public void TestVRPInstances()
{
var vrp = new VehicleRoutingProblem();
var providers = ProblemInstanceManager.GetProviders(vrp);
var failedInstances = new StringBuilder();
Assert.IsTrue(providers.Any(), "No providers could be found.");
foreach (var provider in providers)
{
IEnumerable <IDataDescriptor> instances = ((dynamic)provider).GetDataDescriptors();
Assert.IsTrue(instances.Any(), string.Format("No instances could be found in {0}.", provider.Name));
foreach (var instance in instances)
{
try {
// throws InvalidOperationException if zero or more than one interpreter is found
((dynamic)vrp).Load(((dynamic)provider).LoadData(instance));
} catch (Exception exc) {
failedInstances.AppendLine(instance.Name + ": " + exc.Message);
}
}
}
Assert.IsTrue(failedInstances.Length == 0, "Following instances failed: " + Environment.NewLine + failedInstances);
}
private ScatterSearch CreateScatterSearchVRPSample() {
#region Problem Configuration
var provider = new SolomonInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name == "C101");
VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
vrpProblem.Load(provider.LoadData(instance));
#endregion
#region Algorithm Configuration
ScatterSearch ss = new ScatterSearch();
ss.Engine = new SequentialEngine.SequentialEngine();
ss.Name = "Scatter Search - VRP";
ss.Description = "A scatter search algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
ss.Problem = vrpProblem;
var improver = ss.Problem.Operators.OfType<VRPIntraRouteImprovementOperator>().First();
improver.ImprovementAttemptsParameter.Value.Value = 15;
improver.SampleSizeParameter.Value.Value = 10;
ss.Improver = improver;
var pathRelinker = ss.Problem.Operators.OfType<VRPPathRelinker>().First();
pathRelinker.IterationsParameter.Value.Value = 25;
ss.PathRelinker = pathRelinker;
var similarityCalculator = ss.SimilarityCalculatorParameter.ValidValues.OfType<VRPSimilarityCalculator>().First();
ss.SimilarityCalculator = similarityCalculator;
ss.MaximumIterations.Value = 2;
ss.PopulationSize.Value = 20;
ss.ReferenceSetSize.Value = 10;
ss.Seed.Value = 0;
return ss;
#endregion
}
static async Task Main(string[] args)
{
//IrisDataCluster classifier = new IrisDataCluster(3);
//classifier.RunAsync();
//TravelingSalespersonProblem tsp = new TravelingSalespersonProblem(50);
//tsp.Run();
//TravelingSalespersonProblem tsp = new TravelingSalespersonProblem("./data/cities.data");
//tsp.Run();
//for (int i = 0; i < 3; i++)
//{
// //tsp.RunRestart();
// tsp.RunTwoOpt();
// tsp.RunTwoOpt();
// tsp.RunTwoOpt();
//}
//TravellingSalespersonProblem tsp = new TravellingSalespersonProblem("./data/cities.data");
//tsp.Run();
//BranchAndBound bnb = new BranchAndBound(tsp.GetCities());
//await bnb.RunAsync();
//BranchAndBound bnb = new BranchAndBound("./data/cities.data");
//await bnb.RunAsync();
VehicleRoutingProblem vrp = new VehicleRoutingProblem();
vrp.Run();
}
private void SetProblemInstance(VehicleRoutingProblem problem)
{
//var pi = problem.ProblemInstance.Clone() as HeuristicLab.Problems.VehicleRouting.ProblemInstances.MDCVRPTWProblemInstance;
HeuristicLab.Problems.VehicleRouting.Datalytics.ProblemInstances.MDCVRPTWProblemInstance pi = new HeuristicLab.Problems.VehicleRouting.Datalytics.ProblemInstances.MDCVRPTWProblemInstance();
pi.Cities.Value = _tasks.Count;
pi.Depots.Value = _drivers.Count;
pi.Vehicles.Value = _drivers.Count; // only allow 1 vehicle per depot
pi.DistanceFactor.Value = 1.0;
pi.FleetUsageFactor.Value = 0.0;
pi.UseDistanceMatrix.Value = true;
pi.Capacity = new DoubleArray(GetCapacity());
pi.Coordinates = new DoubleMatrix(GetCoordinates());
//_coordinates = pi.Coordinates;
//string patientList = string.Join(",", _stopIds);
