[16630] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using HEAL.Attic;
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| 23 | using HeuristicLab.Common;
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[16560] | 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Random;
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| 27 | using System.Linq;
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| 28 | using CancellationToken = System.Threading.CancellationToken;
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| 29 |
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| 30 | namespace HeuristicLab.Algorithms.MOEAD {
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| 31 | [Item("Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D)", "MOEA/D implementation adapted from jMetal.")]
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| 32 | [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 125)]
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[16630] | 33 | [StorableType("FE39AD23-B3BF-4368-BB79-FE5BF4C36272")]
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[16560] | 34 | public class MOEADAlgorithm : MOEADAlgorithmBase {
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| 35 | public MOEADAlgorithm() { }
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| 36 |
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| 37 | protected MOEADAlgorithm(MOEADAlgorithm original, Cloner cloner) : base(original, cloner) { }
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| 38 |
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| 39 | public override IDeepCloneable Clone(Cloner cloner) {
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| 40 | return new MOEADAlgorithm(this, cloner);
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| 41 | }
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| 42 |
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| 43 | [StorableConstructor]
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[16630] | 44 | protected MOEADAlgorithm(StorableConstructorFlag deserializing) : base(deserializing) { }
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[16560] | 45 |
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| 46 | protected override void Run(CancellationToken cancellationToken) {
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[16583] | 47 | if (previousExecutionState != ExecutionState.Paused) {
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| 48 | InitializeAlgorithm(cancellationToken);
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| 49 | }
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| 50 |
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[16560] | 51 | var populationSize = PopulationSize.Value;
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| 52 | bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();
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| 53 | var maximumEvaluatedSolutions = MaximumEvaluatedSolutions.Value;
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| 54 | var crossover = Crossover;
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| 55 | var crossoverProbability = CrossoverProbability.Value;
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| 56 | var mutator = Mutator;
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| 57 | var mutationProbability = MutationProbability.Value;
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| 58 | var evaluator = Problem.Evaluator;
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| 59 | var analyzer = Analyzer;
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| 60 | var neighbourhoodSelectionProbability = NeighbourhoodSelectionProbability;
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| 61 | var rand = RandomParameter.Value;
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[16657] | 62 | var normalizeObjectives = NormalizeObjectives;
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| 63 | var maximumNumberOfReplacedSolutions = MaximumNumberOfReplacedSolutions;
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[16560] | 64 |
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| 65 | // cancellation token for the inner operations which should not be immediately cancelled
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| 66 | var innerToken = new CancellationToken();
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| 67 |
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| 68 | while (evaluatedSolutions < maximumEvaluatedSolutions && !cancellationToken.IsCancellationRequested) {
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| 69 | foreach (var subProblemId in Enumerable.Range(0, populationSize).Shuffle(rand)) {
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| 70 | var neighbourType = ChooseNeighborType(rand, neighbourhoodSelectionProbability);
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| 71 | var mates = MatingSelection(rand, subProblemId, 2, neighbourType); // select parents
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| 72 | var s1 = (IScope)population[mates[0]].Individual.Clone();
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| 73 | var s2 = (IScope)population[mates[1]].Individual.Clone();
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| 74 | s1.Parent = s2.Parent = globalScope;
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| 75 |
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| 76 | IScope childScope = null;
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| 77 | // crossover
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| 78 | if (rand.NextDouble() < crossoverProbability) {
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| 79 | childScope = new Scope($"{mates[0]}+{mates[1]}") { Parent = executionContext.Scope };
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| 80 | childScope.SubScopes.Add(s1);
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| 81 | childScope.SubScopes.Add(s2);
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| 82 | var op = executionContext.CreateChildOperation(crossover, childScope);
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| 83 | ExecuteOperation(executionContext, innerToken, op);
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| 84 | childScope.SubScopes.Clear();
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| 85 | }
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| 86 |
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| 87 | // mutation
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| 88 | if (rand.NextDouble() < mutationProbability) {
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| 89 | childScope = childScope ?? s1;
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| 90 | var op = executionContext.CreateChildOperation(mutator, childScope);
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| 91 | ExecuteOperation(executionContext, innerToken, op);
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| 92 | }
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| 93 |
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| 94 | // evaluation
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| 95 | if (childScope != null) {
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| 96 | var op = executionContext.CreateChildOperation(evaluator, childScope);
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| 97 | ExecuteOperation(executionContext, innerToken, op);
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| 98 | var qualities = (DoubleArray)childScope.Variables["Qualities"].Value;
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| 99 | var childSolution = new MOEADSolution(childScope, maximization.Length, 0);
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| 100 | // set child qualities
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| 101 | for (int j = 0; j < maximization.Length; ++j) {
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| 102 | childSolution.Qualities[j] = maximization[j] ? 1 - qualities[j] : qualities[j];
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| 103 | }
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| 104 | IdealPoint.UpdateIdeal(childSolution.Qualities);
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| 105 | NadirPoint.UpdateNadir(childSolution.Qualities);
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| 106 | // update neighbourhood will insert the child into the population
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[16657] | 107 | UpdateNeighbourHood(rand, childSolution, subProblemId, neighbourType, maximumNumberOfReplacedSolutions, normalizeObjectives);
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[16560] | 108 |
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| 109 | ++evaluatedSolutions;
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| 110 | } else {
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| 111 | // no crossover or mutation were applied, a child was not produced, do nothing
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| 112 | }
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| 113 |
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| 114 | if (evaluatedSolutions >= maximumEvaluatedSolutions) {
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| 115 | break;
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| 116 | }
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| 117 | }
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| 118 | // run analyzer
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| 119 | var analyze = executionContext.CreateChildOperation(analyzer, globalScope);
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| 120 | ExecuteOperation(executionContext, innerToken, analyze);
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| 121 |
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| 122 | UpdateParetoFronts();
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[16630] | 123 |
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| 124 | Results.AddOrUpdateResult("IdealPoint", new DoubleArray(IdealPoint));
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| 125 | Results.AddOrUpdateResult("NadirPoint", new DoubleArray(NadirPoint));
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[16560] | 126 | Results.AddOrUpdateResult("Evaluated Solutions", new IntValue(evaluatedSolutions));
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| 127 |
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| 128 | globalScope.SubScopes.Replace(population.Select(x => (IScope)x.Individual));
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| 129 | }
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| 130 | }
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| 131 | }
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| 132 | }
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