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