//string staffList = string.Join(",", _driverIds);
UpdateRun("Calculating Distance Matrix", StrategyState.Running);
//Routing.Routing routing = new Routing.Routing();
//_routeLegs = routing.GetRouteLegs(patientList, staffList, "Distance");
pi.DistanceMatrix = CreateDistanceMatrix();
pi.TimeMatrix = CreateTimeMatrix();
pi.QualityMatrix = CreateQualityMatrix();
// cost-based functionality
pi.CostOptimization.Value = _costOptimization;
pi.DistanceToCostRatio.Value = _distanceCostRatio;
pi.PerStopCost = CreatePerVisitCostArray();
pi.PerMileCost = CreatePerMileCostArray();
pi.AvgLegLength.Value = _avgLegLength;
pi.AvgLegPenality.Value = _avgLegPenalty;
pi.MaxLegLength.Value = _maxLegLength;
pi.MaxLegPenality.Value = _maxLegPenalty;
UpdateRun("Distance Matrix Loaded", StrategyState.Running);
pi.Demand = new DoubleArray(GetDemand(_tasks.Count));
double dueTime = 1.7976931348623157E308; // Max double, we need this for some reason, 0 didn't work
pi.DueTime = new DoubleArray(GetDueTime(_tasks.Count + _drivers.Count, dueTime));
pi.ServiceTime = new DoubleArray(GetServiceTime(_tasks.Count));
pi.ReadyTime = new DoubleArray(GetReadyTime(_tasks.Count + _drivers.Count));
pi.VehicleDepotAssignment = new IntArray(SetVehicleDepotAssignment(_drivers.Count));
// set the vehicle assigment array
_vehicleAssignment = pi.VehicleDepotAssignment;
problem.SetProblemInstance(pi);
UpdateRun("Problem Instance Set", StrategyState.Running);
}
private GeneticAlgorithm CreateGaVrpSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
SolomonFormatInstanceProvider instanceProvider = new SolomonInstanceProvider();
CVRPTWData data = instanceProvider.Import(@"Test Resources\C101.txt", @"Test Resources\C101.opt.txt") as CVRPTWData;
vrpProblem.Load(data);
vrpProblem.Name = "C101 VRP (imported from Solomon)";
vrpProblem.Description = "Represents a Vehicle Routing Problem.";
CVRPTWProblemInstance instance = vrpProblem.ProblemInstance as CVRPTWProblemInstance;
instance.DistanceFactor.Value = 1;
instance.FleetUsageFactor.Value = 100;
instance.OverloadPenalty.Value = 100;
instance.TardinessPenalty.Value = 100;
instance.TimeFactor.Value = 0;
vrpProblem.MaximizationParameter.Value.Value = false;
instance.UseDistanceMatrix.Value = true;
instance.Vehicles.Value = 25;
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Algorithm - VRP";
ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
ga.Problem = vrpProblem;
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
ga, 100, 1, 1000, 0.05, 3);
var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
foreach (var op in xOver.Operators)
{
xOver.Operators.SetItemCheckedState(op, false);
}
xOver.Operators.SetItemCheckedState(xOver.Operators
.OfType <PotvinRouteBasedCrossover>()
.Single(), true);
xOver.Operators.SetItemCheckedState(xOver.Operators
.OfType <PotvinSequenceBasedCrossover>()
.Single(), true);
var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
foreach (var op in manipulator.Operators)
{
manipulator.Operators.SetItemCheckedState(op, false);
}
manipulator.Operators.SetItemCheckedState(manipulator.Operators
.OfType <PotvinOneLevelExchangeMainpulator>()
.Single(), true);
manipulator.Operators.SetItemCheckedState(manipulator.Operators
.OfType <PotvinTwoLevelExchangeManipulator>()
.Single(), true);
#endregion
return(ga);
}
private GeneticAlgorithm CreateGaVrpSample() {
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
SolomonFormatInstanceProvider instanceProvider = new SolomonInstanceProvider();
CVRPTWData data = instanceProvider.Import(@"Test Resources\C101.txt", @"Test Resources\C101.opt.txt") as CVRPTWData;
vrpProblem.Load(data);
