#region License Information
/* HeuristicLab
* Copyright (C) 2002-2011 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.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
using HeuristicLab.Problems.VehicleRouting.Encodings;
using HeuristicLab.Problems.VehicleRouting;
namespace HeuristicLab.Analysis.FitnessLandscape.VRP {
[Item("TwoOptStarManipulator", "Two opt star manipulation")]
[StorableClass]
public sealed class TwoOptStarManipulator : PotvinManipulator {
[StorableConstructor]
private TwoOptStarManipulator(bool deserializing) : base(deserializing) { }
private TwoOptStarManipulator(TwoOptStarManipulator original, Cloner cloner)
: base(original, cloner) {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new TwoOptStarManipulator(this, cloner);
}
public TwoOptStarManipulator()
: base() {
}
private static bool RouteFeasible(Tour tour, DoubleArray demand, DoubleValue capacity) {
double routeLoad = 0;
for (int i = 0; i < tour.Cities.Count; i++) {
routeLoad += demand[tour.Cities[i]];
}
return routeLoad <= capacity.Value;
}
public static void Apply(IRandom random, PotvinEncoding individual, DoubleArray demand, DoubleValue capacity, bool allowInfeasible) {
bool feasible;
//consider creating new tour
individual.Tours.Add(new Tour());
do {
feasible = true;
int route1Idx = random.Next(individual.Tours.Count);
int route2Idx = random.Next(individual.Tours.Count - 1);
if (route2Idx >= route1Idx)
route2Idx++;
Tour route1 = individual.Tours[route1Idx];
Tour route2 = individual.Tours[route2Idx];
int x1 = random.Next(route1.Cities.Count + 1);
int x2 = random.Next(route2.Cities.Count + 1);
if (!allowInfeasible) {
bool originalFeasible =
RouteFeasible(route1, demand, capacity) &&
RouteFeasible(route2, demand, capacity);
if (originalFeasible) {
double routeLoad = 0;
for (int i = 0; i < x1; i++)
routeLoad += demand[route1.Cities[i]];
for (int i = x2; i < route2.Cities.Count; i++)
routeLoad += demand[route2.Cities[i]];
if (routeLoad > capacity.Value) {
feasible = false;
} else {
routeLoad = 0;
for (int i = 0; i < x2; i++)
routeLoad += demand[route2.Cities[i]];
for (int i = x1; i < route1.Cities.Count; i++)
routeLoad += demand[route1.Cities[i]];
if (routeLoad > capacity.Value) {
feasible = false;
}
}
}
}
if (feasible) {
int count = route1.Cities.Count - x1;
List segmentX1 = new List();
if (count > 0) {
segmentX1 = route1.Cities.GetRange(x1, count);
route1.Cities.RemoveRange(x1, count);
}
count = route2.Cities.Count - x2;
List segmentX2 = new List();
if (count > 0) {
segmentX2 = route2.Cities.GetRange(x2, count);
route2.Cities.RemoveRange(x2, count);
}
route1.Cities.AddRange(segmentX2);
route2.Cities.AddRange(segmentX1);
}
} while (!feasible);
individual.Tours.RemoveAll(t => t.Cities.Count == 0);
}
protected override void Manipulate(IRandom random, PotvinEncoding individual) {
BoolValue useDistanceMatrix = UseDistanceMatrixParameter.ActualValue;
DoubleMatrix coordinates = CoordinatesParameter.ActualValue;
DistanceMatrix distMatrix = VRPUtilities.GetDistanceMatrix(coordinates, DistanceMatrixParameter, useDistanceMatrix);
DoubleArray demand = DemandParameter.ActualValue;
DoubleValue capacity = CapacityParameter.ActualValue;
bool allowInfeasible = AllowInfeasibleSolutions.Value.Value;
Apply(random, individual, demand, capacity, allowInfeasible);
}
}
}