public void ExecuteMetaheuristic(ref Solution bestSolution)
{
ShiftInter = 0;
IntraRouteInsertionOP = 0;
WRI_IntraOP = 0;
IntraSwap = 0;
Debug.WriteLine("\n\nNueva búsuqeda: ");
bool ls1 = true;
bool ls2 = true;
bool ls3 = true;
bool ls4 = true;
while (ls1 || ls2 || ls3 || ls4)
{
List <int> localInternal = Enumerable.Range(0, 3).ToList();
do
{
var variable = localInternal[_random.Next(0, localInternal.Count)];
switch (variable)
{
case 0:
ls1 = WRI_Intra(ref bestSolution);
localInternal.Remove(0);
break;
case 1:
ls3 = IntraRouteInsertion(ref bestSolution);
localInternal.Remove(1);
break;
case 2:
ls4 = Swap_Intra(ref bestSolution);
localInternal.Remove(2);
break;
}
}while (localInternal.Count > 0);
//ls4 = Swap_Intra(ref bestSolution);
//ls1 = WRI_Intra(ref bestSolution);
ls2 = Shift1_0Inter(ref bestSolution);
//ls3 = IntraRouteInsertion(ref bestSolution);
//ls1 = false;
//ls2 = false;
//ls3 = false;
//ls4 = false;
}
DARPAlgorithms.UpdateConstraintsSolution(ref bestSolution, _problem);
CheckFesiability(ref bestSolution);
}
public void ExecuteMetaheuristic(ref Solution bestSolution)
{
int bestIteration = 0;
Solution partialSolution = GenericCopier <Solution> .DeepCopy(bestSolution);
int control_Diversify = 0;
SummaryDetails summary = new SummaryDetails();
Splash.SplashGlobalData.SetSplashData(Constants.SPLASH_BEST_SOLUTION, bestSolution.Fitness);
double pertCost = 0, vnsCost = 0;
int iterations = 0;
//Guardar coste inicial.
summary.ILSSummary.InitCost = bestSolution.Parc_Cost;
for (int i = 0; iterations < _ilsSettings.MaxILSIterations && i < _ilsSettings.MaxILSNoImprovement; i++)
{
//Control de intensificación-diversificación.
if (i % _DIVERSIFY_MAX == 0)
{
_diversify--;
}
_diversify = _diversify < 3 ? 3 : _diversify;
if (i % 100 == 0)
{
_diversify = _DIVERSIFY_MAX;
}
//Guardar valores estadísticos.
var initCost = partialSolution.Parc_Cost;
var initFitness = partialSolution.Fitness;
Splash.SplashGlobalData.SetSplashData(Constants.SPLASH_ILS_TOTAL_ITERATIONS, iterations + 1);
//Apply VNS local search.
_vns.ExecuteMetaheuristic(ref partialSolution);
//Validator.ValidateSolution(ref partialSolution, _problem);
bool feasible = partialSolution.Feasible;
//Guardar valores estadísticos.
vnsCost = Costs.ParcialCostSolution(partialSolution, _problem); // partialSolution.Parc_Cost;
var vnsFitness = partialSolution.Fitness;
if (vnsFitness < initFitness)
{
summary.ILSSummary.TotalImpPrevious++; //Incrementar el número de veces que se mejora la solución previa (perturbada).
}
if (bestSolution.Feasible)
{
if (partialSolution.Feasible && (Math.Round(partialSolution.Fitness, 2) < Math.Round(bestSolution.Fitness, 2)))
{
bestSolution = GenericCopier <Solution> .DeepCopy(partialSolution);
//Actualizar la mejor solución con los nuevos parámetros.
control_Diversify = 0;
bestIteration = i;
i = 0;
_diversify = _DIVERSIFY_MAX;
summary.ILSSummary.BestIteration = iterations + 1; //Guardar mejor iteración encontrada.
summary.ILSSummary.TotalImpBest++; //Incrementar el número de veces que se mejora la mejor solución.
Singleton.Instance.UpdatePenaltyTerms(partialSolution);
Splash.SplashGlobalData.SetSplashData(Constants.SPLASH_ILS_IMPROVEMENTS, summary.ILSSummary.TotalImpBest);
Splash.SplashGlobalData.SetSplashData(Constants.SPLASH_BEST_SOLUTION, Math.Round(bestSolution.Fitness, 2));
}
else
{
control_Diversify++;
}
}
else
{
if (partialSolution.Feasible || Math.Round(partialSolution.Fitness, 2) < Math.Round(bestSolution.Fitness, 2))
{
if (partialSolution.Feasible && summary.ILSSummary.FirstItFeasible == null)
{
summary.ILSSummary.FirstItFeasible = iterations + 1; //Guardar primera iteración factible.