vrpProblem.Name = "C101 VRP (imported from Solomon)";
vrpProblem.Description = "Represents a Vehicle Routing Problem.";
CVRPTWProblemInstance instance = vrpProblem.ProblemInstance as CVRPTWProblemInstance;
instance.DistanceFactor.Value = 1;
instance.FleetUsageFactor.Value = 100;
instance.OverloadPenalty.Value = 100;
instance.TardinessPenalty.Value = 100;
instance.TimeFactor.Value = 0;
vrpProblem.MaximizationParameter.Value.Value = false;
instance.UseDistanceMatrix.Value = true;
instance.Vehicles.Value = 25;
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Algorithm - VRP";
ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
ga.Problem = vrpProblem;
SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
ga, 100, 1, 1000, 0.05, 3);
var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
foreach (var op in xOver.Operators) {
xOver.Operators.SetItemCheckedState(op, false);
}
xOver.Operators.SetItemCheckedState(xOver.Operators
.OfType<PotvinRouteBasedCrossover>()
.Single(), true);
xOver.Operators.SetItemCheckedState(xOver.Operators
.OfType<PotvinSequenceBasedCrossover>()
.Single(), true);
var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
foreach (var op in manipulator.Operators) {
manipulator.Operators.SetItemCheckedState(op, false);
}
manipulator.Operators.SetItemCheckedState(manipulator.Operators
.OfType<PotvinOneLevelExchangeMainpulator>()
.Single(), true);
manipulator.Operators.SetItemCheckedState(manipulator.Operators
.OfType<PotvinTwoLevelExchangeManipulator>()
.Single(), true);
#endregion
return ga;
}
private TabuSearch CreateTabuSearchVrpSample()
{
TabuSearch ts = new TabuSearch();
#region Problem Configuration
var provider = new AugeratInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name == "A-n62-k8").Single();
VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
vrpProblem.Load(provider.LoadData(instance));
#endregion
#region Algorithm Configuration
ts.Name = "Tabu Search - VRP";
ts.Description = "A tabu search algorithm that solves the \"A-n62-k8\" VRP (imported from Augerat)";
ts.Problem = vrpProblem;
ts.MaximumIterations.Value = 200;
// move generator has to be set first
var moveGenerator = ts.MoveGeneratorParameter.ValidValues
.OfType <PotvinCustomerRelocationExhaustiveMoveGenerator>()
.Single();
ts.MoveGenerator = moveGenerator;
var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
.OfType <PotvinCustomerRelocationMoveEvaluator>()
.Single();
ts.MoveEvaluator = moveEvaluator;
var moveMaker = ts.MoveMakerParameter.ValidValues
.OfType <PotvinCustomerRelocationMoveMaker>()
.Single();
ts.MoveMaker = moveMaker;
ts.SampleSize.Value = 1000;
ts.Seed.Value = 0;
ts.SetSeedRandomly.Value = true;
var tabuChecker = ts.TabuCheckerParameter.ValidValues
.OfType <PotvinCustomerRelocationMoveTabuCriterion>()
.Single();
tabuChecker.UseAspirationCriterion.Value = false;
ts.TabuChecker = tabuChecker;
var tabuMaker = ts.TabuMakerParameter.ValidValues
.OfType <PotvinCustomerRelocationMoveTabuMaker>()
.Single();
ts.TabuMaker = tabuMaker;
ts.TabuTenure.Value = 6;
#endregion
ts.Engine = new ParallelEngine.ParallelEngine();
return(ts);
}
private TabuSearch CreateTabuSearchVrpSample() {
TabuSearch ts = new TabuSearch();
#region Problem Configuration
var provider = new AugeratInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name == "A-n62-k8").Single();
VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
vrpProblem.Load(provider.LoadData(instance));
#endregion
#region Algorithm Configuration
ts.Name = "Tabu Search - VRP";
ts.Description = "A tabu search algorithm that solves the \"A-n62-k8\" VRP (imported from Augerat)";
ts.Problem = vrpProblem;
ts.MaximumIterations.Value = 200;
