source: branches/2457_ExpertSystem/HeuristicLab.Problems.GeneralizedQuadraticAssignment/3.3/Operators/LocalImprovers/ApproximateLocalSearch.cs @ 17712

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 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
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 *
 * 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.IntegerVectorEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;
using HEAL.Attic;

namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment {
  /// <summary>
  /// 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
  /// </summary>
  [Item("ApproximateLocalSearch", "The approximate local search 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.")]
  [StorableType("58C75FBC-C586-4048-A60B-DCF967CB2E33")]
  public class ApproximateLocalSearch : SingleSuccessorOperator, IProblemInstanceAwareGQAPOperator,
    IQualityAwareGQAPOperator, IGQAPLocalImprovementOperator, IAssignmentAwareGQAPOperator, IStochasticOperator {
    public IProblem Problem { get; set; }
    public Type ProblemType {
      get { return typeof(GQAP); }
    }

    public ILookupParameter<GQAPInstance> ProblemInstanceParameter {
      get { return (ILookupParameter<GQAPInstance>)Parameters["ProblemInstance"]; }
    }
    public ILookupParameter<IntegerVector> AssignmentParameter {
      get { return (ILookupParameter<IntegerVector>)Parameters["Assignment"]; }
    }
    public ILookupParameter<DoubleValue> QualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
    }
    public ILookupParameter<Evaluation> EvaluationParameter {
      get { return (ILookupParameter<Evaluation>)Parameters["Evaluation"]; }
    }
    public IValueLookupParameter<IntValue> MaximumIterationsParameter {
      get { return (IValueLookupParameter<IntValue>)Parameters["MaximumIterations"]; }
    }
    public ILookupParameter<IntValue> EvaluatedSolutionsParameter {
      get { return (ILookupParameter<IntValue>)Parameters["EvaluatedSolutions"]; }
    }
    public ILookupParameter<IRandom> RandomParameter {
      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
    }
    public IValueLookupParameter<IntValue> MaximumCandidateListSizeParameter {
      get { return (IValueLookupParameter<IntValue>)Parameters["MaximumCandidateListSize"]; }
    }
    public IValueLookupParameter<PercentValue> OneMoveProbabilityParameter {
      get { return (IValueLookupParameter<PercentValue>)Parameters["OneMoveProbability"]; }
    }
    public ILookupParameter<ResultCollection> ResultsParameter {
      get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
    }
    public IValueLookupParameter<BoolValue> GreedyParameter {
      get { return (IValueLookupParameter<BoolValue>)Parameters["Greedy"]; }
    }

    [StorableConstructor]
    protected ApproximateLocalSearch(StorableConstructorFlag _) : base(_) { }
    protected ApproximateLocalSearch(ApproximateLocalSearch original, Cloner cloner) : base(original, cloner) { }
    public ApproximateLocalSearch()
      : base() {
      Parameters.Add(new LookupParameter<GQAPInstance>("ProblemInstance", GQAP.ProblemInstanceDescription));
      Parameters.Add(new LookupParameter<IntegerVector>("Assignment", GQAPSolutionCreator.AssignmentDescription));
      Parameters.Add(new LookupParameter<DoubleValue>("Quality", ""));
      Parameters.Add(new LookupParameter<Evaluation>("Evaluation", GQAP.EvaluationDescription));
      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "The maximum number of iterations that should be performed."));
      Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated solution equivalents."));
      Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator to use."));
      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumCandidateListSize", "The maximum number of candidates that should be found in each step.", new IntValue(10)));
      Parameters.Add(new ValueLookupParameter<PercentValue>("OneMoveProbability", "The probability for performing a 1-move, which is the opposite of performing a 2-move.", new PercentValue(.5)));
      Parameters.Add(new LookupParameter<ResultCollection>("Results", "The result collection that stores the results."));
      Parameters.Add(new ValueLookupParameter<BoolValue>("Greedy", "Whether to use a greedy selection strategy or a probabilistic one.", new BoolValue(true)));
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new ApproximateLocalSearch(this, cloner);
    }

    public static void Apply(IRandom random, GQAPSolution sol, int maxCLS,
      double oneMoveProbability, int maximumIterations,
      GQAPInstance problemInstance, out int evaluatedSolutions, bool greedy = true) {
      var fit = problemInstance.ToSingleObjective(sol.Evaluation);
      var eval = sol.Evaluation;
      Apply(random, sol.Assignment, ref fit, ref eval, maxCLS, oneMoveProbability, maximumIterations, problemInstance,
        out evaluatedSolutions, greedy);
      sol.Evaluation = eval;
    }

