source: branches/2994-AutoDiffForIntervals/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3/Indicators/MinimalDistanceIndicator.cs @ 17209

Last change on this file since 17209 was 17209, checked in by gkronber, 5 weeks ago

#2994: merged r17132:17198 from trunk to branch

File size: 3.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Encodings.RealVectorEncoding;
28using HeuristicLab.Optimization;
29using HEAL.Attic;
30
31namespace HeuristicLab.Algorithms.MOCMAEvolutionStrategy {
32  [Item("MinimalDistanceIndicator", "Selection of Offspring based on distance to nearest neighbour")]
33  [StorableType("FBBD4517-164C-4DEE-B87D-49B99172EDF4")]
34  internal class MinimalDistanceIndicator : Item, IIndicator {
35
36    #region Constructor and Cloning
37    [StorableConstructor]
38    protected MinimalDistanceIndicator(StorableConstructorFlag _) : base(_) { }
39    protected MinimalDistanceIndicator(MinimalDistanceIndicator original, Cloner cloner) : base(original, cloner) { }
40    public override IDeepCloneable Clone(Cloner cloner) { return new MinimalDistanceIndicator(this, cloner); }
41    public MinimalDistanceIndicator() { }
42    #endregion
43
44    public int LeastContributer(IReadOnlyList<Individual> front, MultiObjectiveBasicProblem<RealVectorEncoding> problem) {
45      var extracted = front.Select(x => x.PenalizedFitness).ToArray();
46      if (extracted.Length <= 2) return 0;
47      var distances = CalcDistances(extracted);
48      var mindexI = 0;
49      var mindexJ = 0;
50      var min = double.MaxValue;
51      for (var i = 0; i < extracted.Length; i++) {
52        var d = double.MaxValue;
53        var minj = 0;
54        for (var j = 0; j < extracted.Length; j++) {
55          if (i == j) continue;
56          var d1 = distances[i, j];
57          if (!(d1 < d)) continue;
58          minj = j;
59          d = d1;
60        }
61        if (!(d < min)) continue;
62        min = d;
63        mindexI = i;
64        mindexJ = minj;
65      }
66
67      //break tie with distance to second nearest
68      var minI = double.MaxValue;
69      var minJ = double.MaxValue;
70
71      for (var i = 0; i < extracted.Length; i++) {
72        double d;
73        if (mindexI != i) {
74          d = distances[mindexI, i];
75          if (!d.IsAlmost(min)) minI = Math.Min(minI, d);
76        }
77        if (mindexJ == i) continue;
78        d = distances[mindexJ, i];
79        if (!d.IsAlmost(min)) minJ = Math.Min(minJ, d);
80      }
81
82      //find min
83      return minI < minJ ? mindexI : mindexJ;
84    }
85
86    #region Helpers
87    private static double[,] CalcDistances(IReadOnlyList<double[]> extracted) {
88      var res = new double[extracted.Count, extracted.Count];
89      for (var i = 0; i < extracted.Count; i++)
90        for (var j = 0; j < i; j++)
91          res[i, j] = res[j, i] = Dist(extracted[i], extracted[j]);
92      return res;
93    }
94
95    private static double Dist(IEnumerable<double> a, IEnumerable<double> b) {
96      return Math.Sqrt(a.Zip(b, (x, y) => (x - y) * (x - y)).Sum());
97    }
98    #endregion
99  }
100}
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