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source: branches/MOCMAEvolutionStrategy/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3/CrowdingIndicator.cs @ 14728

Last change on this file since 14728 was 14577, checked in by bwerth, 8 years ago

#2592 made MOCMAES compatible with MultiObjectiveBasicProblem instead of MultiObjectiveTestfunction, fixed Bug in CrowdingIndicator

File size: 2.7 KB
RevLine 
[14269]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;
[14404]23using System.Collections.Generic;
[14269]24using System.Linq;
[14404]25using HeuristicLab.Common;
26using HeuristicLab.Core;
[14577]27using HeuristicLab.Encodings.RealVectorEncoding;
28using HeuristicLab.Optimization;
[14269]29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Algorithms.MOCMAEvolutionStrategy {
32  [Item("CrowdingIndicator", "Selection of Offspring based on CrowdingDistance")]
33  [StorableClass]
[14404]34  internal class CrowdingIndicator : Item, IIndicator {
[14269]35
[14577]36    public int LeastContributer<TR>(IEnumerable<TR> front, Func<TR, double[]> extractor, MultiObjectiveBasicProblem<RealVectorEncoding> problem) {
37      var bounds = problem.Encoding.Bounds;
[14404]38      var extracted = front.Select(extractor.Invoke).ToArray();
[14577]39      if (extracted.Length <= 2) return 0;
[14404]40      var pointsums = new double[extracted.Length];
[14269]41
[14577]42      for (var dim = 0; dim < problem.Maximization.Length; dim++) {
[14404]43        var arr = extracted.Select(x => x[dim]).ToArray();
[14269]44        Array.Sort(arr);
[14577]45        var fmax = problem.Encoding.Bounds[dim % bounds.Rows, 1];
[14404]46        var fmin = bounds[dim % bounds.Rows, 0];
47        var pointIdx = 0;
48        foreach (var point in extracted) {
49          var pos = Array.BinarySearch(arr, point[dim]);
50          var d = pos != 0 && pos != arr.Length - 1 ? (arr[pos + 1] - arr[pos - 1]) / (fmax - fmin) : double.PositiveInfinity;
[14269]51          pointsums[pointIdx] += d;
52          pointIdx++;
53        }
54      }
55      //find min
[14577]56      return pointsums.ArgMin();
[14269]57    }
58
59    [StorableConstructor]
60    protected CrowdingIndicator(bool deserializing) : base(deserializing) { }
[14404]61    protected CrowdingIndicator(CrowdingIndicator original, Cloner cloner) : base(original, cloner) { }
62    public override IDeepCloneable Clone(Cloner cloner) { return new CrowdingIndicator(this, cloner); }
[14269]63    public CrowdingIndicator() { }
64  }
65}
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