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273 | if(result == -1) { // Solution i dominates child
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274 | offspringPopulation.Add(population[i]);
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275 | } else if(result == 1) { // child dominates
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276 | offspringPopulation.Add(child);
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277 | } else { // the two solutions are non-dominated
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278 | offspringPopulation.Add(child);
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279 | offspringPopulation.Add(population[i]);
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280 | }
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281 | }
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282 |
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283 | // Ranking the offspring population
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284 | List<SolutionSet>[] ranking = computeRanking(offspringPopulation);
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285 | population = crowdingDistanceSelection(ranking);
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286 | generations++;
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287 | ResultsGenerations = generations;
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288 | displayResults(population);
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289 | }
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290 | }
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291 |
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292 | public override bool SupportsPause { get { return false; } } // XXX does it actually support pause?
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293 |
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294 | private void displayResults(List<SolutionSet> population) {
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295 | List<SolutionSet>[] rankingFinal = computeRanking(population);
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296 |
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297 | int objectives = Problem.Objectives;
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298 | var optimalfront = Problem.TestFunction.OptimalParetoFront(objectives);
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299 |
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300 | double[][] opf = new double[0][];
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301 | if(optimalfront != null) {
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302 | opf = optimalfront.Select(s => s.ToArray()).ToArray();
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303 | }
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304 |
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305 | //compute the final qualities and population
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306 | double[][] qualitiesFinal = new double[rankingFinal[0].Count][];
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307 | double[][] populationFinal = new double[rankingFinal[0].Count][];
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308 |
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309 | for(int i = 0; i < rankingFinal[0].Count; ++i) {
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310 | qualitiesFinal[i] = new double[Problem.Objectives];
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311 | populationFinal[i] = new double[Problem.Objectives];
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312 | for(int j = 0; j < Problem.Objectives; ++j) {
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313 | populationFinal[i][j] = rankingFinal[0][i].Population[j];
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314 | qualitiesFinal[i][j] = rankingFinal[0][i].Quality[j];
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315 | }
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316 | }
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317 | IEnumerable<double[]> en = qualitiesFinal;
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318 | IEnumerable<double[]> frontVectors = NonDominatedSelect.selectNonDominatedVectors(qualitiesFinal, Problem.TestFunction.Maximization(objectives), true);
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319 | //update the results
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320 |
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321 | ResultsEvaluations = this.evals;
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322 | ResultsBestFront = new DoubleMatrix(MultiObjectiveTestFunctionProblem.To2D(qualitiesFinal));
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323 | ResultsCrowding = Crowding.Calculate(qualitiesFinal, Problem.TestFunction.Bounds(objectives));
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324 | GenerationalDistanceCalculator distance = new GenerationalDistanceCalculator();
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325 | ResultsInvertedGenerationalDistance = distance.CalculateGenerationalDistance(qualitiesFinal, opf, Problem.Objectives);
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326 | ResultsHypervolume = Hypervolume.Calculate(frontVectors, Problem.TestFunction.ReferencePoint(objectives), Problem.TestFunction.Maximization(objectives));
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327 | ResultsGenerationalDistance = GenerationalDistance.Calculate(qualitiesFinal, optimalfront, 1);
