source: trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdingDistanceAssignment.cs @ 12012

Last change on this file since 12012 was 12012, checked in by ascheibe, 5 years ago

#2212 merged r12008, r12009, r12010 back into trunk

File size: 4.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Operators;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Common;
31
32namespace HeuristicLab.Algorithms.NSGA2 {
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]
35  public class CrowdingDistanceAssignment : SingleSuccessorOperator {
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."));
55      AttachEventHandlers();
56    }
57
58    [StorableHook(HookType.AfterDeserialization)]
59    private void AttachEventHandlers() {
60      QualitiesParameter.DepthChanged += new EventHandler(QualitiesParameter_DepthChanged);
61    }
62
63    public static void Apply(DoubleArray[] qualities, DoubleValue[] distances) {
64      int populationSize = qualities.Length;
65      int objectiveCount = qualities[0].Length;
66      for (int m = 0; m < objectiveCount; m++) {
67        Array.Sort<DoubleArray, DoubleValue>(qualities, distances, new QualitiesComparer(m));
68
69        distances[0].Value = double.MaxValue;
70        distances[populationSize - 1].Value = double.MaxValue;
71
72        double minQuality = qualities[0][m];
73        double maxQuality = qualities[populationSize - 1][m];
74        for (int i = 1; i < populationSize - 1; i++) {
75          distances[i].Value += (qualities[i + 1][m] - qualities[i - 1][m]) / (maxQuality - minQuality);
76        }
77      }
78    }
79
80    public override IOperation Apply() {
81      DoubleArray[] qualities = QualitiesParameter.ActualValue.ToArray();
82      int populationSize = qualities.Length;
83      DoubleValue[] distances = new DoubleValue[populationSize];
84      for (int i = 0; i < populationSize; i++)
85        distances[i] = new DoubleValue(0);
86
87      CrowdingDistanceParameter.ActualValue = new ItemArray<DoubleValue>(distances);
88     
89      Apply(qualities, distances);
90
91      return base.Apply();
92    }
93
94    private void Initialize(ItemArray<DoubleValue> distances) {
95      for (int i = 0; i < distances.Length; i++) {
96        if (distances[i] == null) distances[i] = new DoubleValue(0);
97        else distances[i].Value = 0;
98      }
99    }
100
101    private class QualitiesComparer : IComparer<DoubleArray> {
102      private int index;
103
104      public QualitiesComparer(int index) {
105        this.index = index;
106      }
107
108      #region IComparer<DoubleArray> Members
109
110      public int Compare(DoubleArray x, DoubleArray y) {
111        if (x[index] < y[index]) return -1;
112        else if (x[index] > y[index]) return +1;
113        else return 0;
114      }
115
116      #endregion
117    }
118
119    public override IDeepCloneable Clone(Cloner cloner) {
120      return new CrowdingDistanceAssignment(this, cloner);
121    }
122  }
123}
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