source: branches/GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment/3.3/SolutionCreators/GreedyRandomizedSolutionCreator.cs @ 15555

Last change on this file since 15555 was 15555, checked in by abeham, 3 years ago

#1614: finished checking the implementation against the paper

File size: 10.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2017 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 System.Threading;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Data;
29using HeuristicLab.Encodings.IntegerVectorEncoding;
30using HeuristicLab.Parameters;
31using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
32using HeuristicLab.Random;
33
34namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment {
35  /// <summary>
36  /// 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
37  /// </summary>
38  [Item("GreedyRandomizedSolutionCreator", "Creates a solution according to the procedure 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.")]
39  [StorableClass]
40  public class GreedyRandomizedSolutionCreator : GQAPStochasticSolutionCreator {
41
42    public IValueLookupParameter<IntValue> MaximumTriesParameter {
43      get { return (IValueLookupParameter<IntValue>)Parameters["MaximumTries"]; }
44    }
45    public IValueLookupParameter<BoolValue> CreateMostFeasibleSolutionParameter {
46      get { return (IValueLookupParameter<BoolValue>)Parameters["CreateMostFeasibleSolution"]; }
47    }
48
49    [StorableConstructor]
50    protected GreedyRandomizedSolutionCreator(bool deserializing) : base(deserializing) { }
51    protected GreedyRandomizedSolutionCreator(GreedyRandomizedSolutionCreator original, Cloner cloner)
52      : base(original, cloner) { }
53    public GreedyRandomizedSolutionCreator()
54      : base() {
55      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumTries", "The maximum number of tries to create a feasible solution after which an exception is thrown. If it is set to 0 or a negative value there will be an infinite number of attempts.", new IntValue(100000)));
56      Parameters.Add(new ValueLookupParameter<BoolValue>("CreateMostFeasibleSolution", "If this is set to true the operator will always succeed, and outputs the solution with the least violation instead of throwing an exception.", new BoolValue(false)));
57    }
58
59    public override IDeepCloneable Clone(Cloner cloner) {
60      return new GreedyRandomizedSolutionCreator(this, cloner);
61    }
62
63    public static IntegerVector CreateSolution(IRandom random, GQAPInstance problemInstance,
64      int maximumTries, bool createMostFeasibleSolution, CancellationToken cancelToken) {
65      var weights = problemInstance.Weights;
66      var distances = problemInstance.Distances;
67      var demands = problemInstance.Demands;
68      var capacities = problemInstance.Capacities.ToArray();
69      var transportCosts = problemInstance.TransportationCosts;
70      var equipments = demands.Length;
71      var locations = capacities.Length;
72      int tries = 0;
73      var assignment = new int[equipments];
74      var slack = new double[locations];
75      double minViolation = double.MaxValue;
76      int[] bestAssignment = null;
77      var F = new List<int>(equipments); // set of (initially) all facilities / equipments
78      var CF = new List<int>(equipments); // set of chosen facilities / equipments
79      var L = new List<int>(locations); // set of (initially) all locations
80      var CL_list = new List<int>(locations); // list of chosen locations
81      var CL_selected = new bool[locations]; // bool decision if location is chosen
82      var T = new List<int>(equipments); // set of facilities / equpiments that can be assigned to the set of chosen locations (CL)
83      var H = new double[locations]; // proportions for choosing locations in stage 1
84      var W = new double[equipments]; // proportions for choosing facilities in stage 2
85      var Z = new double[locations]; // proportions for choosing locations in stage 2
86
87      while (maximumTries <= 0 || tries < maximumTries) {
88        cancelToken.ThrowIfCancellationRequested();
89
90        Array.Copy(capacities, slack, locations); // line 2 of Algorihm 2
91        CF.Clear(); // line 2 of Algorihm 2
92        Array.Clear(CL_selected, 0, locations); // line 2 of Algorihm 2
93        CL_list.Clear(); // line 2 of Algorihm 2
94        T.Clear(); // line 2 of Algorihm 2
95
96        F.Clear(); F.AddRange(Enumerable.Range(0, equipments)); // line 2 of Algorihm 2
97        L.Clear(); L.AddRange(Enumerable.Range(0, locations)); // line 2 of Algorihm 2
98
99        double threshold = 1.0; // line 3 of Algorithm 2
100        do { // line 4 of Algorithm 2
101          if (L.Count > 0 && random.NextDouble() < threshold) { // line 5 of Algorithm 2
102            // H is the proportion that a location is chosen
103            // The paper doesn't mention what happens if the candidate list CL
104            // does not contain an element in which case according to the formula
105            // all H_k elements would be 0 which would be equal to random selection
106            var HH = H.Select((v, i) => new { Index = i, Value = v })
107              .Where(x => !CL_selected[x.Index])
108              .Select(x => x.Value);
