[7419] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[15504] | 3 | * Copyright (C) 2002-2017 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[7419] | 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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[7423] | 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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[7419] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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[7423] | 27 | using HeuristicLab.Data;
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[7419] | 28 | using HeuristicLab.Encodings.IntegerVectorEncoding;
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[7423] | 29 | using HeuristicLab.Parameters;
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[7419] | 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[7813] | 31 | using HeuristicLab.Random;
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[7419] | 32 |
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[7425] | 33 | namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment {
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[15555] | 34 | /// <summary>
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| 35 | /// This is an implementation of the algorithm described in Mateus, G.R., Resende, M.G.C. & Silva, R.M.A. J Heuristics (2011) 17: 527. https://doi.org/10.1007/s10732-010-9144-0
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| 36 | /// </summary>
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[7423] | 37 | [Item("GQAPPathRelinking", "Operator that performs path relinking between two solutions. It is described in Mateus, G., Resende, M., and Silva, R. 2011. GRASP with path-relinking for the generalized quadratic assignment problem. Journal of Heuristics 17, Springer Netherlands, pp. 527-565.")]
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[7419] | 38 | [StorableClass]
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[15504] | 39 | public class GQAPPathRelinking : GQAPCrossover, IQualitiesAwareGQAPOperator {
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[7419] | 40 |
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[7423] | 41 | public IScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 42 | get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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| 43 | }
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[15504] | 44 | public IScopeTreeLookupParameter<Evaluation> EvaluationParameter {
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| 45 | get { return (IScopeTreeLookupParameter<Evaluation>)Parameters["Evaluation"]; }
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[7423] | 46 | }
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| 47 |
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[7432] | 48 | public IValueParameter<PercentValue> CandidateSizeFactorParameter {
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| 49 | get { return (IValueParameter<PercentValue>)Parameters["CandidateSizeFactor"]; }
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| 50 | }
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| 51 |
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[7419] | 52 | [StorableConstructor]
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| 53 | protected GQAPPathRelinking(bool deserializing) : base(deserializing) { }
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| 54 | protected GQAPPathRelinking(GQAPPathRelinking original, Cloner cloner) : base(original, cloner) { }
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| 55 | public GQAPPathRelinking()
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| 56 | : base() {
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[15504] | 57 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", ""));
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| 58 | Parameters.Add(new ScopeTreeLookupParameter<Evaluation>("Evaluation", GQAP.EvaluationDescription));
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[7432] | 59 | Parameters.Add(new ValueParameter<PercentValue>("CandidateSizeFactor", "(η) Determines the size of the set of feasible moves in each path-relinking step relative to the maximum size. A value of 50% means that only half of all possible moves are considered each step.", new PercentValue(0.5)));
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[7419] | 60 | }
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| 61 |
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| 62 | public override IDeepCloneable Clone(Cloner cloner) {
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| 63 | return new GQAPPathRelinking(this, cloner);
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| 64 | }
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| 65 |
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[15553] | 66 | public static GQAPSolution Apply(IRandom random,
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| 67 | IntegerVector source, Evaluation sourceEval,
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| 68 | IntegerVector target, Evaluation targetEval,
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| 69 | GQAPInstance problemInstance, double candidateSizeFactor,
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| 70 | out int evaluatedSolutions) {
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| 71 | evaluatedSolutions = 0;
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[15504] | 72 | var demands = problemInstance.Demands;
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| 73 | var capacities = problemInstance.Capacities;
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| 74 | var cmp = new IntegerVectorEqualityComparer();
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| 75 |
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[15555] | 76 | var greedy = true; // greedy performed better according to the paper
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[15553] | 77 | var sFit = problemInstance.ToSingleObjective(sourceEval);
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| 78 | var tFit = problemInstance.ToSingleObjective(targetEval);
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| 79 | GQAPSolution pi_star = sFit < tFit ? new GQAPSolution(source, sourceEval) : new GQAPSolution(target, targetEval); // line 1 of Algorithm 4