}
bestSolution = GenericCopier <Solution> .DeepCopy(partialSolution);
control_Diversify = 0;
i = 0;
_diversify = _DIVERSIFY_MAX;
summary.ILSSummary.BestIteration = iterations + 1; //Guardar mejor iteración encontrada.
summary.ILSSummary.TotalImpBest++; //Incrementar el número de veces que se mejora la mejor solución.
Singleton.Instance.UpdatePenaltyTerms(partialSolution);
//bestSolution.Fitness = Costs.EvaluationSolution(bestSolution, _problem);
Splash.SplashGlobalData.SetSplashData(Constants.SPLASH_ILS_IMPROVEMENTS, summary.ILSSummary.TotalImpBest);
Splash.SplashGlobalData.SetSplashData(Constants.SPLASH_BEST_SOLUTION, bestSolution.Fitness);
}
else
{
control_Diversify++;
}
}
if (control_Diversify > _diversify)
{
partialSolution = GenericCopier <Solution> .DeepCopy(bestSolution);
control_Diversify = 0;
}
var perturbation = _perturbations.ExecutePerturbation(ref partialSolution);
//Guardar valores estadísticos.
pertCost = partialSolution.Parc_Cost;
var pertFitness = partialSolution.Fitness;
//Añadir resumen de la iteración.
summary.VNSOperators.Add(new VNSOperators(iterations, _vns.ShiftInter, 0, _vns.WRI_IntraOP, _vns.IntraRouteInsertionOP, _vns.IntraSwap));
summary.ILSEvolution.Add(new ILSEvolution(iterations, Math.Round(initCost, 2), Math.Round(initFitness, 2), Math.Round(vnsCost, 2), Math.Round(vnsFitness, 2), perturbation, Math.Round(pertFitness, 2), Math.Round(pertCost, 2), feasible));
Splash.SplashGlobalData.SetSplashData(Constants.SPLASH_PROGRESS, Convert.ToDouble(Splash.SplashGlobalData.GetSplashData <double>(Constants.SPLASH_PROGRESS)) + _coef_Updater_Splash);
//Guardar cambios de cada operador.
iterations++;
}
//Recalcular con el proceso de 8 pasos y actualizar el coste.
foreach (var vehicle in bestSolution.Vehicles)
{
DARPAlgorithms.EightStepsEvaluationProcedure(ref bestSolution, vehicle, _problem);
}
bestSolution.TotalDuration = bestSolution.Vehicles.Sum(t => t.VehicleDuration);
bestSolution.TimeWindowViolation = bestSolution.Vehicles.Sum(t => t.TotalTimeWindowViolation);
bestSolution.RideTimeViolation = bestSolution.Vehicles.Sum(r => r.TotalRideTimeViolation);
bestSolution.DurationViolation = bestSolution.Vehicles.Sum(d => d.TotalDurationViolation);
bestSolution.LoadViolation = bestSolution.Vehicles.Sum(l => l.TotalLoadViolation);
bestSolution.TotalWaitingTime = bestSolution.Vehicles.Sum(t => t.TotalWaitingTime);
DARPAlgorithms.UpdateConstraintsSolution(ref bestSolution, _problem);
bestSolution.Fitness = Costs.EvaluationSolution(bestSolution, _problem);
bestSolution.Parc_Cost = Costs.ParcialCostSolution(bestSolution, _problem);
summary.ILSSummary.TotalIterations = iterations + 1;
summary.ILSSummary.FinalCost = bestSolution.Parc_Cost;
bestSolution.SummaryDetails = summary;
}
private void ShiftPerturbation(ref Solution solution)
{
int v1 = -1, v2 = -1;
//marcar todos los vehículos como no perturbados.
solution.Vehicles = solution.Vehicles.Select(t => { t.Perturbed = true; return(t); }).ToList();
//Recorrer todos los vehículos
//for (int v = 0; v < solution.Vehicles.Count; v++)
//{
//Seleccionar el primer vehículo.
do
{
v1 = _random.Next(0, PerturbationMechanism.Keys.Count);
}while (solution.Vehicles[v1].Requests.Count <= 4);
if (PerturbationMechanism[v1].Count == 0)
{
ResetPerturbationMechanism(false, v1);
}
do
{
v2 = PerturbationMechanism[v1][_random.Next(0, PerturbationMechanism[v1].Count)];
}while (v1 == v2);
//Seleccionar aleatoriamente un cliente de v1.