109            int l = L.SampleProportional(random, 1, HH, false, false).Single(); // line 6 of Algorithm 2
110            L.Remove(l); // line 7 of Algorithm 2
111            CL_list.Add(l); // line 7 of Algorithm 2
112            CL_selected[l] = true; // line 7 of Algorithm 2
113            for (var k = 0; k < locations; k++)
114              if (!CL_selected[k])
115                H[k] += capacities[k] * capacities[l] / distances[k, l];
116            T = new List<int>(WhereDemandEqualOrLess(F, GetMaximumSlack(slack, CL_selected), demands)); // line 8 of Algorithm 2
117          }
118          if (T.Count > 0) { // line 10 of Algorithm 2
119            // W is the proportion that an equipment is chosen
120            Array.Clear(W, 0, W.Length);
121            var wk = 0;
122            foreach (var k in T) {
123              for (var h = 0; h < equipments; h++) {
124                if (k == h) continue;
125                W[wk] += weights[k, h];
126              }
127              W[wk] *= demands[k];
128              wk++;
129            }
130            var f = T.SampleProportional(random, 1, W.Take(T.Count), false, false) // line 11 of Algorithm 2
131              .Single();
132            T.Remove(f); // line 12 of Algorithm 2
133            F.Remove(f); // line 12 of Algorithm 2
134            CF.Add(f); // line 12 of Algorithm 2
135            var R = WhereSlackGreaterOrEqual(CL_list, demands[f], slack).ToList(); // line 13 of Algorithm 2
136            // Z is the proportion that a location is chosen in stage 2
137            Array.Clear(Z, 0, Z.Length);
138            var zk = 0;
139            foreach (var k in R) {
140              // d is an increase in fitness if f would be assigned to location k
141              var d = problemInstance.InstallationCosts[f, k];
142              foreach (var i in CF) {
143                if (assignment[i] == 0) continue; // i is unassigned
144                var j = assignment[i] - 1;
145                d += transportCosts * weights[f, i] * distances[k, j];
146              }
147              foreach (var h in CL_list) {
148                if (k == h) continue;
149                Z[zk] += slack[k] * capacities[h] / (d * distances[k, h]);
150              }
151              zk++;
152            }
153            int l = R.SampleProportional(random, 1, Z.Take(R.Count), false, false).Single(); // line 14 of Algorithm 2
154            assignment[f] = l + 1; // line 15 of Algorithm 2
155            slack[l] -= demands[f];
156            T = new List<int>(WhereDemandEqualOrLess(F, GetMaximumSlack(slack, CL_selected), demands)); // line 16 of Algorithm 2
157            threshold = 1.0 - (double)T.Count / Math.Max(F.Count, 1.0); // line 17 of Algorithm 2
158          }
159        } while (T.Count > 0 || L.Count > 0); // line 19 of Algorithm 2
160
161        if (maximumTries > 0) tries++;
162
163        if (F.Count == 0) {
164          bestAssignment = assignment.Select(x => x - 1).ToArray();
165          break;
166        } else if (createMostFeasibleSolution) {
167          // complete the solution and remember the one with least violation
168          foreach (var l in L.ToArray()) {
169            CL_list.Add(l);
170            CL_selected[l] = true;
171            L.Remove(l);
172          }
173          while (F.Count > 0) {
174            var f = F.Select((v, i) => new { Index = i, Value = v }).MaxItems(x => demands[x.Value]).SampleRandom(random);
175            var l = CL_list.MaxItems(x => slack[x]).SampleRandom(random);
176            F.RemoveAt(f.Index);
177            assignment[f.Value] = l + 1;
178            slack[l] -= demands[f.Value];
179          }
180          double violation = slack.Select(x => x < 0 ? -x : 0).Sum();
181          if (violation < minViolation) {
182            bestAssignment = assignment.Select(x => x - 1).ToArray();
183            minViolation = violation;
184          }
185          assignment = new int[equipments];
186        }
187      }
188
189      if (bestAssignment == null)
190        throw new InvalidOperationException(String.Format("No solution could be found in {0} tries.", maximumTries));
191
192      return new IntegerVector(bestAssignment);
193    }
194
195    protected override IntegerVector CreateRandomSolution(IRandom random, GQAPInstance problemInstance) {
196      return CreateSolution(random, problemInstance,
197        MaximumTriesParameter.ActualValue.Value,
198        CreateMostFeasibleSolutionParameter.ActualValue.Value,
199        CancellationToken);
200    }
201
202    private static IEnumerable<int> WhereDemandEqualOrLess(IEnumerable<int> facilities, double maximum, DoubleArray demands) {
203      foreach (int f in facilities) {
204        if (demands[f] <= maximum) yield return f;
205      }
206    }
207
208    private static double GetMaximumSlack(double[] slack, bool[] CL) {
209      var max = double.MinValue;
210      for (var i = 0; i < slack.Length; i++) {
211        if (CL[i] && max < slack[i]) max = slack[i];
212      }
213      return max;
214    }
215
216    private static IEnumerable<int> WhereSlackGreaterOrEqual(IEnumerable<int> locations, double minimum, double[] slack) {
217      foreach (int l in locations) {
218        if (slack[l] >= minimum) yield return l;
219      }
220    }
221  }
222}
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