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| 80 | double pi_star_Fit = problemInstance.ToSingleObjective(pi_star.Evaluation); // line 2 of Algorithm 4
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| 81 |
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| 82 | var pi_prime = (IntegerVector)source.Clone(); // line 3 of Algorithm 4
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| 83 | //var fix = new bool[demands.Length]; // line 3 of Algorithm 4, note that according to the description it is not necessary to track the fixed equipments
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| 84 | var nonFix = Enumerable.Range(0, demands.Length).ToList(); // line 3 of Algorithm 4
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| 85 | var phi = new HashSet<int>(IntegerVectorEqualityComparer.GetDifferingIndices(pi_prime, target)); // line 4 of Algorithm 4
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[7423] | 86 |
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[15553] | 87 | while (phi.Count > 0) { // line 5 of Algorithm 4
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| 88 | var B = new List<GQAPSolution>((int)Math.Ceiling(phi.Count * candidateSizeFactor)); // line 6 of Algorithm 4
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| 89 | var B_fit = new List<double>(B.Capacity); // line 6 of Algorithm 4 (B is split into two synchronized lists)
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| 90 | foreach (var v in phi) { // line 7 of Algorithm 4
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[7432] | 91 | int oldLocation = pi_prime[v];
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[15553] | 92 | pi_prime[v] = target[v]; // line 8 of Algorithm 4
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| 93 | var pi_dash = MakeFeasible(random, pi_prime, v, nonFix, demands, capacities); // line 9 of Algorithm 4
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| 94 | pi_prime[v] = oldLocation; // not mentioned in Algorithm 4, but seems reasonable
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| 95 | var pi_dash_eval = problemInstance.Evaluate(pi_dash);
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| 96 | evaluatedSolutions++;
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| 97 | var pi_dash_fit = problemInstance.ToSingleObjective(pi_dash_eval);
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[7423] | 98 |
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[15553] | 99 | if (problemInstance.IsFeasible(pi_dash)) { // line 10 of Algorithm 4
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| 100 | if (B.Any(x => cmp.Equals(x.Assignment, pi_dash))) continue; // cond. 2 of line 12 and cond. 1 of line 16 in Algorithm 4
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[7425] | 101 |
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[15553] | 102 | if (B.Count >= candidateSizeFactor * phi.Count) { // line 11 of Algorithm 4
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| 103 | var replacement = B_fit.Select((val, idx) => new { Index = idx, Fitness = val })
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| 104 | .Where(x => x.Fitness >= pi_dash_fit) // cond. 1 in line 12 of Algorithm 4
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| 105 | .Select(x => new { Index = x.Index, Fitness = x.Fitness, Similarity = HammingSimilarityCalculator.CalculateSimilarity(B[x.Index].Assignment, pi_dash) })
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| 106 | .ToArray();
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| 107 | if (replacement.Length > 0) {
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| 108 | var mostSimilar = replacement.MaxItems(x => x.Similarity).First().Index;
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| 109 | B[mostSimilar].Assignment = pi_dash; // line 13 of Algorithm 4
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| 110 | B[mostSimilar].Evaluation = pi_dash_eval; // line 13 of Algorithm 4
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| 111 | B_fit[mostSimilar] = pi_dash_fit; // line 13 of Algorithm 4
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[7425] | 112 | }
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[15553] | 113 | } else { // line 16, condition has been checked above already
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| 114 | B.Add(new GQAPSolution(pi_dash, pi_dash_eval)); // line 17 of Algorithm 4
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| 115 | B_fit.Add(pi_dash_fit); // line 17 of Algorithm 4
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[7425] | 116 | }
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| 117 | }
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[7423] | 118 | }
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[15553] | 119 | if (B.Count > 0) { // line 21 of Algorithm 4
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[15555] | 120 | GQAPSolution pi;
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| 121 | // line 22 of Algorithm 4
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| 122 | if (greedy) {
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| 123 | pi = B.Select((val, idx) => new { Index = idx, Value = val }).MinItems(x => B_fit[x.Index]).Shuffle(random).First().Value;
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| 124 | } else {
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| 125 | pi = B.SampleProportional(random, 1, B_fit.Select(x => 1.0 / x), false).First();
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| 126 | }
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[15553] | 127 | var diff = IntegerVectorEqualityComparer.GetDifferingIndices(pi.Assignment, target); // line 23 of Algorithm 4
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| 128 | var I = phi.Except(diff); // line 24 of Algorithm 4
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| 129 | var i = I.SampleRandom(random); // line 25 of Algorithm 4
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| 130 | //fix[i] = true; // line 26 of Algorithm 4
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| 131 | nonFix.Remove(i); // line 26 of Algorithm 4
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| 132 | pi_prime = pi.Assignment; // line 27 of Algorithm 4
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| 133 | var fit = problemInstance.ToSingleObjective(pi.Evaluation);
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| 134 | if (fit < pi_star_Fit) { // line 28 of Algorithm 4