List <Request> nodes = new List <Request>();
var node = solution.Vehicles[v1].Requests[_random.Next(1, solution.Vehicles[v1].Requests.Count - 1)];
nodes = solution.Vehicles[v1].Requests.FindAll(t => t.PairID.Equals(node.PairID)); //Extraemos inicio y destino.
//Siguiente paso. Insertarlos en su mejor posición posible.
var origin = nodes.Find(t => t.Origin == true);
int posOrigin = solution.Vehicles[v1].Requests.IndexOf(origin);
var destination = nodes.Find(t => t.Origin == false);
int posDestination = solution.Vehicles[v1].Requests.IndexOf(destination);
if (solution.Vehicles[v2].Requests.Count == 2)
{
solution.Vehicles[v2].Requests.Insert(1, destination);
solution.Vehicles[v2].Requests.Insert(1, origin);
}
else
{
solution.Vehicles[v1].Requests.RemoveAll(t => t.PairID.Equals(nodes.First().PairID)); //Los eliminamos de su ruta inicio.
//Determinamos nodo crítico (será el primero a insertar).
Request critical;
Request notCritical;
if (origin.IsCritical)
{
critical = origin;
notCritical = destination;
}
else
{
critical = destination;
notCritical = origin;
}
//Seleccionar posiciones en el vehículo v2
Dictionary <int, int> bestPair = new Dictionary <int, int>();
int bestPosSource = -1;
double min = double.MaxValue;
for (int pos = 1; pos < solution.Vehicles[v2].Requests.Count; pos++)
{
double twDist = 0.0;
if (pos == 1)
{
if ((critical.TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[pos].ID_Unique]) > solution.Vehicles[v2].Requests[pos].TimeWindow.UpperBound)
{
continue;
}
twDist = CalculateDistance(critical, v2, pos, solution);
}
else if (pos == solution.Vehicles[v2].Requests.Count - 1)
{
if ((solution.Vehicles[v2].Requests[pos].TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[pos].ID_Unique]) > critical.TimeWindow.UpperBound)
{
continue;
}
twDist = CalculateDistance(critical, v2, pos - 1, solution);
}
else
{
if (((solution.Vehicles[v2].Requests[pos - 1].TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[pos - 1].ID_Unique]) > critical.TimeWindow.UpperBound) ||
(critical.TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[pos].ID_Unique]) > solution.Vehicles[v2].Requests[pos].TimeWindow.UpperBound)
{
continue;
}
twDist = (CalculateDistance(critical, v2, pos, solution) + CalculateDistance(critical, v2, pos - 1, solution)) / 2.0;
}
if (twDist < min)
{
bestPosSource = pos;
min = twDist;
}
}
if (bestPosSource == -1) //Insertar en pos = 1;
{
bestPosSource = 1;
}
solution.Vehicles[v2].Requests.Insert(bestPosSource, critical);
int notCriticalPos = -1;
min = double.MaxValue;
//Insertar no crítico.
if (critical.Origin)
{
//De pos en adelante.
for (int init = bestPosSource + 1; init < solution.Vehicles[v2].Requests.Count; init++)
{
if ((solution.Vehicles[v2].Requests[init - 1].TimeWindow.LowerBound + _problem.Distances[solution.Vehicles[v2].Requests[init - 1].ID_Unique][notCritical.ID_Unique]) > notCritical.TimeWindow.UpperBound)
{
continue;
}
double twDist = 0.0;
if (init == solution.Vehicles[v2].Requests.Count)
{
if ((solution.Vehicles[v2].Requests[init - 1].TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[init - 1].ID_Unique]) > critical.TimeWindow.UpperBound)
{
continue;
}
twDist = CalculateDistance(critical, v2, init - 1, solution);
}
else
{
if (((solution.Vehicles[v2].Requests[init - 1].TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[init - 1].ID_Unique]) > critical.TimeWindow.UpperBound) ||
(critical.TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[init].ID_Unique]) > solution.Vehicles[v2].Requests[init].TimeWindow.UpperBound)
{
continue;
}
twDist = (CalculateDistance(critical, v2, init, solution) + CalculateDistance(critical, v2, init - 1, solution)) / 2.0;
}
//var diff = Problem.EuclideanDistance(notCritical.TimeWindow.LowerBound, notCritical.TimeWindow.UpperBound, solution.Vehicles[v2].Requests[init - 1].TimeWindow.LowerBound, solution.Vehicles[v2].Requests[init - 1].TimeWindow.UpperBound);
if (twDist < min)
{
notCriticalPos = init;
min = twDist;
}
}
}
else
{
//De pos hacia atrás.
