#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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.Threading; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.PermutationEncoding; using HeuristicLab.Operators; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.QuadraticAssignment { [Item("QAPStochasticScrambleLocalImprovement", "Takes a solution and finds the local optimum with respect to the scramble neighborhood by decending along the steepest gradient.")] [StorableClass] public class QAPStochasticScrambleLocalImprovement : SingleSuccessorOperator, ILocalImprovementOperator, IStochasticOperator, ISingleObjectiveOperator { public ILookupParameter LocalIterationsParameter { get { return (ILookupParameter)Parameters["LocalIterations"]; } } public ILookupParameter RandomParameter { get { return (ILookupParameter)Parameters["Random"]; } } public IValueLookupParameter MaximumIterationsParameter { get { return (IValueLookupParameter)Parameters["MaximumIterations"]; } } public ILookupParameter EvaluatedSolutionsParameter { get { return (ILookupParameter)Parameters["EvaluatedSolutions"]; } } public ILookupParameter ResultsParameter { get { return (ILookupParameter)Parameters["Results"]; } } public ILookupParameter AssignmentParameter { get { return (ILookupParameter)Parameters["Assignment"]; } } public ILookupParameter QualityParameter { get { return (ILookupParameter)Parameters["Quality"]; } } public ILookupParameter MaximizationParameter { get { return (ILookupParameter)Parameters["Maximization"]; } } public ILookupParameter WeightsParameter { get { return (ILookupParameter)Parameters["Weights"]; } } public ILookupParameter DistancesParameter { get { return (ILookupParameter)Parameters["Distances"]; } } public IValueLookupParameter NeighborhoodSizeParameter { get { return (IValueLookupParameter)Parameters["NeighborhoodSize"]; } } [StorableConstructor] protected QAPStochasticScrambleLocalImprovement(bool deserializing) : base(deserializing) { } protected QAPStochasticScrambleLocalImprovement(QAPStochasticScrambleLocalImprovement original, Cloner cloner) : base(original, cloner) { } public QAPStochasticScrambleLocalImprovement() : base() { Parameters.Add(new LookupParameter("LocalIterations", "The number of iterations that have already been performed.")); Parameters.Add(new LookupParameter("Random", "The random number generator to use.")); Parameters.Add(new ValueLookupParameter("MaximumIterations", "The maximum amount of iterations that should be performed (note that this operator will abort earlier when a local optimum is reached).", new IntValue(10000))); Parameters.Add(new LookupParameter("EvaluatedSolutions", "The amount of evaluated solutions (here a move is counted only as 4/n evaluated solutions with n being the length of the permutation).")); Parameters.Add(new LookupParameter("Results", "The collection where to store results.")); Parameters.Add(new LookupParameter("Assignment", "The permutation that is to be locally optimized.")); Parameters.Add(new LookupParameter("Quality", "The quality value of the assignment.")); Parameters.Add(new LookupParameter("Maximization", "True if the problem should be maximized or minimized.")); Parameters.Add(new LookupParameter("Weights", "The weights matrix.")); Parameters.Add(new LookupParameter("Distances", "The distances matrix.")); Parameters.Add(new ValueLookupParameter("NeighborhoodSize", "The number of moves to sample from the neighborhood.", new IntValue(100))); } public override IDeepCloneable Clone(Cloner cloner) { return new QAPStochasticScrambleLocalImprovement(this, cloner); } public static void Improve(IRandom random, Permutation assignment, DoubleMatrix weights, DoubleMatrix distances, DoubleValue quality, IntValue localIterations, IntValue evaluatedSolutions, bool maximization, int maxIterations, int neighborhoodSize, CancellationToken cancellation) { for (int i = localIterations.Value; i < maxIterations; i++) { ScrambleMove bestMove = null; double bestQuality = 0; // we have to make an improvement, so 0 is the baseline double evaluations = 0.0; for (int j = 0; j < neighborhoodSize; j++) { var move = StochasticScrambleMultiMoveGenerator.GenerateRandomMove(assignment, random); double moveQuality = QAPScrambleMoveEvaluator.Apply(assignment, move, weights, distances); evaluations += 2.0 * move.ScrambledIndices.Length / assignment.Length; if (maximization && moveQuality > bestQuality || !maximization && moveQuality < bestQuality) { bestQuality = moveQuality; bestMove = move; } } evaluatedSolutions.Value = (int)Math.Ceiling(evaluations); if (bestMove == null) break; ScrambleManipulator.Apply(assignment, bestMove.StartIndex, bestMove.ScrambledIndices); quality.Value += bestQuality; localIterations.Value++; cancellation.ThrowIfCancellationRequested(); } } public override IOperation Apply() { var random = RandomParameter.ActualValue; var maxIterations = MaximumIterationsParameter.ActualValue.Value; var neighborhoodSize = NeighborhoodSizeParameter.ActualValue.Value; var assignment = AssignmentParameter.ActualValue; var maximization = MaximizationParameter.ActualValue.Value; var weights = WeightsParameter.ActualValue; var distances = DistancesParameter.ActualValue; var quality = QualityParameter.ActualValue; var localIterations = LocalIterationsParameter.ActualValue; var evaluations = EvaluatedSolutionsParameter.ActualValue; if (localIterations == null) { localIterations = new IntValue(0); LocalIterationsParameter.ActualValue = localIterations; } Improve(random, assignment, weights, distances, quality, localIterations, evaluations, maximization, maxIterations, neighborhoodSize, CancellationToken); localIterations.Value = 0; return base.Apply(); } } }