#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 * 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 . */ #endregion using System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.VehicleRouting.Encodings.GVR { [Item("GVRCrossover", "The GVR crossover operation. It is implemented as described in Pereira, F.B. et al (2002). GVR: a New Genetic Representation for the Vehicle Routing Problem. AICS 2002, LNAI 2464, pp. 95-102.")] [StorableClass] public sealed class GVRCrossover : VRPCrossover, IStochasticOperator { public ILookupParameter RandomParameter { get { return (LookupParameter)Parameters["Random"]; } } [StorableConstructor] private GVRCrossover(bool deserializing) : base(deserializing) { } private GVRCrossover(GVRCrossover original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new GVRCrossover(this, cloner); } public GVRCrossover() { Parameters.Add(new LookupParameter("Random", "The pseudo random number generator which should be used for stochastic manipulation operators.")); //remove unused parameters Parameters.Remove("ReadyTime"); Parameters.Remove("DueTime"); Parameters.Remove("ServiceTime"); } private GVREncoding Crossover(IRandom random, GVREncoding parent1, GVREncoding parent2) { GVREncoding child = parent1.Clone() as GVREncoding; Tour tour = parent2.Tours[random.Next(parent2.Tours.Count)]; int breakPoint1 = random.Next(tour.Cities.Count); int length = random.Next(1, tour.Cities.Count - breakPoint1 + 1); List subroute = tour.Cities.GetRange(breakPoint1, length); //remove duplicates List toBeRemoved = new List(); foreach (Tour route in child.Tours) { foreach (int city in subroute) { route.Cities.Remove(city); } if (route.Cities.Count == 0) toBeRemoved.Add(route); } foreach (Tour route in toBeRemoved) { child.Tours.Remove(route); } //choose nearest customer double minDistance = -1; int customer = -1; for (int i = 1; i <= Cities; i++) { if (!subroute.Contains(i)) { double distance = VRPUtilities.GetDistance(subroute[0], i, CoordinatesParameter.ActualValue, DistanceMatrixParameter, UseDistanceMatrixParameter.ActualValue); if (customer == -1 || distance < minDistance) { customer = i; minDistance = distance; } } } //insert if (customer != -1) { Tour newTour; int newPosition; child.FindCustomer(customer, out newTour, out newPosition); newTour.Cities.InsertRange(newPosition + 1, subroute); } else { //special case -> only one tour, whole tour has been chosen as subroute child = parent1.Clone() as GVREncoding; } return child; } public override IOperation Apply() { ItemArray parents = new ItemArray(ParentsParameter.ActualValue.Length); for (int i = 0; i < ParentsParameter.ActualValue.Length; i++) { IVRPEncoding solution = ParentsParameter.ActualValue[i]; if (!(solution is GVREncoding)) { parents[i] = GVREncoding.ConvertFrom(solution, CapacityParameter.ActualValue, DemandParameter.ActualValue, DistanceMatrixParameter); } else { parents[i] = solution; } } ParentsParameter.ActualValue = parents; ChildParameter.ActualValue = Crossover(RandomParameter.ActualValue, parents[0] as GVREncoding, parents[1] as GVREncoding); return base.Apply(); } } }