source: branches/GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment/3.3/Operators/LocalImprovers/ApproximateLocalSearch.cs @ 15553

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2017 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
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 * GNU General Public License for more details.
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#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;

namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment {
  [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.")]
  [StorableClass]
  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"]; }
    }

    [StorableConstructor]
    protected ApproximateLocalSearch(bool deserializing) : base(deserializing) { }
    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."));
    }

    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) {
      var fit = problemInstance.ToSingleObjective(sol.Evaluation);
      var eval = sol.Evaluation;
      Apply(random, sol.Assignment, ref fit, ref eval, maxCLS, oneMoveProbability, maximumIterations, problemInstance,
        out evaluatedSolutions);
      sol.Evaluation = eval;
    }

      /// <summary>
      /// The implementation differs slightly from Mateus et al. in that the maximumIterations parameter defines a cap
      /// on the number of steps that the local search can perform. While the maxSampleSize parameter corresponds to
      /// the maxItr parameter defined by Mateus et al.
      /// </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>
      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) {
      evaluatedSolutions = 0;
      var capacities = problemInstance.Capacities;
      var demands = problemInstance.Demands;
      var evaluations = 0.0;
      var deltaEvaluationFactor = 1.0 / assignment.Length;
      while (true) {
        int count = 0;
        var CLS = new List<Tuple<NMove, double, Evaluation>>();
        double sum = 0.0;
        do {
          NMove move;
          if (random.NextDouble() < oneMoveProbability)
            move = StochasticNMoveSingleMoveGenerator.GenerateOneMove(random, assignment, capacities);
          else move = StochasticNMoveSingleMoveGenerator.GenerateTwoMove(random, assignment, capacities);
          
          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) {
            CLS.Add(Tuple.Create(move, moveQuality, moveEval));
            sum += 1.0 / moveQuality;
          }
          count++;
        } while (CLS.Count < maxCLS && count < maximumIterations);

        if (CLS.Count == 0) {
          evaluatedSolutions += (int)Math.Ceiling(evaluations);
          return; // END
        } else {
          var ball = random.NextDouble() * sum;
          var selected = CLS.Last();
          foreach (var candidate in CLS) {
            ball -= 1.0 / candidate.Item2;
            if (ball <= 0.0) {
              selected = candidate;
              break;
            }
          }
          NMoveMaker.Apply(assignment, selected.Item1);
          quality = selected.Item2;
          evaluation = selected.Item3;
        }
      }
    }

    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);

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