source: branches/GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment/3.3/SolutionCreators/SlackMinimizationSolutionCreator.cs @ 13770

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
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 * 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 System.Threading;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.IntegerVectorEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment {
  [Item("SlackMinimizationSolutionCreator", "A heuristic that creates a solution to the Generalized Quadratic Assignment Problem by minimizing the amount of slack.")]
  [StorableClass]
  public class SlackMinimizationSolutionCreator : GQAPStochasticSolutionCreator,
    IEvaluatorAwareGQAPOperator {

    public IValueLookupParameter<IntValue> MaximumTriesParameter {
      get { return (IValueLookupParameter<IntValue>)Parameters["MaximumTries"]; }
    }
    public IValueLookupParameter<BoolValue> CreateMostFeasibleSolutionParameter {
      get { return (IValueLookupParameter<BoolValue>)Parameters["CreateMostFeasibleSolution"]; }
    }
    public IValueLookupParameter<IntValue> DepthParameter {
      get { return (IValueLookupParameter<IntValue>)Parameters["Depth"]; }
    }
    public IValueLookupParameter<IntValue> RandomWalkLengthParameter {
      get { return (IValueLookupParameter<IntValue>)Parameters["RandomWalkLength"]; }
    }
    public IValueLookupParameter<IGQAPEvaluator> EvaluatorParameter {
      get { return (IValueLookupParameter<IGQAPEvaluator>)Parameters["Evaluator"]; }
    }

    [StorableConstructor]
    protected SlackMinimizationSolutionCreator(bool deserializing) : base(deserializing) { }
    protected SlackMinimizationSolutionCreator(SlackMinimizationSolutionCreator original, Cloner cloner) : base(original, cloner) { }
    public SlackMinimizationSolutionCreator()
      : base() {
      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumTries", "The maximum number of tries to create a feasible solution after which an exception is thrown. If it is set to 0 or a negative value there will be an infinite number of attempts to create a feasible solution.", new IntValue(100000)));
      Parameters.Add(new ValueLookupParameter<BoolValue>("CreateMostFeasibleSolution", "If this is set to true the operator will always succeed, and outputs the solution with the least violation instead of throwing an exception.", new BoolValue(false)));
      Parameters.Add(new ValueLookupParameter<IntValue>("Depth", "How deep the algorithm should look forward.", new IntValue(3)));
      Parameters.Add(new ValueLookupParameter<IntValue>("RandomWalkLength", "The length of the random walk in the feasible region that is used to diversify the found assignments.", new IntValue(10)));
      Parameters.Add(new ValueLookupParameter<IGQAPEvaluator>("Evaluator", "The evaluator that is used to evaluate GQAP solutions."));
    }

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

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      if (!Parameters.ContainsKey("Depth")) {
        Parameters.Add(new ValueLookupParameter<IntValue>("Depth", "How deep the algorithm should look forward.", new IntValue(3)));
      }
      if (!Parameters.ContainsKey("RandomWalkLength")) {
        Parameters.Add(new ValueLookupParameter<IntValue>("RandomWalkLength", "The length of the random walk in the feasible region that is used to diversify the found assignments.", new IntValue(10)));
      }
    }

    public static IntegerVector CreateSolution(IRandom random, DoubleArray demands,
      DoubleArray capacities, IGQAPEvaluator evaluator,
      int depth, int maximumTries, bool createMostFeasibleSolution, int randomWalkLength, CancellationToken cancel) {
      IntegerVector result = null;
      bool isFeasible = false;
      int counter = 0;
      double minViolation = double.MaxValue;
      var slack = new DoubleArray(capacities.Length);
      var assignment = new Dictionary<int, int>(demands.Length);

