[8346] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
[16662] | 3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
[8346] | 4 | *
|
---|
| 5 | * This file is part of HeuristicLab.
|
---|
| 6 | *
|
---|
| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
| 8 | * it under the terms of the GNU General Public License as published by
|
---|
| 9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
| 10 | * (at your option) any later version.
|
---|
| 11 | *
|
---|
| 12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
| 15 | * GNU General Public License for more details.
|
---|
| 16 | *
|
---|
| 17 | * You should have received a copy of the GNU General Public License
|
---|
| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
| 19 | */
|
---|
| 20 | #endregion
|
---|
| 21 |
|
---|
| 22 | using System;
|
---|
| 23 | using System.Collections.Generic;
|
---|
| 24 | using System.Linq;
|
---|
| 25 | using HeuristicLab.Common;
|
---|
| 26 | using HeuristicLab.Core;
|
---|
| 27 | using HeuristicLab.Data;
|
---|
| 28 | using HeuristicLab.Optimization;
|
---|
| 29 | using HeuristicLab.Optimization.Operators;
|
---|
| 30 | using HeuristicLab.Parameters;
|
---|
[16662] | 31 | using HEAL.Attic;
|
---|
[8346] | 32 | using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
|
---|
| 33 | using HeuristicLab.Problems.VehicleRouting.Interfaces;
|
---|
| 34 | using HeuristicLab.Problems.VehicleRouting.ProblemInstances;
|
---|
| 35 | using HeuristicLab.Problems.VehicleRouting.Variants;
|
---|
| 36 |
|
---|
| 37 | namespace HeuristicLab.Problems.VehicleRouting {
|
---|
| 38 | /// <summary>
|
---|
[8894] | 39 | /// An operator which relinks paths between VRP solutions.
|
---|
[8346] | 40 | /// </summary>
|
---|
[8894] | 41 | [Item("VRPPathRelinker", "An operator which relinks paths between VRP solutions.")]
|
---|
[16662] | 42 | [StorableType("C0C17982-BC36-4DF9-8C33-2B6F9A19CA53")]
|
---|
[8346] | 43 | public sealed class VRPPathRelinker : SingleObjectivePathRelinker, IGeneralVRPOperator, IStochasticOperator {
|
---|
| 44 | #region Parameter properties
|
---|
[8894] | 45 | public IValueParameter<IntValue> IterationsParameter {
|
---|
| 46 | get { return (IValueParameter<IntValue>)Parameters["Iterations"]; }
|
---|
[8346] | 47 | }
|
---|
| 48 | public ILookupParameter<IVRPProblemInstance> ProblemInstanceParameter {
|
---|
| 49 | get { return (ILookupParameter<IVRPProblemInstance>)Parameters["ProblemInstance"]; }
|
---|
| 50 | }
|
---|
[8894] | 51 | public ILookupParameter<IRandom> RandomParameter {
|
---|
| 52 | get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
|
---|
| 53 | }
|
---|
[8346] | 54 | public IValueParameter<IntValue> SampleSizeParameter {
|
---|
| 55 | get { return (IValueParameter<IntValue>)Parameters["SampleSize"]; }
|
---|
| 56 | }
|
---|
| 57 | #endregion
|
---|
| 58 |
|
---|
| 59 | [StorableConstructor]
|
---|
[16662] | 60 | private VRPPathRelinker(StorableConstructorFlag _) : base(_) { }
|
---|
[8346] | 61 | private VRPPathRelinker(VRPPathRelinker original, Cloner cloner) : base(original, cloner) { }
|
---|
| 62 | public VRPPathRelinker()
|
---|
| 63 | : base() {
