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source: branches/2817-BinPackingSpeedup/HeuristicLab.Optimization.Operators/3.3/MultiObjective/CrowdingDistanceAssignment.cs @ 16654

Last change on this file since 16654 was 16140, checked in by abeham, 6 years ago

#2817: updated to trunk r15680

File size: 4.8 KB
RevLine 
[5143]1#region License Information
2/* HeuristicLab
[16140]3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[5143]4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Operators;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Optimization.Operators {
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.")]
34  [StorableClass]
[11970]35  public class CrowdingDistanceAssignment : SingleSuccessorOperator, IMultiObjectiveOperator {
[5143]36
37    public ScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
38      get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
39    }
40
41    public ScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
42      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
43    }
44
45    private void QualitiesParameter_DepthChanged(object sender, EventArgs e) {
46      CrowdingDistanceParameter.Depth = QualitiesParameter.Depth;
47    }
48
49    [StorableConstructor]
50    protected CrowdingDistanceAssignment(bool deserializing) : base(deserializing) { }
51    protected CrowdingDistanceAssignment(CrowdingDistanceAssignment original, Cloner cloner) : base(original, cloner) { }
52    public CrowdingDistanceAssignment() {
53      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of quality values."));
54      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "Sets the crowding distance in each sub-scope."));
[7351]55      RegisterEventHandlers();
[5143]56    }
57
58    [StorableHook(HookType.AfterDeserialization)]
[7351]59    private void AfterDeserialization() {
60      RegisterEventHandlers();
61    }
62
63    private void RegisterEventHandlers() {
[5143]64      QualitiesParameter.DepthChanged += new EventHandler(QualitiesParameter_DepthChanged);
65    }
66
67    public static void Apply(DoubleArray[] qualities, DoubleValue[] distances) {
68      int populationSize = qualities.Length;
69      int objectiveCount = qualities[0].Length;
70      for (int m = 0; m < objectiveCount; m++) {
71        Array.Sort<DoubleArray, DoubleValue>(qualities, distances, new QualitiesComparer(m));
72
73        distances[0].Value = double.MaxValue;
74        distances[populationSize - 1].Value = double.MaxValue;
75
76        double minQuality = qualities[0][m];
77        double maxQuality = qualities[populationSize - 1][m];
78        for (int i = 1; i < populationSize - 1; i++) {
79          distances[i].Value += (qualities[i + 1][m] - qualities[i - 1][m]) / (maxQuality - minQuality);
80        }
81      }
82    }
83
84    public override IOperation Apply() {
85      DoubleArray[] qualities = QualitiesParameter.ActualValue.ToArray();
86      int populationSize = qualities.Length;
87      DoubleValue[] distances = new DoubleValue[populationSize];
88      for (int i = 0; i < populationSize; i++)
89        distances[i] = new DoubleValue(0);
90
91      CrowdingDistanceParameter.ActualValue = new ItemArray<DoubleValue>(distances);
92
93      Apply(qualities, distances);
94
95      return base.Apply();
96    }
97
98    private void Initialize(ItemArray<DoubleValue> distances) {
99      for (int i = 0; i < distances.Length; i++) {
100        if (distances[i] == null) distances[i] = new DoubleValue(0);
101        else distances[i].Value = 0;
102      }
103    }
104
105    private class QualitiesComparer : IComparer<DoubleArray> {
106      private int index;
107
108      public QualitiesComparer(int index) {
109        this.index = index;
110      }
111
112      #region IComparer<DoubleArray> Members
113
114      public int Compare(DoubleArray x, DoubleArray y) {
115        if (x[index] < y[index]) return -1;
116        else if (x[index] > y[index]) return +1;
117        else return 0;
118      }
119
120      #endregion
121    }
122
123    public override IDeepCloneable Clone(Cloner cloner) {
124      return new CrowdingDistanceAssignment(this, cloner);
125    }
126  }
127}
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