#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 HeuristicLab.Problems.VehicleRouting.Interfaces;

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

    public static void Apply(IRandom random, PotvinEncoding individual, IVRPProblemInstance problemInstance, bool allowInfeasible) {
      bool feasible;
      int count = 0;

      do {
        feasible = true;
        if (individual.Tours.Count > 1) {
          int tour1Idx = random.Next(individual.Tours.Count);
          int tour2Idx = random.Next(individual.Tours.Count - 1);
          if (tour2Idx >= tour1Idx)
            tour2Idx++;

          Tour tour1 = individual.Tours[tour1Idx];
          Tour tour2 = individual.Tours[tour2Idx];

          int index1 = random.Next(tour1.Stops.Count);
          int city1 = tour1.Stops[index1];

          int index2 = random.Next(tour2.Stops.Count);
          int city2 = tour2.Stops[index2];

          bool originalFeasible = problemInstance.TourFeasible(tour1, individual) &&
                                  problemInstance.TourFeasible(tour2, individual);

          tour1.Stops[index1] = city2;
          tour2.Stops[index2] = city1;

          if (!allowInfeasible && originalFeasible) {
            feasible = problemInstance.TourFeasible(tour1, individual) &&
                       problemInstance.TourFeasible(tour2, individual);
            if (!feasible) {
              tour1.Stops[index1] = city1;
              tour2.Stops[index2] = city2;
            }
          }
        }
      } while (!feasible && count++ < 100);
    }

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