|
---|
[8894] | 64 | Parameters.Add(new ValueParameter<IntValue>("Iterations", "The number of iterations the operator should perform.", new IntValue(50)));
|
---|
| 65 | Parameters.Add(new LookupParameter<IVRPProblemInstance>("ProblemInstance", "The VRP problem instance"));
|
---|
[8346] | 66 | Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used for stochastic manipulation operators."));
|
---|
| 67 | Parameters.Add(new ValueParameter<IntValue>("SampleSize", "The number of moves that should be executed.", new IntValue(10)));
|
---|
| 68 | }
|
---|
| 69 |
|
---|
| 70 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 71 | return new VRPPathRelinker(this, cloner);
|
---|
| 72 | }
|
---|
| 73 |
|
---|
[8894] | 74 | public static ItemArray<IItem> Apply(PotvinEncoding initiator, PotvinEncoding guide, PercentValue n, int sampleSize, int iterations, IRandom rand, IVRPProblemInstance problemInstance) {
|
---|
| 75 | if (initiator == null || guide == null)
|
---|
| 76 | throw new ArgumentException("Cannot relink path because one of the provided solutions or both are null.");
|
---|
| 77 |
|
---|
| 78 | double sigma = 1.5;
|
---|
| 79 | double minPenalty = 0.001;
|
---|
| 80 | double maxPenalty = 1000000000;
|
---|
| 81 |
|
---|
| 82 | var originalOverloadPenalty = new DoubleValue();
|
---|
| 83 | if (problemInstance is IHomogenousCapacitatedProblemInstance)
|
---|
| 84 | originalOverloadPenalty.Value = (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value;
|
---|
| 85 | var originalTardinessPenalty = new DoubleValue();
|
---|
| 86 | if (problemInstance is ITimeWindowedProblemInstance)
|
---|
| 87 | originalTardinessPenalty.Value = (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value;
|
---|
| 88 |
|
---|
| 89 | PotvinEncoding current = MatchTours(initiator, guide, problemInstance);
|
---|
| 90 | double currentSimilarity = VRPSimilarityCalculator.CalculateSimilarity(current, guide);
|
---|
| 91 |
|
---|
| 92 | IList<PotvinEncoding> solutions = new List<PotvinEncoding>();
|
---|
| 93 | int i = 0;
|
---|
| 94 | while (i < iterations && !currentSimilarity.IsAlmost(1.0)) {
|
---|
| 95 | var currentEval = problemInstance.Evaluate(current);
|
---|
| 96 | currentSimilarity = VRPSimilarityCalculator.CalculateSimilarity(current, guide);
|
---|
| 97 |
|
---|
| 98 | if (currentSimilarity < 1.0) {
|
---|
| 99 | for (int sample = 0; sample < sampleSize; sample++) {
|
---|
| 100 | var next = current.Clone() as PotvinEncoding;
|
---|
| 101 |
|
---|
| 102 | int neighborhood = rand.Next(3);
|
---|
| 103 | switch (neighborhood) {
|
---|
| 104 | case 0: next = RouteBasedXOver(next, guide, rand,
|
---|
| 105 | problemInstance);
|
---|
| 106 | break;
|
---|
| 107 | case 1: next = SequenceBasedXOver(next, guide, rand,
|
---|
| 108 | problemInstance);
|
---|
| 109 | break;
|
---|
| 110 | case 2: GuidedRelocateMove(next, guide, rand);
|
---|
| 111 | break;
|
---|
| 112 | }
|
---|
| 113 |
|
---|
| 114 | next = MatchTours(next, guide, problemInstance);
|
---|
| 115 |
|
---|
| 116 | var nextEval = problemInstance.Evaluate(next);
|
---|
| 117 | if ((nextEval.Quality < currentEval.Quality)) {
