source: branches/GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment/3.3/Operators/Crossovers/DiscreteLocationCrossover.cs @ 7813

#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
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 * 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/>.
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#endregion

using System.Collections.Generic;
using System.Linq;
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("DiscreteLocationCrossover", "Combines the assignment to locations from various parents.")]
  [StorableClass]
  public class DiscreteLocationCrossover : GQAPCrossover, ICapacitiesAwareGQAPOperator {

    public ILookupParameter<DoubleArray> CapacitiesParameter {
      get { return (ILookupParameter<DoubleArray>)Parameters["Capacities"]; }
    }

    [StorableConstructor]
    protected DiscreteLocationCrossover(bool deserializing) : base(deserializing) { }
    protected DiscreteLocationCrossover(DiscreteLocationCrossover original, Cloner cloner)
      : base(original, cloner) { }
    public DiscreteLocationCrossover()
      : base() {
      Parameters.Add(new LookupParameter<DoubleArray>("Capacities", GeneralizedQuadraticAssignmentProblem.CapacitiesDescription));
    }

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

    public static IntegerVector Apply(IRandom random, ItemArray<IntegerVector> parents, DoubleArray capacities) {

      var groupedLocations = parents
              .Select(p => p.Select((value, index) => new { Equipment = index, Location = value })
              .GroupBy(x => x.Location)
              .ToDictionary(x => x.Key, y => y.AsEnumerable())).ToArray();

      IntegerVector child = new IntegerVector(parents[0].Length);
      HashSet<int> remainingEquipment = new HashSet<int>(Enumerable.Range(0, child.Length));
      int locations = capacities.Length;
      foreach (var i in Enumerable.Range(0, locations).Shuffle(random)) {
        int parent = random.Next(parents.Length);
        if (!groupedLocations[parent].ContainsKey(i)) {
          int tmp = parent;
          do {
            tmp = (tmp + 1) % parents.Length;
          } while (tmp != parent && !groupedLocations[tmp].ContainsKey(i));
          if (parent == tmp) continue;
          else parent = tmp;
        }
        foreach (var item in groupedLocations[parent][i]) {
          if (remainingEquipment.Contains(item.Equipment)) {
            child[item.Equipment] = i;
            remainingEquipment.Remove(item.Equipment);
          }
        }
      }

      foreach (var equipment in remainingEquipment) {
        int parent = random.Next(parents.Length);
        child[equipment] = parents[parent][equipment];
      }

      return child;
    }

    protected override IntegerVector Cross(IRandom random, ItemArray<IntegerVector> parents) {
      return Apply(random, parents, CapacitiesParameter.ActualValue);
    }
  }
}