for (int init = bestPosSource; init > 0; init--)
{
if ((notCritical.TimeWindow.LowerBound + _problem.Distances[notCritical.ID_Unique][solution.Vehicles[v2].Requests[init].ID_Unique]) > solution.Vehicles[v2].Requests[init].TimeWindow.UpperBound)
{
continue;
}
//var diff = Problem.EuclideanDistance(notCritical.TimeWindow.LowerBound, notCritical.TimeWindow.UpperBound, solution.Vehicles[v2].Requests[init].TimeWindow.LowerBound, solution.Vehicles[v2].Requests[init].TimeWindow.UpperBound);
double twDist = 0.0;
if (init == 1)
{
if ((critical.TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[init].ID_Unique]) > solution.Vehicles[v2].Requests[init].TimeWindow.UpperBound)
{
continue;
}
twDist = CalculateDistance(critical, v2, init, solution);
}
else
{
if (((solution.Vehicles[v2].Requests[init - 1].TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[init - 1].ID_Unique]) > critical.TimeWindow.UpperBound) ||
(critical.TimeWindow.LowerBound + _problem.Distances[critical.ID_Unique][solution.Vehicles[v2].Requests[init].ID_Unique]) > solution.Vehicles[v2].Requests[init].TimeWindow.UpperBound)
{
continue;
}
twDist = CalculateDistance(critical, v2, init, solution);
}
if (twDist < min)
{
notCriticalPos = init;
min = twDist;
}
}
}
solution.Vehicles[v2].Requests.Insert(notCriticalPos, notCritical);
}
// }
//Recalcular con el proceso de 8 pasos y actualizar el coste.
foreach (var vehicle in solution.Vehicles)
{
DARPAlgorithms.EightStepsEvaluationProcedure(ref solution, vehicle, _problem);
}
solution.TotalDuration = solution.Vehicles.Sum(t => t.VehicleDuration);
solution.TimeWindowViolation = solution.Vehicles.Sum(t => t.TotalTimeWindowViolation);
solution.RideTimeViolation = solution.Vehicles.Sum(r => r.TotalRideTimeViolation);
solution.DurationViolation = solution.Vehicles.Sum(d => d.TotalDurationViolation);
solution.LoadViolation = solution.Vehicles.Sum(l => l.TotalLoadViolation);
solution.TotalWaitingTime = solution.Vehicles.Sum(t => t.TotalWaitingTime);
DARPAlgorithms.UpdateConstraintsSolution(ref solution, _problem);
solution.Fitness = Costs.EvaluationSolution(solution, _problem);
try
{
Validator.Positions(ref solution, _problem);
Validation.Validator.ValidateSolution(ref solution, _problem);
}
catch (Exception ex)
{
Console.WriteLine();
}
}
private void SpecialPerturbation(ref Solution solution)
{
solution.Vehicles = solution.Vehicles.Select(t => { t.Perturbed = true; return(t); }).ToList();
//1. Sacar un vehículo aleatorio.
var vehicle = solution.Vehicles[_random.Next(0, solution.Vehicles.Count - 1)];
List <int> numbers = new List <int>();
//2. Sacar un cliente crítico.
var client = vehicle.Requests[_random.Next(1, vehicle.Requests.Count - 1)];
List <Tuple <Request, Tuple <int, int> > > data = new List <Tuple <Request, Tuple <int, int> > >();
if (!client.Origin)
{
client = vehicle.Requests.Find(t => t.Origin && t.PairID == client.PairID);
}
var vehicle_pos = solution.Vehicles.IndexOf(vehicle);
var client_pos = vehicle.Requests.IndexOf(client);
numbers.Add(client.PairID);
data.Add(new Tuple <Request, Tuple <int, int> >(client, new Tuple <int, int>(vehicle_pos, client_pos)));
//3. Sacar sus N vecinos críticos.