      while (!isFeasible) {
        cancel.ThrowIfCancellationRequested();
        if (maximumTries > 0) {
          counter++;
          if (counter > maximumTries) {
            if (createMostFeasibleSolution) break;
            else throw new InvalidOperationException("A feasible solution could not be obtained after " + maximumTries + " attempts.");
          }
        }
        assignment.Clear();
        for (int i = 0; i < capacities.Length; i++) slack[i] = capacities[i];
        var remainingEquipment = new HashSet<int>(Enumerable.Range(0, demands.Length));
        while (remainingEquipment.Any()) {
          var minimumDemand = remainingEquipment.Min(x => demands[x]);
          var possibleLocations = Enumerable.Range(0, capacities.Length).Where(x => slack[x] >= minimumDemand);
          if (!possibleLocations.Any()) break;
          foreach (var location in possibleLocations.Shuffle(random)) {
            var group = FindBestGroup(location, slack[location], remainingEquipment, demands, depth);
            foreach (var eq in group) {
              remainingEquipment.Remove(eq);
              assignment[eq] = location;
              slack[location] -= demands[eq];
            }
          }
        }
        if (assignment.Count != demands.Length) {
          // complete the solution
          while (remainingEquipment.Any()) {
            var f = remainingEquipment.MaxItems(x => demands[x]).SampleRandom(random);
            var l = Enumerable.Range(0, capacities.Length).MaxItems(x => slack[x]).SampleRandom(random);
            remainingEquipment.Remove(f);
            assignment.Add(f, l);
            slack[l] -= demands[f];
          }
        } else RandomFeasibleWalk(random, assignment, demands, slack, randomWalkLength);
        double violation = evaluator.EvaluateOverbooking(slack, capacities);
        isFeasible = violation == 0;
        if (isFeasible || violation < minViolation) {
          result = new IntegerVector(assignment.OrderBy(x => x.Key).Select(x => x.Value).ToArray());
          minViolation = violation;
        }
      }
      return result;
    }

    private static IEnumerable<int> FindBestGroup(int location, double slack, HashSet<int> remainingEquipment, DoubleArray demands, int depth = 3) {
      var feasibleEquipment = remainingEquipment.Where(x => demands[x] <= slack).ToArray();

      if (!feasibleEquipment.Any()) yield break;
      if (depth == 0) {
        var e = feasibleEquipment.MaxItems(x => demands[x]).First();
        yield return e;
        yield break;
      }

      double bestSlack = slack;
      int bestEquipment = -1;
      int[] bestColleagues = new int[0];
      foreach (var e in feasibleEquipment) {
        remainingEquipment.Remove(e);
        var colleagues = FindBestGroup(location, slack - demands[e], remainingEquipment, demands, depth - 1).ToArray();
        var slackWithColleagues = slack - demands[e] - colleagues.Sum(x => demands[x]);
        if (bestSlack > slackWithColleagues || (bestSlack == slackWithColleagues && colleagues.Length < bestColleagues.Length)) {
          bestSlack = slackWithColleagues;
          bestEquipment = e;
          bestColleagues = colleagues;
        }
        remainingEquipment.Add(e);
      }
      yield return bestEquipment;
      foreach (var a in bestColleagues) yield return a;
    }

    private static void RandomFeasibleWalk(IRandom random, Dictionary<int, int> assignment, DoubleArray demands, DoubleArray slack, int walkLength) {
      for (int i = 0; i < walkLength; i++) {
        var equipments = Enumerable.Range(0, demands.Length).Shuffle(random);
        foreach (var e in equipments) {
          var partners = Enumerable.Range(0, demands.Length)
            .Where(x => slack[assignment[x]] + demands[x] - demands[e] >= 0
                && slack[assignment[e]] + demands[e] - demands[x] >= 0);
          if (!partners.Any()) continue;
          var f = partners.SampleRandom(random);
          int h = assignment[e];
          assignment[e] = assignment[f];
          assignment[f] = h;
          slack[assignment[e]] += demands[f] - demands[e];
          slack[assignment[f]] += demands[e] - demands[f];
          break;
        }
      }
    }

    protected override IntegerVector CreateRandomSolution(IRandom random, DoubleArray demands, DoubleArray capacities) {
      return CreateSolution(random, demands, capacities,
        EvaluatorParameter.ActualValue,
        DepthParameter.ActualValue.Value,
        MaximumTriesParameter.ActualValue.Value,
        CreateMostFeasibleSolutionParameter.ActualValue.Value,
        RandomWalkLengthParameter.ActualValue.Value,
        CancellationToken);
    }
  }
}