    /// <summary>
    /// </summary>
    /// <param name="random">The random number generator to use.</param>
    /// <param name="assignment">The equipment-location assignment vector.</param>
    /// <param name="quality">The solution quality.</param>
    /// <param name="evaluation">The evaluation result of the solution.</param>
    /// <param name="maxCLS">The maximum number of candidates that should be found in each step.</param>
    /// <param name="oneMoveProbability">The probability for performing a 1-move, which is the opposite of performing a 2-move.</param>
    /// <param name="maximumIterations">The maximum number of iterations that should be performed each time the candidate list is generated.</param>
    /// <param name="problemInstance">The problem instance that contains the data.</param>
    /// <param name="evaluatedSolutions">The number of evaluated solutions.</param>
    /// <param name="greedy">Greedy selection performed better in 5 of 8 instances according to the paper</param>
    public static void Apply(IRandom random, IntegerVector assignment,
      ref double quality, ref Evaluation evaluation, int maxCLS,
      double oneMoveProbability, int maximumIterations,
      GQAPInstance problemInstance, out int evaluatedSolutions, bool greedy = true) {
      evaluatedSolutions = 0;
      var capacities = problemInstance.Capacities;
      var demands = problemInstance.Demands;
      var evaluations = 0.0;
      var deltaEvaluationFactor = 1.0 / assignment.Length;
      while (true) { // line 1 of Algorithm 3
        var count = 0; // line 2 of Algorithm 3
        var CLS = new List<Tuple<NMove, double, Evaluation>>(); // line 3 of Algorithm 3
        do {
          var move = Move(random, assignment, oneMoveProbability, capacities); // line 4 of Algorithm 3

          var moveEval = GQAPNMoveEvaluator.Evaluate(move, assignment, evaluation, problemInstance);
          evaluations += move.Indices.Count * deltaEvaluationFactor;
          double moveQuality = problemInstance.ToSingleObjective(moveEval);

          if (moveEval.ExcessDemand <= 0.0 && moveQuality < quality) { // line 5 of Algorithm 3
            CLS.Add(Tuple.Create(move, moveQuality, moveEval)); // line 6 of Algorithm 3
          }
          count++; // line 8 of Algorithm 3
        } while (CLS.Count < maxCLS && count < maximumIterations); // line 9 of Algorithm 3

        if (CLS.Count == 0) { // line 10 of Algorithm 3
          evaluatedSolutions += (int)Math.Ceiling(evaluations);
          return; // END
        } else {
          // line 11 of Algorithm 3
          Tuple<NMove, double, Evaluation> selected;
          if (greedy) {
            selected = CLS.MinItems(x => x.Item2).Shuffle(random).First();
          } else {
            selected = CLS.SampleProportional(random, 1, CLS.Select(x => 1.0 / x.Item2), false, false).Single();
          }
          NMoveMaker.Apply(assignment, selected.Item1);
          quality = selected.Item2;
          evaluation = selected.Item3;
        }
      }
    }

    private static NMove Move(IRandom random, IntegerVector assignment, double oneMoveProbability, DoubleArray capacities) {
      if (random.NextDouble() < oneMoveProbability)
        return StochasticNMoveSingleMoveGenerator.GenerateOneMove(random, assignment, capacities);
      return StochasticNMoveSingleMoveGenerator.GenerateTwoMove(random, assignment, capacities);
    }

    public override IOperation Apply() {
      var evaluation = EvaluationParameter.ActualValue;
      var quality = QualityParameter.ActualValue;
      var fit = quality.Value;
      var evaluatedSolutions = 0;

      Apply(RandomParameter.ActualValue,
        AssignmentParameter.ActualValue,
        ref fit,
        ref evaluation,
        MaximumCandidateListSizeParameter.ActualValue.Value,
        OneMoveProbabilityParameter.ActualValue.Value,
        MaximumIterationsParameter.ActualValue.Value,
        ProblemInstanceParameter.ActualValue,
        out evaluatedSolutions,
        GreedyParameter.ActualValue.Value);

      EvaluationParameter.ActualValue = evaluation;
      quality.Value = fit;
      EvaluatedSolutionsParameter.ActualValue.Value += evaluatedSolutions;
      return base.Apply();
    }
  }
}