source: branches/1614_GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment/3.3/Operators/Crossovers/DiscreteLocationCrossover.cs

#region License Information
/* HeuristicLab
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 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
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 * GNU General Public License for more details.
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#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.IntegerVectorEncoding;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;
using HEAL.Attic;

namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment {
  [Item("DiscreteLocationCrossover", "Combines the assignment to locations from various parents.")]
  [StorableType("E001CEB3-DAA4-4AF4-843B-2DD951F0EAA6")]
  public class DiscreteLocationCrossover : GQAPCrossover {

    [StorableConstructor]
    protected DiscreteLocationCrossover(StorableConstructorFlag _) : base(_) { }
    protected DiscreteLocationCrossover(DiscreteLocationCrossover original, Cloner cloner)
      : base(original, cloner) { }
    public DiscreteLocationCrossover()
      : base() {
    }

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

    public static IntegerVector Apply(IRandom random, ItemArray<IntegerVector> parents, DoubleArray demands, DoubleArray capacities) {
      var locations = capacities.Length;
      var cap = Math.Max(parents[0].Length / locations, 1);
      var lookup = new List<int>[parents.Length][];
      for (var p = 0; p < parents.Length; p++) {
        lookup[p] = new List<int>[locations];
        var assign = parents[p];
        for (var e = 0; e < parents[p].Length; e++) {
          var loc = assign[e];
          if (lookup[p][loc] == null) lookup[p][loc] = new List<int>(cap);
          lookup[p][loc].Add(e);
        }
      }

      var slack = capacities.ToArray();
      IntegerVector child = new IntegerVector(parents[0].Length);
      var takenEquip = new bool[child.Length];
      foreach (var loc in Enumerable.Range(0, locations).Shuffle(random)) {
        int parent = random.Next(parents.Length);
        if (lookup[parent][loc] == null) {
          int tmp = parent;
          do {
            tmp = (tmp + 1) % parents.Length;
          } while (tmp != parent && lookup[tmp][loc] == null);
          if (parent == tmp) continue;
          else parent = tmp;
        }
        foreach (var equip in lookup[parent][loc]) {
          if (!takenEquip[equip]) {
            child[equip] = loc;
            takenEquip[equip] = true;
            slack[loc] -= demands[equip];
          }
        }
      }

      var order = Enumerable.Range(0, takenEquip.Length)
        .Where(x => !takenEquip[x])
        .Shuffle(random); // avoid bias
      foreach (var e in order) {
        var assigned = false;
        // try 1: find a parent where equipment can be assigned feasibly
        var fallback = -1;
        var count = 1;
        foreach (var p in parents.Shuffle(random)) {
          if (slack[p[e]] >= demands[e]) {
            child[e] = p[e];
            slack[child[e]] -= demands[e];
            assigned = true;
            break;
          } else if (random.NextDouble() < 1.0 / count) {
            fallback = p[e];
          }
          count++;
        }
        // try 2: find a random feasible location
        if (!assigned) {
          var possible = Enumerable.Range(0, locations).Where(x => slack[x] >= demands[e]).ToList();
          if (possible.Count > 0) {
            var loc = possible.SampleRandom(random);
            child[e] = loc;
            slack[loc] -= demands[e];
          } else {
            // otherwise: fallback
            child[e] = fallback;
            slack[child[e]] -= demands[e];
          }
        }
      }

      return child;
    }

    protected override IntegerVector Cross(IRandom random, ItemArray<IntegerVector> parents,
      GQAPInstance problemInstance) {
      return Apply(random, parents, problemInstance.Demands, problemInstance.Capacities);
    }
  }
}