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328 | Results["Scatterplot"].Value = new MOSolution(qualitiesFinal, populationFinal, opf, objectives);
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329 | ResultsSpacing = Spacing.Calculate(qualitiesFinal);
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330 |
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331 | if(ResultsIGDBest > ResultsInvertedGenerationalDistance) {
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332 | ResultsIGDBest = ResultsInvertedGenerationalDistance;
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333 | }
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334 | if(ResultsIGDWorst < ResultsInvertedGenerationalDistance) {
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335 | ResultsIGDWorst = ResultsInvertedGenerationalDistance;
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336 | }
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337 | this.IGDSumm += ResultsInvertedGenerationalDistance;
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338 | ResultsIGDMean = this.IGDSumm / ResultsGenerations;
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339 | }
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340 |
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341 | private int getWorstIndex(List<SolutionSet> SolutionsList) {
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342 | int result = 0;
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343 |
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344 | if((SolutionsList == null) || SolutionsList.Count == 0) {
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345 | result = 0;
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346 | } else {
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347 | SolutionSet worstKnown = SolutionsList[0],
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348 | candidateSolution;
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349 | int flag;
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350 | for(int i = 1; i < SolutionsList.Count; i++) {
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351 | candidateSolution = SolutionsList[i];
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352 | flag = compareDomination(worstKnown, candidateSolution);
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353 | if(flag == -1) {
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354 | result = i;
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355 | worstKnown = candidateSolution;
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356 | }
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357 | }
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358 | }
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359 | return result;
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360 | }
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361 |
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362 | private SolutionSet createIndividual() {
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363 | var dim = Problem.ProblemSize;
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364 | var lb = Problem.Bounds[0, 0];
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365 | var ub = Problem.Bounds[0, 1];
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366 | var range = ub - lb;
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367 | var v = new double[Problem.ProblemSize];
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368 | SolutionSet solutionObject = new SolutionSet(PopulationSizeParameter.Value.Value);
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369 |
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370 | for(int i = 0; i < Problem.ProblemSize; ++i) {
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371 | v[i] = _random.NextDouble() * range + lb;
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372 |
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373 | }
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374 | solutionObject.createSolution(v);
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375 | return solutionObject;
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376 | }
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377 |
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378 | private SolutionSet createEmptyIndividual() {
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379 | SolutionSet solutionObject = new SolutionSet(PopulationSizeParameter.Value.Value);
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380 | var n = new RealVector(Problem.ProblemSize);
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381 | solutionObject.Population = n;
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382 | return solutionObject;
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383 | }
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384 |
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385 | private void initProgress() {
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386 | this.evals = PopulationSizeParameter.Value.Value;
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387 | }
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388 |
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389 | private void updateProgres() {
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390 | this.evals++;
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391 | }
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392 |
