#region License Information
/* HeuristicLab
* Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.PermutationEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.PTSP {
[Item("Analytical Probabilistic Traveling Salesman Problem (PTSP)", "Represents a probabilistic traveling salesman problem where the expected tour length is calculated exactly.")]
[Creatable(CreatableAttribute.Categories.CombinatorialProblems)]
[StorableClass]
public sealed class AnalyticalProbabilisticTravelingSalesmanProblem : ProbabilisticTravelingSalesmanProblem {
[StorableConstructor]
private AnalyticalProbabilisticTravelingSalesmanProblem(bool deserializing) : base(deserializing) { }
private AnalyticalProbabilisticTravelingSalesmanProblem(AnalyticalProbabilisticTravelingSalesmanProblem original, Cloner cloner) : base(original, cloner) { }
public AnalyticalProbabilisticTravelingSalesmanProblem() {
Operators.Add(new BestPTSPSolutionAnalyzer());
Operators.Add(new PTSPAnalyticalInversionMoveEvaluator());
Operators.Add(new PTSPAnalyticalInsertionMoveEvaluator());
Operators.Add(new PTSPAnalyticalInversionLocalImprovement());
Operators.Add(new PTSPAnalyticalInsertionLocalImprovement());
Operators.Add(new PTSPAnalyticalTwoPointFiveLocalImprovement());
Operators.Add(new ExhaustiveTwoPointFiveMoveGenerator());
Operators.Add(new StochasticTwoPointFiveMultiMoveGenerator());
Operators.Add(new StochasticTwoPointFiveSingleMoveGenerator());
Operators.Add(new TwoPointFiveMoveMaker());
Operators.Add(new PTSPAnalyticalTwoPointFiveMoveEvaluator());
Operators.RemoveAll(x => x is SingleObjectiveMoveGenerator);
Operators.RemoveAll(x => x is SingleObjectiveMoveMaker);
Operators.RemoveAll(x => x is SingleObjectiveMoveEvaluator);
Encoding.ConfigureOperators(Operators.OfType());
foreach (var twopointfiveMoveOperator in Operators.OfType()) {
twopointfiveMoveOperator.TwoPointFiveMoveParameter.ActualName = "Permutation.TwoPointFiveMove";
}
}
public override IDeepCloneable Clone(Cloner cloner) {
return new AnalyticalProbabilisticTravelingSalesmanProblem(this, cloner);
}
public override double Evaluate(Permutation tour, IRandom random) {
// abeham: Cache in local variable for performance reasons
var distanceMatrix = DistanceMatrix;
return Evaluate(tour, (a, b) => distanceMatrix[a, b], Probabilities);
}
public static double Evaluate(Permutation tour, Func distance, DoubleArray probabilities) {
// Analytical evaluation
var firstSum = 0.0;
for (var i = 0; i < tour.Length - 1; i++) {
for (var j = i + 1; j < tour.Length; j++) {
var prod1 = distance(tour[i], tour[j]) * probabilities[tour[i]] * probabilities[tour[j]];
for (var k = i + 1; k < j; k++) {
prod1 = prod1 * (1 - probabilities[tour[k]]);
}
firstSum += prod1;
}
}
var secondSum = 0.0;
for (var j = 0; j < tour.Length; j++) {
for (var i = 0; i < j; i++) {
var prod2 = distance(tour[j], tour[i]) * probabilities[tour[i]] * probabilities[tour[j]];
for (var k = j + 1; k < tour.Length; k++) {
prod2 = prod2 * (1 - probabilities[tour[k]]);
}
for (var k = 0; k < i; k++) {
prod2 = prod2 * (1 - probabilities[tour[k]]);
}
secondSum += prod2;
}
}
return firstSum + secondSum;
}
public static double Evaluate(Permutation tour, DistanceMatrix distanceMatrix, DoubleArray probabilities) {
return Evaluate(tour, (a, b) => distanceMatrix[a, b], probabilities);
}
}
}