[5143] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17181] | 3 | * Copyright (C) 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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[17097] | 30 | using HEAL.Attic;
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[5143] | 31 |
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| 32 | namespace HeuristicLab.Optimization.Operators {
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| 33 | /// <summary>
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| 34 | /// CrowdedComparisonSorter as described in: Deb, Pratap, Agrawal and Meyarivan, "A Fast and Elitist Multiobjective
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| 35 | /// Genetic Algorithm: NSGA-II", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002
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| 36 | /// </summary>
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| 37 | [Item("CrowdedComparisonSorter", @"CrowdedComparisonSorter as described in: Deb, Pratap, Agrawal and Meyarivan, ""A Fast and Elitist Multiobjective
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| 38 | Genetic Algorithm: NSGA-II"", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002.")]
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[17097] | 39 | [StorableType("48F09742-454D-4BAB-A057-96BDF4ACDCE9")]
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[5143] | 40 | public class CrowdedComparisonSorter : SingleSuccessorOperator {
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| 41 |
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| 42 | public IScopeTreeLookupParameter<IntValue> RankParameter {
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| 43 | get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
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| 44 | }
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| 45 |
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| 46 | public IScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
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| 47 | get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
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| 48 | }
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| 49 |
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| 50 | [StorableConstructor]
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[17097] | 51 | protected CrowdedComparisonSorter(StorableConstructorFlag _) : base(_) { }
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[5143] | 52 | protected CrowdedComparisonSorter(CrowdedComparisonSorter original, Cloner cloner) : base(original, cloner) { }
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| 53 | public CrowdedComparisonSorter() {
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| 54 | Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of the solution."));
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| 55 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "The crowding distance of the solution."));
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| 56 | }
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| 57 |
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| 58 | public override IOperation Apply() {
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| 59 | ItemArray<IntValue> ranks = RankParameter.ActualValue;
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| 60 | ItemArray<DoubleValue> distances = CrowdingDistanceParameter.ActualValue;
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| 61 | int size = ranks.Length;
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| 62 | int[] indices = Enumerable.Range(0, size).ToArray();
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| 63 |
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| 64 | IScope[] scopes = ExecutionContext.Scope.SubScopes.ToArray();
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| 65 | Array.Sort(indices, scopes, new CustomComparer(ranks, distances));
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| 66 | ExecutionContext.Scope.SubScopes.Clear();
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| 67 | ExecutionContext.Scope.SubScopes.AddRange(scopes);
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| 68 | return base.Apply();
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| 69 | }
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| 70 |
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[17097] | 71 | [StorableType("c16dd393-bd08-4a81-abb5-1b7e918a1038")]
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[5143] | 72 | private class CustomComparer : IComparer<int> {
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| 73 | ItemArray<IntValue> ranks;
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| 74 | ItemArray<DoubleValue> distances;
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| 75 |
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| 76 | public CustomComparer(ItemArray<IntValue> ranks, ItemArray<DoubleValue> distances) {
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| 77 | this.ranks = ranks;
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| 78 | this.distances = distances;
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| 79 | }
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| 80 |
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| 81 | #region IComparer<int> Members
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| 82 |
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| 83 | public int Compare(int x, int y) {
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| 84 | if (ranks[x].Value < ranks[y].Value) return -1;
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| 85 | else if (ranks[x].Value > ranks[y].Value) return 1;
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| 86 | else { // ranks are the same -> compare by distance
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| 87 | if (distances[x].Value > distances[y].Value) return -1;
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| 88 | else if (distances[x].Value < distances[y].Value) return 1;
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| 89 | else return 0; // same distance
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| 90 | }
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| 91 | }
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| 92 |
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| 93 | #endregion
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| 94 | }
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| 95 |
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| 96 | public override IDeepCloneable Clone(Cloner cloner) {
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| 97 | return new CrowdedComparisonSorter(this, cloner);
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| 98 | }
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| 99 | }
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| 100 | }
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