source: branches/ScatterSearch (trunk integration)/HeuristicLab.Problems.TravelingSalesman/3.3/PathRelinkers/TSPMultipleGuidesPathRelinker.cs @ 8319

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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 <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.PermutationEncoding;
using HeuristicLab.Optimization.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.TravelingSalesman {
  /// <summary>
  /// An operator that relinks paths between traveling salesman solutions using a multiple guiding strategy.
  /// </summary>
  /// <remarks>
  /// The operator incrementally changes the initiating solution towards the guiding solution by correcting edges as needed. For each city it choses the best edge from all guiding solutions.
  /// </remarks>
  [Item("TSPMultipleGuidesPathRelinker", "An operator that relinks paths between traveling salesman solutions using a multiple guiding strategy.")]
  [StorableClass]
  public sealed class TSPMultipleGuidesPathRelinker : SingleObjectivePathRelinker {
    #region Parameter properties
    public ILookupParameter<DistanceMatrix> DistanceMatrixParameter {
      get { return (ILookupParameter<DistanceMatrix>)Parameters["DistanceMatrix"]; }
    }
    #endregion

    #region Properties
    public DistanceMatrix DistanceMatrix {
      get { return DistanceMatrixParameter.ActualValue; }
    }
    #endregion

    [StorableConstructor]
    private TSPMultipleGuidesPathRelinker(bool deserializing) : base(deserializing) { }
    private TSPMultipleGuidesPathRelinker(TSPMultipleGuidesPathRelinker original, Cloner cloner) : base(original, cloner) { }
    public TSPMultipleGuidesPathRelinker()
      : base() {
      #region Create parameters
      Parameters.Add(new LookupParameter<DistanceMatrix>("DistanceMatrix", "The matrix which contains the distances between the cities."));
      #endregion
    }

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

    public static ItemArray<IItem> Apply(IItem initiator, IItem[] guides, DistanceMatrix distances, PercentValue n) {
      if (!(initiator is Permutation) || guides.Any(x => !(x is Permutation)))
        throw new ArgumentException("Cannot relink path because some of the provided solutions have the wrong type.");
      if (n.Value <= 0.0)
        throw new ArgumentException("RelinkingAccuracy must be greater than 0.");

      Permutation v1 = initiator.Clone() as Permutation;
      Permutation[] targets = new Permutation[guides.Length];
      Array.Copy(guides, targets, guides.Length);

      if (targets.Any(x => x.Length != v1.Length))
        throw new ArgumentException("At least one solution is of different length.");

      IList<Permutation> solutions = new List<Permutation>();
      for (int i = 0; i < v1.Length; i++) {
        int currCityIndex = i;
        int bestCityIndex = (i + 1) % v1.Length;
        double currDistance = distances[v1[currCityIndex], v1[bestCityIndex]];
        targets.ToList().ForEach(solution => {
          var node = solution.Select((x, index) => new { Id = x, Index = index }).First(x => x.Id == v1[currCityIndex]);
          int pred = solution[(node.Index - 1 + solution.Length) % solution.Length];
          int succ = solution[(node.Index + 1) % solution.Length];
          var results = new[] { pred, succ }.Select(x => new { Id = x, Distance = distances[x, node.Id] });
          if (results.Any(x => x.Distance < currDistance)) {
            var bestCity = results.OrderBy(x => x.Distance).First();
            bestCityIndex = v1.Select((x, index) => new { Id = x, Index = index }).First(x => x.Id == bestCity.Id).Index;
            currDistance = bestCity.Distance;
          }
        });
        Invert(v1, currCityIndex + 1, bestCityIndex);
        solutions.Add(v1.Clone() as Permutation);
      }

      IList<IItem> selection = new List<IItem>();
      if (solutions.Count > 0) {
        int noSol = (int)(solutions.Count * n.Value);
        if (noSol <= 0) noSol++;
        double stepSize = (double)solutions.Count / (double)noSol;
        for (int i = 0; i < noSol; i++)
          selection.Add(solutions.ElementAt((int)((i + 1) * stepSize - stepSize * 0.5)));
      }

      return new ItemArray<IItem>(selection);
    }

    private static void Invert(Permutation sol, int i, int j) {
      if (i != j)
        for (int a = 0; a < Math.Abs(i - j) / 2; a++)
          if (sol[(i + a) % sol.Length] != sol[(j - a + sol.Length) % sol.Length]) {
            // XOR swap
            sol[(i + a) % sol.Length] ^= sol[(j - a + sol.Length) % sol.Length];
            sol[(j - a + sol.Length) % sol.Length] ^= sol[(i + a) % sol.Length];
            sol[(i + a) % sol.Length] ^= sol[(j - a + sol.Length) % sol.Length];
          }
    }

    protected override ItemArray<IItem> Relink(ItemArray<IItem> parents, PercentValue n) {
      if (parents.Length < 2)
        throw new ArgumentException("The number of parents is smaller than 2.");
      return Apply(parents[0], parents.Skip(1).ToArray(), DistanceMatrix, n);
    }
  }
}