var neighbours = _problem.CloseTWMatrix[client.ID_Unique];
List <Request> vecinos = new List <Request>();
for (int i = 0; vecinos.Count < Convert.ToInt32(Math.Sqrt(_problem.NumRequests)) && i < neighbours.Count; i++)
{
if (_problem.Requests.Find(t => t.ID_Unique == neighbours[i]).Origin)
{
vecinos.Add(_problem.Requests.Find(t => t.ID_Unique == neighbours[i]));
numbers.Add(vecinos[vecinos.Count - 1].PairID);
}
}
//4. Sacar vehículo y posición de cada uno
foreach (var v in vecinos)
{
for (int i = 0; i < solution.Vehicles.Count; i++)
{
if (solution.Vehicles[i].Requests.Find(t => t.ID_Unique == v.ID_Unique) != null)
{
data.Add(new Tuple <Request, Tuple <int, int> >(v, new Tuple <int, int>(i, solution.Vehicles[i].Requests.IndexOf(solution.Vehicles[i].Requests.Find(t => t.ID_Unique == v.ID_Unique)))));
}
}
}
List <Tuple <Request, Tuple <int, int> > > movements = new List <Tuple <Request, Tuple <int, int> > >();
foreach (var element in data)
{
var request = element.Item1;
int selected = -1;
if (numbers.Count == 1 && numbers.First() == element.Item1.PairID)
{
break;
}
//5. Mezclarlos.
do
{
selected = numbers[_random.Next(0, numbers.Count)];
}while (selected == request.ID_Unique);
numbers.Remove(selected); //eliminarlo.
var posInterchange = data.Find(t => t.Item1.ID_Unique == selected);
//solution.Vehicles[element.Item2.Item1].Requests[element.Item2.Item2] = posInterchange.Item1;
//solution.Vehicles[posInterchange.Item2.Item1].Requests[posInterchange.Item2.Item2] = element.Item1;
//los destinos.
var destinoElement = solution.Vehicles[element.Item2.Item1].Requests.Find(t => t.PairID == element.Item1.PairID && t.Origin == false);
var destinoInterchange = solution.Vehicles[posInterchange.Item2.Item1].Requests.Find(t => t.PairID == posInterchange.Item1.PairID && t.Origin == false);
//solution.Vehicles[element.Item2.Item1].Requests[solution.Vehicles[element.Item2.Item1].Requests.IndexOf(destinoElement)] = destinoInterchange;
//solution.Vehicles[posInterchange.Item2.Item1].Requests[solution.Vehicles[posInterchange.Item2.Item1].Requests.IndexOf(destinoInterchange)] = destinoElement;
movements.Add(new Tuple <Request, Tuple <int, int> >(element.Item1, new Tuple <int, int>(posInterchange.Item2.Item1, posInterchange.Item2.Item2)));
movements.Add(new Tuple <Request, Tuple <int, int> >(destinoElement, new Tuple <int, int>(posInterchange.Item2.Item1, solution.Vehicles[posInterchange.Item2.Item1].Requests.IndexOf(destinoInterchange))));
}
foreach (var entry in movements)
{
solution.Vehicles[entry.Item2.Item1].Requests[entry.Item2.Item2] = entry.Item1;
}
//Recalcular con el proceso de 8 pasos y actualizar el coste.
foreach (var veh in solution.Vehicles)
{
DARPAlgorithms.EightStepsEvaluationProcedure(ref solution, veh, _problem);
}
solution.TotalDuration = solution.Vehicles.Sum(t => t.VehicleDuration);
solution.TimeWindowViolation = solution.Vehicles.Sum(t => t.TotalTimeWindowViolation);
solution.RideTimeViolation = solution.Vehicles.Sum(r => r.TotalRideTimeViolation);
solution.DurationViolation = solution.Vehicles.Sum(d => d.TotalDurationViolation);
solution.LoadViolation = solution.Vehicles.Sum(l => l.TotalLoadViolation);
solution.TotalWaitingTime = solution.Vehicles.Sum(t => t.TotalWaitingTime);
DARPAlgorithms.UpdateConstraintsSolution(ref solution, _problem);
solution.Fitness = Costs.EvaluationSolution(solution, _problem);
}