|
---|
| 118 | current = next;
|
---|
| 119 | solutions.Add(current);
|
---|
| 120 | break;
|
---|
| 121 | }
|
---|
| 122 | }
|
---|
| 123 |
|
---|
| 124 | if (problemInstance is IHomogenousCapacitatedProblemInstance) {
|
---|
| 125 | if (((CVRPEvaluation)currentEval).Overload > 0) {
|
---|
| 126 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value =
|
---|
| 127 | Math.Min(maxPenalty,
|
---|
| 128 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value * sigma);
|
---|
| 129 | } else {
|
---|
| 130 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value =
|
---|
| 131 | Math.Max(minPenalty,
|
---|
| 132 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value * sigma);
|
---|
| 133 | }
|
---|
| 134 | }
|
---|
| 135 |
|
---|
| 136 |
|
---|
| 137 | if (problemInstance is ITimeWindowedProblemInstance) {
|
---|
| 138 | if (((CVRPTWEvaluation)currentEval).Tardiness > 0) {
|
---|
| 139 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value =
|
---|
| 140 | Math.Min(maxPenalty,
|
---|
| 141 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value * sigma);
|
---|
| 142 | } else {
|
---|
| 143 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value =
|
---|
| 144 | Math.Max(minPenalty,
|
---|
| 145 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value / sigma);
|
---|
| 146 | }
|
---|
| 147 | }
|
---|
| 148 |
|
---|
| 149 | i++;
|
---|
| 150 | }
|
---|
| 151 | }
|
---|
| 152 |
|
---|
| 153 | if (problemInstance is IHomogenousCapacitatedProblemInstance)
|
---|
| 154 | (problemInstance as IHomogenousCapacitatedProblemInstance).OverloadPenalty.Value = originalOverloadPenalty.Value;
|
---|
| 155 | if (problemInstance is ITimeWindowedProblemInstance)
|
---|
| 156 | (problemInstance as ITimeWindowedProblemInstance).TardinessPenalty.Value = originalTardinessPenalty.Value;
|
---|
| 157 |
|
---|
| 158 | return new ItemArray<IItem>(ChooseSelection(solutions, n));
|
---|
| 159 | }
|
---|
| 160 |
|
---|
| 161 | private static IList<IItem> ChooseSelection(IList<PotvinEncoding> solutions, PercentValue n) {
|
---|
| 162 | IList<IItem> selection = new List<IItem>();
|
---|
| 163 | if (solutions.Count > 0) {
|
---|
| 164 | int noSol = (int)(solutions.Count * n.Value);
|
---|
| 165 | if (noSol <= 0) noSol++;
|
---|
| 166 | double stepSize = (double)solutions.Count / (double)noSol;
|
---|
| 167 | for (int i = 0; i < noSol; i++)
|
---|
| 168 | selection.Add(solutions.ElementAt((int)((i + 1) * stepSize - stepSize * 0.5)));
|
---|
| 169 | }
|
---|
| 170 |
|
---|
| 171 | return selection;
|
---|
| 172 | }
|
---|
| 173 |
|
---|
| 174 | protected override ItemArray<IItem> Relink(ItemArray<IItem> parents, PercentValue n) {
|
---|
| 175 | if (parents.Length != 2)
|
---|
| 176 | throw new ArgumentException("The number of parents is not equal to 2.");
|
---|
| 177 |
|
---|
| 178 | if (!(parents[0] is PotvinEncoding))
|
---|
| 179 | parents[0] = PotvinEncoding.ConvertFrom(parents[0] as IVRPEncoding, ProblemInstanceParameter.ActualValue);
|
---|
| 180 | if (!(parents[1] is PotvinEncoding))
|
---|
| 181 | parents[1] = PotvinEncoding.ConvertFrom(parents[1] as IVRPEncoding, ProblemInstanceParameter.ActualValue);