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| 135 | pi_star_Fit = fit; // line 29 of Algorithm 4
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| 136 | pi_star = pi; // line 30 of Algorithm 4
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[7432] | 137 | }
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[15555] | 138 | } else return pi_star;
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[15492] | 139 | phi = new HashSet<int>(IntegerVectorEqualityComparer.GetDifferingIndices(pi_prime, target));
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[7423] | 140 | }
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| 141 |
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[15555] | 142 | return pi_star;
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[7423] | 143 | }
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| 144 |
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[15504] | 145 | protected override IntegerVector Cross(IRandom random, ItemArray<IntegerVector> parents,
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| 146 | GQAPInstance problemInstance) {
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[15553] | 147 |
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| 148 | var qualities = QualityParameter.ActualValue;
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| 149 | var evaluations = EvaluationParameter.ActualValue;
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| 150 | var betterParent = qualities[0].Value <= qualities[1].Value ? 0 : 1;
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| 151 | var worseParent = 1 - betterParent;
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| 152 | var source = parents[betterParent];
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| 153 | var target = parents[worseParent];
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| 154 |
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| 155 | int evaluatedSolution;
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| 156 | return Apply(random, source, evaluations[betterParent],
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| 157 | target, evaluations[worseParent], problemInstance,
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| 158 | CandidateSizeFactorParameter.Value.Value, out evaluatedSolution).Assignment;
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[7419] | 159 | }
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[7423] | 160 |
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[15553] | 161 | private static IntegerVector MakeFeasible(IRandom random, IntegerVector pi, int equipment, List<int> nonFix, DoubleArray demands, DoubleArray capacities, int maximumTries = 1000) {
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| 162 | int l = pi[equipment];
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| 163 | var slack = ComputeSlack(pi, demands, capacities);
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| 164 | if (slack[l] >= 0) // line 1 of Algorithm 5
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| 165 | return new IntegerVector(pi); // line 2 of Algorithm 5
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[7432] | 166 |
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[15553] | 167 | IntegerVector pi_prime = null;
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| 168 | int k = 0; // line 4 of Algorithm 5
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| 169 | while (k < maximumTries && slack[l] < 0) { // line 5 of Algorithm 5
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| 170 | pi_prime = new IntegerVector(pi); // line 6 of Algorithm 5
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| 171 | do { // line 7 of Algorithm 5
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| 172 | var maxSlack = slack.Max(); // line 8-9 of Algorithm 5
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| 173 | var T = nonFix.Where(x => pi[x] == l && demands[x] <= maxSlack).ToList(); // line 8-9 of Algorithm 5
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| 174 | if (T.Count > 0) { // line 10 of Algorithm 5
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| 175 | int i = T.SampleProportional(random, 1, T.Select(x => demands[x]), false).First(); // line 11 of Algorithm 5
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| 176 | var j = Enumerable.Range(0, capacities.Length)
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| 177 | .Where(x => slack[x] >= demands[i]) // line 12 of Algorithm 5
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| 178 | .SampleRandom(random); // line 13 of Algorithm 5
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| 179 | pi_prime[i] = j; // line 14 of Algorithm 5
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| 180 | slack[j] -= demands[i]; // line 14 of Algorithm 5
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| 181 | slack[l] += demands[i]; // line 14 of Algorithm 5
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| 182 | } else break; // cond. 1 in line 16 of Algorithm 5
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| 183 | } while (slack[l] < 0); // cond. 2 in line 16 of Algorithm 5
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| 184 | k++; // line 17 of Algorithm 5
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[7432] | 185 | }
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[15553] | 186 | return pi_prime; // line 19-23 of Algorithm 5
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[7423] | 187 | }
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[7432] | 188 |
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[15553] | 189 | private static double[] ComputeSlack(IntegerVector assignment, DoubleArray demands, DoubleArray capacities) {
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| 190 | var slack = new double[capacities.Length];
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[7432] | 191 | for (int i = 0; i < assignment.Length; i++) {
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| 192 | slack[assignment[i]] -= demands[i];
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| 193 | }
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| 194 | return slack;
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| 195 | }
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[7419] | 196 | }
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| 197 | }
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