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source: branches/2521_ProblemRefactoring/HeuristicLab.Problems.PTSP/3.3/AnalyticalPTSP.cs @ 17695

Last change on this file since 17695 was 17695, checked in by abeham, 4 years ago

#2521:

  • Moving solution creator parameter from problems to algorithms (breaking wiring in some HeuristicOptimizationProblems)
  • Disallowing evaluator or encoding changes in encoding-specific base problems (to avoid confusion in derived problems whether this needs to be handled or not)
  • Added private set to ReferenceParameter property (serialization)
File size: 3.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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.Threading;
23using HEAL.Attic;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Encodings.PermutationEncoding;
27using HeuristicLab.Optimization;
28
29namespace HeuristicLab.Problems.PTSP {
30  [Item("Analytical Probabilistic TSP (pTSP)", "Represents a probabilistic traveling salesman problem where the expected tour length is calculated exactly.")]
31  [Creatable(CreatableAttribute.Categories.CombinatorialProblems)]
32  [StorableType("509B6AB5-F4DE-4144-A031-43EEBAD02CA6")]
33  public sealed class AnalyticalPTSP : ProbabilisticTSP {
34
35    [StorableConstructor]
36    private AnalyticalPTSP(StorableConstructorFlag _) : base(_) { }
37    private AnalyticalPTSP(AnalyticalPTSP original, Cloner cloner) : base(original, cloner) { }
38    public AnalyticalPTSP() {
39      Operators.Add(new PTSPAnalyticalInversionMoveEvaluator());
40      Operators.Add(new PTSPAnalyticalInsertionMoveEvaluator());
41      Operators.Add(new PTSPAnalyticalInversionLocalImprovement());
42      Operators.Add(new PTSPAnalyticalInsertionLocalImprovement());
43      Operators.Add(new PTSPAnalyticalTwoPointFiveLocalImprovement());
44
45      Operators.Add(new ExhaustiveTwoPointFiveMoveGenerator());
46      Operators.Add(new StochasticTwoPointFiveMultiMoveGenerator());
47      Operators.Add(new StochasticTwoPointFiveSingleMoveGenerator());
48      Operators.Add(new TwoPointFiveMoveMaker());
49      Operators.Add(new PTSPAnalyticalTwoPointFiveMoveEvaluator());
50
51      Encoding.ConfigureOperators(Operators);
52    }
53
54    public override IDeepCloneable Clone(Cloner cloner) {
55      return new AnalyticalPTSP(this, cloner);
56    }
57
58    public override ISingleObjectiveEvaluationResult Evaluate(Permutation tour, IRandom random, CancellationToken cancellationToken) {
59      var quality = Evaluate(tour, ProbabilisticTSPData, cancellationToken);
60      return new SingleObjectiveEvaluationResult(quality);
61    }
62
63    public static double Evaluate(Permutation tour, IProbabilisticTSPData data, CancellationToken cancellationToken) {
64      // Analytical evaluation
65      var firstSum = 0.0;
66      for (var i = 0; i < tour.Length - 1; i++) {
67        for (var j = i + 1; j < tour.Length; j++) {
68          var prod1 = data.GetDistance(tour[i], tour[j]) * data.GetProbability(tour[i]) * data.GetProbability(tour[j]);
69          for (var k = i + 1; k < j; k++) {
70            prod1 *= (1 - data.GetProbability(tour[k]));
71          }
72          firstSum += prod1;
73        }
74      }
75      var secondSum = 0.0;
76      for (var j = 0; j < tour.Length; j++) {
77        for (var i = 0; i < j; i++) {
78          var prod2 = data.GetDistance(tour[j], tour[i]) * data.GetProbability(tour[i]) * data.GetProbability(tour[j]);
79          for (var k = j + 1; k < tour.Length; k++) {
80            prod2 *= (1 - data.GetProbability(tour[k]));
81          }
82          for (var k = 0; k < i; k++) {
83            prod2 *= (1 - data.GetProbability(tour[k]));
84          }
85          secondSum += prod2;
86        }
87      }
88      return firstSum + secondSum;
89    }
90  }
91}
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