#region License Information
/* HeuristicLab
 * Copyright (C) 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 HEAL.Attic;

namespace HeuristicLab.Problems.VehicleRouting.Encodings.Zhu {
  [Item("ZhuHeuristicCrossover2", "The Zhu Heuristic Crossover (Version 2). It is implemented as described in Zhu, K.Q. (2000). A New Genetic Algorithm For VRPTW. Proceedings of the International Conference on Artificial Intelligence.")]
  [StorableType("69206FC0-4870-4C78-B8FD-6C6EF2ADDA31")]
  public sealed class ZhuHeuristicCrossover2 : ZhuCrossover {
    [StorableConstructor]
    private ZhuHeuristicCrossover2(StorableConstructorFlag _) : base(_) { }

    public ZhuHeuristicCrossover2()
      : base() {
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new ZhuHeuristicCrossover2(this, cloner);
    }

    private ZhuHeuristicCrossover2(ZhuHeuristicCrossover2 original, Cloner cloner)
      : base(original, cloner) {
    }

    protected override ZhuEncoding Crossover(IRandom random, ZhuEncoding parent1, ZhuEncoding parent2) {
      List<int> p1 = new List<int>(parent1);
      List<int> p2 = new List<int>(parent2);

      ZhuEncoding child = parent2.Clone() as ZhuEncoding;

      if (parent1.Length != parent2.Length)
        return child;

      int breakPoint = random.Next(child.Length);
      int i = breakPoint;
      int predecessor = breakPoint - 1;
      if (predecessor < 0)
        predecessor = predecessor + child.Length;

      int parent1Index = i;
      int parent2Index = i;

      while (i != predecessor) {
        if (i == breakPoint) {
          child[i] = p1[parent1Index];

          p1.Remove(child[i]);
          if (parent1Index >= p1.Count)
            parent1Index = 0;

          p2.Remove(child[i]);
          if (parent2Index >= p2.Count)
            parent2Index = 0;
        }

        if (ProblemInstance.GetDistance(
         child[i] + 1, p1[parent1Index] + 1, child)
         <
         ProblemInstance.GetDistance(
         child[i] + 1, p2[parent2Index] + 1, child)) {
          child[(i + 1) % child.Length] = p1[parent1Index];
        } else {
          child[(i + 1) % child.Length] = p2[parent2Index];
        }

        p1.Remove(child[(i + 1) % child.Length]);
        if (parent1Index >= p1.Count)
          parent1Index = 0;

        p2.Remove(child[(i + 1) % child.Length]);
        if (parent2Index >= p2.Count)
          parent2Index = 0;

        i = (i + 1) % child.Length;
      }

      return child;
    }
  }
}