|
---|
| 182 |
|
---|
| 183 | return Apply(parents[0] as PotvinEncoding, parents[1] as PotvinEncoding, n,
|
---|
| 184 | SampleSizeParameter.Value.Value, IterationsParameter.Value.Value, RandomParameter.ActualValue, ProblemInstanceParameter.ActualValue);
|
---|
| 185 | }
|
---|
| 186 |
|
---|
[8346] | 187 | private static int MatchingCities(Tour tour1, Tour tour2) {
|
---|
| 188 | return tour1.Stops.Intersect(tour2.Stops).Count();
|
---|
| 189 | }
|
---|
| 190 |
|
---|
| 191 | private static PotvinEncoding MatchTours(PotvinEncoding initiator, PotvinEncoding guide, IVRPProblemInstance problemInstance) {
|
---|
[8894] | 192 | var result = new PotvinEncoding(problemInstance);
|
---|
[8346] | 193 |
|
---|
[8894] | 194 | var used = new List<bool>();
|
---|
[8346] | 195 | for (int i = 0; i < initiator.Tours.Count; i++) {
|
---|
| 196 | used.Add(false);
|
---|
| 197 | }
|
---|
| 198 |
|
---|
| 199 | for (int i = 0; i < guide.Tours.Count; i++) {
|
---|
| 200 | int bestMatch = -1;
|
---|
| 201 | int bestTour = -1;
|
---|
| 202 |
|
---|
| 203 | for (int j = 0; j < initiator.Tours.Count; j++) {
|
---|
| 204 | if (!used[j]) {
|
---|
| 205 | int match = MatchingCities(guide.Tours[i], initiator.Tours[j]);
|
---|
| 206 | if (match > bestMatch) {
|
---|
| 207 | bestMatch = match;
|
---|
| 208 | bestTour = j;
|
---|
| 209 | }
|
---|
| 210 | }
|
---|
| 211 | }
|
---|
| 212 |
|
---|
| 213 | if (bestTour != -1) {
|
---|
| 214 | result.Tours.Add(initiator.Tours[bestTour]);
|
---|
| 215 | used[bestTour] = true;
|
---|
| 216 | }
|
---|
| 217 | }
|
---|
| 218 |
|
---|
| 219 | for (int i = 0; i < initiator.Tours.Count; i++) {
|
---|
| 220 | if (!used[i])
|
---|
| 221 | result.Tours.Add(initiator.Tours[i]);
|
---|
| 222 | }
|
---|
| 223 |
|
---|
| 224 | return result;
|
---|
| 225 | }
|
---|
| 226 |
|
---|
| 227 | #region moves
|
---|
| 228 | public static PotvinEncoding RouteBasedXOver(PotvinEncoding initiator, PotvinEncoding guide, IRandom random, IVRPProblemInstance problemInstance) {
|
---|
| 229 | return PotvinRouteBasedCrossover.Apply(random, initiator, guide, problemInstance, false);
|
---|
| 230 | }
|
---|
| 231 |
|
---|
| 232 | public static PotvinEncoding SequenceBasedXOver(PotvinEncoding initiator, PotvinEncoding guide, IRandom random, IVRPProblemInstance problemInstance) {
|
---|
| 233 | return PotvinSequenceBasedCrossover.Apply(random, initiator, guide, problemInstance, false);
|
---|
| 234 | }
|
---|
| 235 |
|
---|
| 236 | public static void GuidedRelocateMove(PotvinEncoding initiator, PotvinEncoding guide, IRandom random) {
|
---|
| 237 | List<int> cities = new List<int>();
|
---|
| 238 | foreach (Tour tour in initiator.Tours) {
|
---|
| 239 | foreach (int city in tour.Stops) {
|
---|
| 240 | Tour guideTour = guide.Tours.First(t => t.Stops.Contains(city));
|
---|
| 241 | if (guide.Tours.IndexOf(guideTour) != initiator.Tours.IndexOf(tour)) {
|
---|
| 242 | cities.Add(city);
|
---|
| 243 | }
|
---|
| 244 | }
|
---|
| 245 | }
|
---|
| 246 |
|
---|
| 247 | if (cities.Count == 0) {
|
---|
| 248 | RelocateMove(initiator, random);
|
---|
| 249 | } else {
|
---|