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393 | private SolutionSet[] selection(List<SolutionSet> population, int i) {
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394 | SolutionSet[] parents = new SolutionSet[3];
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395 | int r0, r1, r2;
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396 | //assure the selected vectors r0, r1 and r2 are different
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397 | do {
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398 | r0 = _random.Next(0, PopulationSizeParameter.Value.Value);
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399 | } while(r0 == i);
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400 | do {
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401 | r1 = _random.Next(0, PopulationSizeParameter.Value.Value);
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402 | } while(r1 == i || r1 == r0);
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403 | do {
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404 | r2 = _random.Next(0, PopulationSizeParameter.Value.Value);
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405 | } while(r2 == i || r2 == r0 || r2 == r1);
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406 |
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407 | parents[0] = population[r0];
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408 | parents[1] = population[r1];
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409 | parents[2] = population[r2];
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410 |
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411 | return parents;
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412 | }
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413 |
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414 | private SolutionSet reproduction(SolutionSet parent, SolutionSet[] parentsSolutions) {
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415 | var individual = createEmptyIndividual();
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416 | double rnbr = _random.Next(0, Problem.ProblemSize);
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417 | for(int m = 0; m < Problem.ProblemSize; m++) {
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418 | if(_random.NextDouble() < CrossoverProbabilityParameter.Value.Value || m == rnbr) {
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419 | double value;
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420 | value = parentsSolutions[2].Population[m] +
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421 | ScalingFactorParameter.Value.Value * (parentsSolutions[0].Population[m] - parentsSolutions[1].Population[m]);
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422 | //check the problem upper and lower bounds
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423 | if(value > Problem.Bounds[0, 1]) value = Problem.Bounds[0, 1];
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424 | if(value < Problem.Bounds[0, 0]) value = Problem.Bounds[0, 0];
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425 | individual.Population[m] = value;
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426 | } else {
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427 | double value;
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428 | value = parent.Population[m];
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429 | individual.Population[m] = value;
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430 | }
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431 | }
|
---|
432 | return individual;
|
---|
433 | }
|
---|
434 |
|
---|
435 | private List<SolutionSet> crowdingDistanceSelection(List<SolutionSet>[] ranking) {
|
---|
436 | List<SolutionSet> population = new List<SolutionSet>();
|
---|
437 | int rankingIndex = 0;
|
---|
438 | while(populationIsNotFull(population)) {
|
---|
439 | if(subFrontFillsIntoThePopulation(ranking, rankingIndex, population)) {
|
---|
440 | addRankedSolutionToPopulation(ranking, rankingIndex, population);
|
---|
441 | rankingIndex++;
|
---|
442 | } else {
|
---|
443 | crowdingDistanceAssignment(ranking[rankingIndex]);
|
---|
444 | addLastRankedSolutionToPopulation(ranking, rankingIndex, population);
|
---|
445 | }
|
---|
446 | }
|
---|
447 | return population;
|
---|
448 | }
|
---|
449 |
|
---|
450 | private void addLastRankedSolutionToPopulation(List<SolutionSet>[] ranking, int rankingIndex, List<SolutionSet> population) {
|
---|
451 | List<SolutionSet> currentRankedFront = ranking[rankingIndex];
|
---|
452 | //descending sort and add the front with highest crowding distance to the population
|
---|
453 | currentRankedFront.Sort((x, y) => -x.CrowdingDistance.CompareTo(y.CrowdingDistance));
|
---|
454 | int i = 0;
|
---|
455 | while(population.Count < PopulationSizeParameter.Value.Value) {
|
---|
456 | population.Add(currentRankedFront[i]);
|
---|
457 | i++;
|
---|
458 | }
|
---|
459 | }
|
---|
460 |
|
---|
461 | private void crowdingDistanceAssignment(List<SolutionSet> rankingSubfront) {
|
---|
462 | int size = rankingSubfront.Count;
|
---|
463 |
|
---|
464 | if(size == 0)
|
---|
465 | return;
|
---|
466 |
|
---|
467 | if(size == 1) {
|
---|
468 | rankingSubfront[0].CrowdingDistance = double.PositiveInfinity;
|
---|
469 | return;
|
---|
470 | }
|
---|
471 |
|
---|
472 | if(size == 2) {
|
---|
473 | rankingSubfront[0].CrowdingDistance = double.PositiveInfinity;
|
---|
