1 | using HeuristicLab.Common;
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2 | using HeuristicLab.Core;
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3 | using HeuristicLab.Data;
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4 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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5 | using HeuristicLab.Random;
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6 | using System.Linq;
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7 | using CancellationToken = System.Threading.CancellationToken;
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8 |
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9 | namespace HeuristicLab.Algorithms.MOEAD {
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10 | [Item("Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D)", "MOEA/D implementation adapted from jMetal.")]
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11 | [StorableClass]
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12 | [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 125)]
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13 | public class MOEADAlgorithm : MOEADAlgorithmBase {
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14 | public MOEADAlgorithm() { }
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15 |
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16 | protected MOEADAlgorithm(MOEADAlgorithm original, Cloner cloner) : base(original, cloner) { }
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17 |
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18 | public override IDeepCloneable Clone(Cloner cloner) {
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19 | return new MOEADAlgorithm(this, cloner);
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20 | }
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21 |
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22 | [StorableConstructor]
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23 | protected MOEADAlgorithm(bool deserializing) : base(deserializing) { }
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24 |
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25 | protected override void Run(CancellationToken cancellationToken) {
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26 | var populationSize = PopulationSize.Value;
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27 | bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();
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28 | var maximumEvaluatedSolutions = MaximumEvaluatedSolutions.Value;
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29 | var crossover = Crossover;
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30 | var crossoverProbability = CrossoverProbability.Value;
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31 | var mutator = Mutator;
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32 | var mutationProbability = MutationProbability.Value;
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33 | var evaluator = Problem.Evaluator;
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34 | var analyzer = Analyzer;
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35 | var neighbourhoodSelectionProbability = NeighbourhoodSelectionProbability;
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36 | var rand = RandomParameter.Value;
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37 |
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38 | // cancellation token for the inner operations which should not be immediately cancelled
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39 | var innerToken = new CancellationToken();
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40 |
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41 | while (evaluatedSolutions < maximumEvaluatedSolutions && !cancellationToken.IsCancellationRequested) {
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42 | foreach (var subProblemId in Enumerable.Range(0, populationSize).Shuffle(rand)) {
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43 | var neighbourType = ChooseNeighborType(rand, neighbourhoodSelectionProbability);
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44 | var mates = MatingSelection(rand, subProblemId, 2, neighbourType); // select parents
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45 | var s1 = (IScope)population[mates[0]].Individual.Clone();
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46 | var s2 = (IScope)population[mates[1]].Individual.Clone();
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47 | s1.Parent = s2.Parent = globalScope;
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48 |
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49 | IScope childScope = null;
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50 | // crossover
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51 | if (rand.NextDouble() < crossoverProbability) {
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52 | childScope = new Scope($"{mates[0]}+{mates[1]}") { Parent = executionContext.Scope };
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53 | childScope.SubScopes.Add(s1);
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54 | childScope.SubScopes.Add(s2);
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55 | var op = executionContext.CreateChildOperation(crossover, childScope);
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56 | ExecuteOperation(executionContext, innerToken, op);
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57 | childScope.SubScopes.Clear();
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58 | }
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59 |
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60 | // mutation
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61 | if (rand.NextDouble() < mutationProbability) {
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62 | childScope = childScope ?? s1;
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63 | var op = executionContext.CreateChildOperation(mutator, childScope);
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64 | ExecuteOperation(executionContext, innerToken, op);
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65 | }
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66 |
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67 | // evaluation
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68 | if (childScope != null) {
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69 | var op = executionContext.CreateChildOperation(evaluator, childScope);
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70 | ExecuteOperation(executionContext, innerToken, op);
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71 | var qualities = (DoubleArray)childScope.Variables["Qualities"].Value;
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72 | var childSolution = new MOEADSolution(childScope, maximization.Length, 0);
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73 | // set child qualities
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74 | for (int j = 0; j < maximization.Length; ++j) {
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75 | childSolution.Qualities[j] = maximization[j] ? 1 - qualities[j] : qualities[j];
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76 | }
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77 | IdealPoint.UpdateIdeal(childSolution.Qualities);
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78 | NadirPoint.UpdateNadir(childSolution.Qualities);
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79 | // update neighbourhood will insert the child into the population
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80 | UpdateNeighbourHood(rand, childSolution, subProblemId, neighbourType);
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81 |
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82 | ++evaluatedSolutions;
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83 | } else {
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84 | // no crossover or mutation were applied, a child was not produced, do nothing
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85 | }
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86 |
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87 | if (evaluatedSolutions >= maximumEvaluatedSolutions) {
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88 | break;
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89 | }
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90 | }
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91 | // run analyzer
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92 | var analyze = executionContext.CreateChildOperation(analyzer, globalScope);
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93 | ExecuteOperation(executionContext, innerToken, analyze);
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94 |
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95 | UpdateParetoFronts();
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96 | Results.AddOrUpdateResult("Evaluated Solutions", new IntValue(evaluatedSolutions));
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97 |
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98 | globalScope.SubScopes.Replace(population.Select(x => (IScope)x.Individual));
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99 | }
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100 | }
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101 | }
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102 | }
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