source: branches/FitnessLandscapeAnalysis/HeuristicLab.Analysis.FitnessLandscape.VRP/Manipulators/OrOptManipulator.cs @ 12897

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2011 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.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
using HeuristicLab.Problems.VehicleRouting.Encodings;
using HeuristicLab.Problems.VehicleRouting;
using System;
using HeuristicLab.Problems.VehicleRouting.Interfaces;

namespace HeuristicLab.Analysis.FitnessLandscape.VRP {
  [Item("OrOptManipulator", "Or opt manipulation")]
  [StorableClass]
  public sealed class OrOptManipulator : PotvinManipulator {
    [StorableConstructor]
    private OrOptManipulator(bool deserializing) : base(deserializing) { }
    private OrOptManipulator(OrOptManipulator original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new OrOptManipulator(this, cloner);
    }
    public OrOptManipulator()
      : base() {
    }

    public static void Apply(IRandom random, PotvinEncoding individual, IVRPProblemInstance instance, bool allowInfeasible) {
      List<Tour> tours = individual.Tours.FindAll(t => t.Stops.Count >= 2);
      if (tours.Count == 0) return;
      bool feasible;
      int count = 0;

      do {
        feasible = true;
        int tourIdx = random.Next(tours.Count);
        Tour tour = tours[tourIdx];

        int segmentStart = random.Next(tour.Stops.Count);
        int segmentLength;
        if (segmentStart == 0) {
          segmentLength = 1 + random.Next(tour.Stops.Count - 1);
        }
        else {
          segmentLength = 1 + random.Next(tour.Stops.Count - segmentStart);
        }

        bool originalFeasible = instance.TourFeasible(tour, individual);

        List<int> segment = tour.Stops.GetRange(segmentStart, segmentLength);
        tour.Stops.RemoveRange(segmentStart, segmentLength);
        int newPos;
        if (tour.Stops.Count == 1)
          newPos = 0;
        else
          newPos = random.Next(tour.Stops.Count - 1);

        if (newPos >= segmentStart)
          newPos++;
        tour.Stops.InsertRange(newPos, segment);

        if (!allowInfeasible && originalFeasible) {
          feasible = instance.TourFeasible(tour, individual);
          if (!feasible) {
            tour.Stops.RemoveRange(newPos, segment.Count);
            tour.Stops.InsertRange(segmentStart, segment);
          }
        }
      } while (!feasible && count++ < 100);
    }

    protected override void Manipulate(IRandom random, PotvinEncoding individual) {
      bool allowInfeasible = AllowInfeasibleSolutions.Value.Value;
      Apply(random, individual, ProblemInstance, allowInfeasible);
    }
  }
}