| 250 | int city = cities[random.Next(cities.Count)];
|
---|
| 251 | Tour tour = initiator.Tours.First(t => t.Stops.Contains(city));
|
---|
| 252 | tour.Stops.Remove(city);
|
---|
| 253 |
|
---|
| 254 | Tour guideTour = guide.Tours.First(t => t.Stops.Contains(city));
|
---|
| 255 | int guideTourIndex = guide.Tours.IndexOf(guideTour);
|
---|
| 256 |
|
---|
| 257 | if (guideTourIndex < initiator.Tours.Count) {
|
---|
| 258 | Tour tour2 = initiator.Tours[guideTourIndex];
|
---|
| 259 |
|
---|
| 260 | int guideIndex = guideTour.Stops.IndexOf(city);
|
---|
| 261 | if (guideIndex == 0) {
|
---|
| 262 | tour2.Stops.Insert(0, city);
|
---|
| 263 | } else {
|
---|
| 264 | int predecessor = guideTour.Stops[guideIndex - 1];
|
---|
| 265 | int initIndex = tour2.Stops.IndexOf(predecessor);
|
---|
| 266 | if (initIndex != -1) {
|
---|
| 267 | tour2.Stops.Insert(initIndex + 1, city);
|
---|
| 268 | } else {
|
---|
| 269 | if (guideIndex == guideTour.Stops.Count - 1) {
|
---|
| 270 | tour2.Stops.Insert(tour2.Stops.Count, city);
|
---|
| 271 | } else {
|
---|
| 272 | int sucessor = guideTour.Stops[guideIndex + 1];
|
---|
| 273 | initIndex = tour2.Stops.IndexOf(sucessor);
|
---|
| 274 | if (initIndex != -1) {
|
---|
| 275 | tour2.Stops.Insert(initIndex, city);
|
---|
| 276 | } else {
|
---|
| 277 | tour2.Stops.Insert(random.Next(tour2.Stops.Count + 1), city);
|
---|
| 278 | }
|
---|
| 279 | }
|
---|
| 280 | }
|
---|
| 281 | }
|
---|
| 282 | } else {
|
---|
| 283 | Tour tour2 = new Tour();
|
---|
| 284 | tour2.Stops.Add(city);
|
---|
| 285 | initiator.Tours.Add(tour2);
|
---|
| 286 | }
|
---|
| 287 |
|
---|
| 288 | if (tour.Stops.Count == 0)
|
---|
| 289 | initiator.Tours.Remove(tour);
|
---|
| 290 | }
|
---|
| 291 | }
|
---|
| 292 |
|
---|
| 293 | public static void RelocateMove(PotvinEncoding individual, IRandom random) {
|
---|
| 294 | int cities = individual.Cities;
|
---|
| 295 | int city = 1 + random.Next(cities);
|
---|
| 296 | Tour originalTour = individual.Tours.Find(t => t.Stops.Contains(city));
|
---|
| 297 | //consider creating new route
|
---|
| 298 | individual.Tours.Add(new Tour());
|
---|
| 299 |
|
---|
| 300 | int position = 1 + random.Next(cities + individual.Tours.Count - 1);
|
---|
| 301 | if (position >= city) {
|
---|
| 302 | position++;
|
---|
| 303 | }
|
---|
| 304 |
|
---|
| 305 | int originalPosition = originalTour.Stops.IndexOf(city);
|
---|
| 306 | originalTour.Stops.RemoveAt(originalPosition);
|
---|
| 307 |
|
---|
| 308 | Tour insertionTour;
|
---|
| 309 | int insertionPosition;
|
---|
| 310 | if (position <= cities) {
|
---|
| 311 | insertionTour = individual.Tours.Find(t => t.Stops.Contains(position));
|
---|
| 312 | insertionPosition = insertionTour.Stops.IndexOf(position) + 1;
|
---|
| 313 | } else {
|
---|
| 314 | insertionTour = individual.Tours[position - cities - 1];
|
---|
| 315 | insertionPosition = 0;
|
---|
| 316 | }
|
---|
| 317 |
|
---|
| 318 | insertionTour.Stops.Insert(insertionPosition, city);
|
---|
| 319 |
|
---|
| 320 | individual.Tours.RemoveAll(t => t.Stops.Count == 0);
|
---|
| 321 | }
|
---|
| 322 | #endregion
|
---|
| 323 | }
|
---|
[8894] | 324 | } |
---|