[11191] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17226] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[11191] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System.Collections.Generic;
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| 23 | using System.Linq;
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[17525] | 24 | using HEAL.Attic;
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[11191] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Encodings.IntegerVectorEncoding;
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| 29 | using HeuristicLab.Parameters;
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| 30 |
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| 31 | namespace HeuristicLab.Problems.Orienteering {
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[12721] | 32 | /// <summary>
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| 33 | /// The initial solution for P-VNS is generated by means of a greedy algorithm that takes into
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| 34 | /// account all vertices vi that are located within the cost limit Tmax. These points are sorted
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| 35 | /// in descending order regarding the sum of their objective values. Afterwards, the algorithm
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| 36 | /// starts with a tour only including the starting and ending point and successively inserts the
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| 37 | /// points from this list at the first position in which they can feasibly be inserted.
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| 38 | /// (Schilde et. al. 2009)
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| 39 | /// </summary>
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| 40 | [Item("GreedyOrienteeringTourCreator", @"Implements the solution creation procedure described in Schilde M., Doerner K.F., Hartl R.F., Kiechle G. 2009. Metaheuristics for the bi-objective orienteering problem. Swarm Intelligence, Volume 3, Issue 3, pp 179-201.")]
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[16723] | 41 | [StorableType("FB68525D-DD53-4BE7-A6B4-EC54E6FD0E64")]
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[11321] | 42 | public sealed class GreedyOrienteeringTourCreator : IntegerVectorCreator, IOrienteeringSolutionCreator {
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[11191] | 43 | public override bool CanChangeName { get { return false; } }
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| 44 |
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[17525] | 45 | public ILookupParameter<IOrienteeringProblemData> OrienteeringProblemDataParameter {
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| 46 | get { return (ILookupParameter<IOrienteeringProblemData>)Parameters["OrienteeringProblemData"]; }
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[11191] | 47 | }
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| 48 |
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| 49 | [StorableConstructor]
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[16723] | 50 | private GreedyOrienteeringTourCreator(StorableConstructorFlag _) : base(_) { }
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[11307] | 51 | private GreedyOrienteeringTourCreator(GreedyOrienteeringTourCreator original, Cloner cloner)
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[11191] | 52 | : base(original, cloner) { }
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| 53 |
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| 54 | public GreedyOrienteeringTourCreator()
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| 55 | : base() {
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[17525] | 56 | Parameters.Add(new LookupParameter<IOrienteeringProblemData>("OrienteeringProblemData", "The main data that comprises the orienteering problem."));
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[11191] | 57 | }
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| 58 |
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| 59 | public override IDeepCloneable Clone(Cloner cloner) {
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| 60 | return new GreedyOrienteeringTourCreator(this, cloner);
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| 61 | }
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| 62 |
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[12721] | 63 | protected override IntegerVector Create(IRandom random, IntValue length, IntMatrix bounds) {
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[17525] | 64 | var data = OrienteeringProblemDataParameter.ActualValue;
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[11191] | 65 |
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| 66 | // Find all points within the maximum distance allowed (ellipse)
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[11228] | 67 | var feasiblePoints = (
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[17533] | 68 | from point in Enumerable.Range(0, data.Cities)
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| 69 | let travelCosts = data.GetDistance(data.StartingPoint, point)
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| 70 | + data.GetDistance(point, data.TerminalPoint) + data.PointVisitingCosts
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[17525] | 71 | let score = data.GetScore(point)
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| 72 | where travelCosts <= data.MaximumTravelCosts
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| 73 | where point != data.StartingPoint && point != data.TerminalPoint
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[11228] | 74 | orderby score descending
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| 75 | select point
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| 76 | ).ToList();
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[11191] | 77 |
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| 78 | // Add the starting and terminus point
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| 79 | var tour = new List<int> {
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[17525] | 80 | data.StartingPoint,
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| 81 | data.TerminalPoint
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[11191] | 82 | };
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[17533] | 83 | double tourLength = data.GetDistance(data.StartingPoint, data.TerminalPoint);
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[11191] | 84 |
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| 85 | // Add points in a greedy way
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| 86 | bool insertionPerformed = true;
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| 87 | while (insertionPerformed) {
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| 88 | insertionPerformed = false;
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| 89 |
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[11228] | 90 | for (int i = 0; i < feasiblePoints.Count; i++) {
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[11192] | 91 | for (int insertPosition = 1; insertPosition < tour.Count; insertPosition++) {
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[11191] | 92 | // Create the candidate tour
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[17525] | 93 | double detour = OrienteeringProblem.CalculateInsertionCosts(data, tour, insertPosition, feasiblePoints[i]);
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[11191] | 94 |
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| 95 | // If the insertion would be feasible, perform it
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[17525] | 96 | if (tourLength + detour <= data.MaximumTravelCosts) {
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[11228] | 97 | tour.Insert(insertPosition, feasiblePoints[i]);
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[12721] | 98 | tourLength += detour;
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[11228] | 99 | feasiblePoints.RemoveAt(i);
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[11191] | 100 | insertionPerformed = true;
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| 101 | break;
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| 102 | }
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| 103 | }
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| 104 | if (insertionPerformed) break;
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| 105 | }
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| 106 | }
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| 107 |
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| 108 | return new IntegerVector(tour.ToArray());
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| 109 | }
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| 110 | }
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| 111 | } |
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