[13672] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[15583] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[13672] | 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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[13936] | 21 | using System;
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[16171] | 22 | using System.Linq;
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[13620] | 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Encodings.RealVectorEncoding;
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[16171] | 25 | using HeuristicLab.Optimization;
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[13620] | 26 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 27 |
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[14111] | 28 | namespace HeuristicLab.Problems.TestFunctions.MultiObjective {
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[13620] | 29 | [StorableClass]
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[13936] | 30 | public abstract class IHR : MultiObjectiveTestFunction {
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[14068] | 31 | protected override double[,] GetBounds(int objectives) {
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[13936] | 32 | return new double[,] { { -1, 1 } };
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[13620] | 33 | }
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| 34 |
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[14068] | 35 | protected override bool[] GetMaximization(int objectives) {
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[13620] | 36 | return new bool[objectives];
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| 37 | }
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| 38 |
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[14068] | 39 | protected override double[] GetReferencePoint(int objectives) {
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[16171] | 40 | var rp = new double[objectives];
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| 41 | for (var i = 0; i < objectives; i++) {
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[13620] | 42 | rp[i] = 11;
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| 43 | }
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| 44 | return rp;
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| 45 | }
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| 46 |
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| 47 | [StorableConstructor]
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[13936] | 48 | protected IHR(bool deserializing) : base(deserializing) { }
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| 49 | protected IHR(IHR original, Cloner cloner) : base(original, cloner) { }
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[14067] | 50 | public IHR() : base(minimumObjectives: 2, maximumObjectives: 2, minimumSolutionLength: 2, maximumSolutionLength: int.MaxValue) { }
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[13620] | 51 |
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[13936] | 52 | public override double[] Evaluate(RealVector r, int objectives) {
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| 53 | if (r.Length < objectives) {
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| 54 | throw new ArgumentException("The dimensionality of the problem(ProblemSize) must be larger than or equal to the number of objectives");
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| 55 | }
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[14090] | 56 | return new double[] { F1(r), F2(r) };
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[13936] | 57 | }
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| 58 | protected abstract double F1(RealVector y);
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[14090] | 59 | protected abstract double F2(RealVector y);
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[13936] | 60 | protected abstract double G(RealVector y);
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[13620] | 61 |
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[16171] | 62 | protected override double GetBestKnownHypervolume(int objectives) {
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| 63 | return HypervolumeCalculator.CalculateHypervolume(GetOptimalParetoFront(objectives).ToArray(), GetReferencePoint(objectives), GetMaximization(objectives));
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| 64 | }
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[13936] | 65 |
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| 66 | protected double H(double x, RealVector r) {
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| 67 | return 1.0 / (1 + Math.Exp(-x / Math.Sqrt(r.Length)));
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| 68 | }
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| 69 | protected double HF(double x, RealVector r) {
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| 70 | double ymax = 1;
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[13988] | 71 | return Math.Abs(r[0]) <= ymax ? x : 1 + Math.Abs(r[0]);
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[13936] | 72 | }
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| 73 | protected double HG(double x) {
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| 74 | return (x * x) / (Math.Abs(x) + 0.1);
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| 75 | }
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[13620] | 76 | }
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| 77 | }
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