[5143] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12009] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5143] | 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 System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Parameters;
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| 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 31 |
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| 32 | namespace HeuristicLab.Optimization.Operators {
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| 33 | [Item("CrowdingDistanceAssignment", "Calculates the crowding distances for each sub-scope as described in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.")]
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| 34 | [StorableClass]
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[12005] | 35 | public class CrowdingDistanceAssignment : SingleSuccessorOperator, IMultiObjectiveOperator {
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[5143] | 36 |
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| 37 | public ScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
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| 38 | get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
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| 39 | }
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| 40 |
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| 41 | public ScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
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| 42 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
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| 43 | }
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| 44 |
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| 45 | private void QualitiesParameter_DepthChanged(object sender, EventArgs e) {
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| 46 | CrowdingDistanceParameter.Depth = QualitiesParameter.Depth;
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| 47 | }
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| 48 |
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| 49 | [StorableConstructor]
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| 50 | protected CrowdingDistanceAssignment(bool deserializing) : base(deserializing) { }
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| 51 | protected CrowdingDistanceAssignment(CrowdingDistanceAssignment original, Cloner cloner) : base(original, cloner) { }
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| 52 | public CrowdingDistanceAssignment() {
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| 53 | Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of quality values."));
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| 54 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "Sets the crowding distance in each sub-scope."));
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[7351] | 55 | RegisterEventHandlers();
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[5143] | 56 | }
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| 57 |
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| 58 | [StorableHook(HookType.AfterDeserialization)]
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[7351] | 59 | private void AfterDeserialization() {
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| 60 | RegisterEventHandlers();
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| 61 | }
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| 62 |
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| 63 | private void RegisterEventHandlers() {
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[5143] | 64 | QualitiesParameter.DepthChanged += new EventHandler(QualitiesParameter_DepthChanged);
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| 65 | }
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| 66 |
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| 67 | public static void Apply(DoubleArray[] qualities, DoubleValue[] distances) {
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| 68 | int populationSize = qualities.Length;
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| 69 | int objectiveCount = qualities[0].Length;
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| 70 | for (int m = 0; m < objectiveCount; m++) {
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| 71 | Array.Sort<DoubleArray, DoubleValue>(qualities, distances, new QualitiesComparer(m));
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| 72 |
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| 73 | distances[0].Value = double.MaxValue;
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| 74 | distances[populationSize - 1].Value = double.MaxValue;
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| 75 |
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| 76 | double minQuality = qualities[0][m];
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| 77 | double maxQuality = qualities[populationSize - 1][m];
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| 78 | for (int i = 1; i < populationSize - 1; i++) {
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| 79 | distances[i].Value += (qualities[i + 1][m] - qualities[i - 1][m]) / (maxQuality - minQuality);
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| 80 | }
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| 81 | }
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| 82 | }
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| 83 |
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| 84 | public override IOperation Apply() {
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| 85 | DoubleArray[] qualities = QualitiesParameter.ActualValue.ToArray();
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| 86 | int populationSize = qualities.Length;
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| 87 | DoubleValue[] distances = new DoubleValue[populationSize];
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| 88 | for (int i = 0; i < populationSize; i++)
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| 89 | distances[i] = new DoubleValue(0);
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| 90 |
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| 91 | CrowdingDistanceParameter.ActualValue = new ItemArray<DoubleValue>(distances);
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| 92 |
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| 93 | Apply(qualities, distances);
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| 94 |
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| 95 | return base.Apply();
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| 96 | }
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| 97 |
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| 98 | private void Initialize(ItemArray<DoubleValue> distances) {
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| 99 | for (int i = 0; i < distances.Length; i++) {
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| 100 | if (distances[i] == null) distances[i] = new DoubleValue(0);
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| 101 | else distances[i].Value = 0;
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| 102 | }
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| 103 | }
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| 104 |
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| 105 | private class QualitiesComparer : IComparer<DoubleArray> {
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| 106 | private int index;
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| 107 |
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| 108 | public QualitiesComparer(int index) {
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| 109 | this.index = index;
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| 110 | }
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| 111 |
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| 112 | #region IComparer<DoubleArray> Members
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| 113 |
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| 114 | public int Compare(DoubleArray x, DoubleArray y) {
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| 115 | if (x[index] < y[index]) return -1;
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| 116 | else if (x[index] > y[index]) return +1;
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| 117 | else return 0;
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| 118 | }
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| 119 |
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| 120 | #endregion
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| 121 | }
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| 122 |
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| 123 | public override IDeepCloneable Clone(Cloner cloner) {
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| 124 | return new CrowdingDistanceAssignment(this, cloner);
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| 125 | }
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| 126 | }
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| 127 | }
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