474 | rankingSubfront[1].CrowdingDistance = double.PositiveInfinity;
|
---|
475 | return;
|
---|
476 | }
|
---|
477 |
|
---|
478 | //Use a new SolutionSet to evite alter original solutionSet
|
---|
479 | List<SolutionSet> front = new List<SolutionSet>(size);
|
---|
480 | for(int i = 0; i < size; i++) {
|
---|
481 | front.Add(rankingSubfront[i]);
|
---|
482 | }
|
---|
483 |
|
---|
484 | for(int i = 0; i < size; i++)
|
---|
485 | front[i].CrowdingDistance = 0.0;
|
---|
486 |
|
---|
487 | double objetiveMaxn;
|
---|
488 | double objetiveMinn;
|
---|
489 | double distance;
|
---|
490 |
|
---|
491 | for(int i = 0; i < Problem.Objectives; i++) {
|
---|
492 | // Sort the front population by the objective i
|
---|
493 | front.Sort((x, y) => x.Quality[i].CompareTo(y.Quality[i]));
|
---|
494 | objetiveMinn = front[0].Quality[i];
|
---|
495 | objetiveMaxn = front[front.Count - 1].Quality[i];
|
---|
496 |
|
---|
497 | //Set crowding distance for the current front
|
---|
498 | front[0].CrowdingDistance = double.PositiveInfinity;
|
---|
499 | front[size - 1].CrowdingDistance = double.PositiveInfinity;
|
---|
500 |
|
---|
501 | for(int j = 1; j < size - 1; j++) {
|
---|
502 | distance = front[j + 1].Quality[i] - front[j - 1].Quality[i];
|
---|
503 | distance = distance / (objetiveMaxn - objetiveMinn);
|
---|
504 | distance += front[j].CrowdingDistance;
|
---|
505 | front[j].CrowdingDistance = distance;
|
---|
506 | }
|
---|
507 | }
|
---|
508 | }
|
---|
509 |
|
---|
510 | private void addRankedSolutionToPopulation(List<SolutionSet>[] ranking, int rankingIndex, List<SolutionSet> population) {
|
---|
511 | foreach(SolutionSet solution in ranking[rankingIndex]) {
|
---|
512 | population.Add(solution);
|
---|
513 | }
|
---|
514 | }
|
---|
515 |
|
---|
516 | private bool subFrontFillsIntoThePopulation(List<SolutionSet>[] ranking, int rankingIndex, List<SolutionSet> population) {
|
---|
517 | return ranking[rankingIndex].Count < (PopulationSizeParameter.Value.Value - population.Count);
|
---|
518 | }
|
---|
519 |
|
---|
520 | private bool populationIsNotFull(List<SolutionSet> population) {
|
---|
521 | return population.Count < PopulationSizeParameter.Value.Value;
|
---|
522 | }
|
---|
523 |
|
---|
524 | private List<SolutionSet>[] computeRanking(List<SolutionSet> tmpList) {
|
---|
525 | // dominateMe[i] contains the number of solutions dominating i
|
---|
526 | int[] dominateMe = new int[tmpList.Count];
|
---|
527 |
|
---|
528 | // iDominate[k] contains the list of solutions dominated by k
|
---|
529 | List<int>[] iDominate = new List<int>[tmpList.Count];
|
---|
530 |
|
---|
531 | // front[i] contains the list of individuals belonging to the front i
|
---|
532 | List<int>[] front = new List<int>[tmpList.Count + 1];
|
---|
533 |
|
---|
534 | // flagDominate is an auxiliar encodings.variable
|
---|
535 | int flagDominate;
|
---|
536 |
|
---|
537 | // Initialize the fronts
|
---|
538 | for(int i = 0; i < front.Length; i++) {
|
---|
539 | front[i] = new List<int>();
|
---|
540 | }
|
---|
541 |
|
---|
542 | //-> Fast non dominated sorting algorithm
|
---|
543 | // Contribution of Guillaume Jacquenot
|
---|
544 | for(int p = 0; p < tmpList.Count; p++) {
|
---|
545 | // Initialize the list of individuals that i dominate and the number
|
---|
546 | // of individuals that dominate me
|
---|
547 | iDominate[p] = new List<int>();
|
---|
548 | dominateMe[p] = 0;
|
---|
549 | }
|
---|
550 | for(int p = 0; p < (tmpList.Count - 1); p++) {
|
---|
551 | // For all q individuals , calculate if p dominates q or vice versa
|
---|
552 | for(int q = p + 1; q < tmpList.Count; q++) {
|
---|
553 | flagDominate = compareConstraintsViolation(tmpList[p], tmpList[q]);
|
---|
554 | if(flagDominate == 0) {
|
---|
555 | flagDominate = compareDomination(tmpList[p], tmpList[q]);
|
---|
556 | }
|
---|
557 | if(flagDominate == -1) {
|
---|
558 | iDominate[p].Add(q);
|
---|
559 | dominateMe[q]++;
|
---|
560 | } else if(flagDominate == 1) {
|
---|
561 | iDominate[q].Add(p);
|
---|
562 | dominateMe[p]++;
|
---|
563 | }
|
---|
564 | }
|
---|
565 | // If nobody dominates p, p belongs to the first front
|
---|
566 | }
|
---|
567 | for(int i = 0; i < tmpList.Count; i++) {
|
---|
568 | if(dominateMe[i] == 0) {
|
---|
569 | front[0].Add(i);
|
---|
570 | tmpList[i].Rank = 0;
|
---|
571 | }
|
---|
572 | }
|
---|
573 |
|
---|
574 | //Obtain the rest of fronts
|
---|
575 | int k = 0;
|
---|
576 |
|
---|
577 | while(front[k].Count != 0) {
|
---|
578 | k++;
|
---|
579 | foreach(var it1 in front[k - 1]) {
|
---|
580 | foreach(var it2 in iDominate[it1]) {
|
---|
581 | int index = it2;
|
---|
582 | dominateMe[index]--;
|
---|
583 | if(dominateMe[index] == 0) {
|
---|
584 | front[k].Add(index);
|
---|
585 | tmpList[index].Rank = k;
|
---|
586 | }
|
---|
587 | }
|
---|
588 | }
|
---|
589 | }
|
---|
590 | //<-
|
---|
591 |
|
---|
592 | var rankedSubpopulation = new List<SolutionSet>[k];
|
---|
593 | //0,1,2,....,i-1 are front, then i fronts
|
---|
594 | for(int j = 0; j < k; j++) {
|
---|
595 | rankedSubpopulation[j] = new List<SolutionSet>(front[j].Count);
|
---|
596 | foreach(var it1 in front[j]) {
|
---|
597 | rankedSubpopulation[j].Add(tmpList[it1]);
|
---|
598 | }
|
---|
599 | }
|
---|
600 | return rankedSubpopulation;
|
---|
601 | }
|
---|
602 |
|
---|
603 | private int compareDomination(SolutionSet solution1, SolutionSet solution2) {
|
---|
604 | int dominate1; // dominate1 indicates if some objective of solution1
|
---|
605 | // dominates the same objective in solution2. dominate2
|
---|
606 | int dominate2; // is the complementary of dominate1.
|
---|
607 |
|
---|
608 | dominate1 = 0;
|
---|
609 | dominate2 = 0;
|
---|
610 |
|
---|
611 | int flag; //stores the result of the comparison
|
---|
612 |
|
---|
613 | // Test to determine whether at least a solution violates some constraint
|
---|
614 | if(needToCompareViolations(solution1, solution2)) {
|
---|
615 | return compareConstraintsViolation(solution1, solution2);
|
---|
616 | }
|
---|
617 |
|
---|
618 | // Equal number of violated constraints. Applying a dominance Test then
|
---|
619 | double value1, value2;
|
---|
620 | for(int i = 0; i < Problem.Objectives; i++) {
|
---|
621 | value1 = solution1.Quality[i];
|
---|
622 | value2 = solution2.Quality[i];
|
---|
623 | if(value1 < value2) {
|
---|
624 | flag = -1;
|
---|
625 | } else if(value2 < value1) {
|
---|
626 | flag = 1;
|
---|
627 | } else {
|
---|
628 | flag = 0;
|
---|
629 | }
|
---|
630 |
|
---|
631 | if(flag == -1) {
|
---|
632 | dominate1 = 1;
|
---|
633 | }
|
---|
634 |
|
---|
635 | if(flag == 1) {
|
---|
636 | dominate2 = 1;
|
---|
637 | }
|
---|
638 | }
|
---|
639 |
|
---|
640 | if(dominate1 == dominate2) {
|
---|
641 | return 0; //No one dominate the other
|
---|
642 | }
|
---|
643 | if(dominate1 == 1) {
|
---|
644 | return -1; // solution1 dominate
|
---|
645 | }
|
---|
646 | return 1; // solution2 dominate
|
---|
647 | }
|
---|
648 |
|
---|
649 | private bool needToCompareViolations(SolutionSet solution1, SolutionSet solution2) {
|
---|
650 | bool needToCompare;
|
---|
651 | needToCompare = (solution1.OverallConstrainViolation < 0) || (solution2.OverallConstrainViolation < 0);
|
---|
652 |
|
---|
653 | return needToCompare;
|
---|
654 | }
|
---|
655 |
|
---|
656 | private int compareConstraintsViolation(SolutionSet solution1, SolutionSet solution2) {
|
---|
657 | int result;
|
---|
658 | double overall1, overall2;
|
---|
659 | overall1 = solution1.OverallConstrainViolation;
|
---|
660 | overall2 = solution2.OverallConstrainViolation;
|
---|
661 |
|
---|
662 | if((overall1 < 0) && (overall2 < 0)) {
|
---|
663 | if(overall1 > overall2) {
|
---|
664 | result = -1;
|
---|
665 | } else if(overall2 > overall1) {
|
---|
666 | result = 1;
|
---|
667 | } else {
|
---|
668 | result = 0;
|
---|
669 | }
|
---|
670 | } else if((overall1 == 0) && (overall2 < 0)) {
|
---|
671 | result = -1;
|
---|
672 | } else if((overall1 < 0) && (overall2 == 0)) {
|
---|
673 | result = 1;
|
---|
674 | } else {
|
---|
675 | result = 0;
|
---|
676 | }
|
---|
677 | return result;
|
---|
678 | }
|
---|
679 | }
|
---|
680 | }
|
---|
681 |
|
---|
682 |
|
---|
683 |
|
---|