// move generator has to be set first
var moveGenerator = ts.MoveGeneratorParameter.ValidValues
.OfType<PotvinCustomerRelocationExhaustiveMoveGenerator>()
.Single();
ts.MoveGenerator = moveGenerator;
var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
.OfType<PotvinCustomerRelocationMoveEvaluator>()
.Single();
ts.MoveEvaluator = moveEvaluator;
var moveMaker = ts.MoveMakerParameter.ValidValues
.OfType<PotvinCustomerRelocationMoveMaker>()
.Single();
ts.MoveMaker = moveMaker;
ts.SampleSize.Value = 1000;
ts.Seed.Value = 0;
ts.SetSeedRandomly.Value = true;
var tabuChecker = ts.TabuCheckerParameter.ValidValues
.OfType<PotvinCustomerRelocationMoveTabuCriterion>()
.Single();
tabuChecker.UseAspirationCriterion.Value = false;
ts.TabuChecker = tabuChecker;
var tabuMaker = ts.TabuMakerParameter.ValidValues
.OfType<PotvinCustomerRelocationMoveTabuMaker>()
.Single();
ts.TabuMaker = tabuMaker;
ts.TabuTenure.Value = 6;
#endregion
ts.Engine = new ParallelEngine.ParallelEngine();
return ts;
}
private void LoadingCompleted(IStorableContent content)
{
//if (error != null) throw error;
UpdateRun("Algorithm loading complete", StrategyState.Running);
if (content is HeuristicLab.Algorithms.EvolutionStrategy.EvolutionStrategy)
{
algorithm = content as HeuristicLab.Algorithms.EvolutionStrategy.EvolutionStrategy;
VehicleRoutingProblem problem = algorithm.Problem as VehicleRoutingProblem;
if (problem == null)
{
Console.WriteLine("Unsupported problem type detected.");
return;
}
SetProblemInstance(problem);
ParameterizeProblem(problem);
ParameterizeAlgorithm(algorithm);
RegisterAlgorithmEventHandlers(algorithm);
try
{
algorithm.Start();
}
catch (Exception exc)
{
Console.WriteLine("Error Loading: " + exc.Message);
}
}
else
{
// TODO: update the run status to error...
//_strategyState = StrategyState.Stopped;
}
}
public void TestVRPInstances() {
var vrp = new VehicleRoutingProblem();
var providers = ProblemInstanceManager.GetProviders(vrp);
var failedInstances = new StringBuilder();
Assert.IsTrue(providers.Any(), "No providers could be found.");
foreach (var provider in providers) {
IEnumerable<IDataDescriptor> instances = ((dynamic)provider).GetDataDescriptors();
Assert.IsTrue(instances.Any(), string.Format("No instances could be found in {0}.", provider.Name));
foreach (var instance in instances) {
try {
// throws InvalidOperationException if zero or more than one interpreter is found
((dynamic)vrp).Load(((dynamic)provider).LoadData(instance));
} catch (Exception exc) {
failedInstances.AppendLine(instance.Name + ": " + exc.Message);
}
}
}
Assert.IsTrue(failedInstances.Length == 0, "Following instances failed: " + Environment.NewLine + failedInstances);
}
private ScatterSearch CreateScatterSearchVRPSample()
{
#region Problem Configuration
var provider = new SolomonInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name == "C101");
VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
vrpProblem.Load(provider.LoadData(instance));
#endregion
#region Algorithm Configuration
ScatterSearch ss = new ScatterSearch();
ss.Engine = new SequentialEngine.SequentialEngine();
ss.Name = "Scatter Search - VRP";
ss.Description = "A scatter search algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
ss.Problem = vrpProblem;
var improver = ss.Problem.Operators.OfType <VRPIntraRouteImprovementOperator>().First();
improver.ImprovementAttemptsParameter.Value.Value = 15;
improver.SampleSizeParameter.Value.Value = 10;
ss.Improver = improver;
var pathRelinker = ss.Problem.Operators.OfType <VRPPathRelinker>().First();
pathRelinker.IterationsParameter.Value.Value = 25;
ss.PathRelinker = pathRelinker;
var similarityCalculator = ss.SimilarityCalculatorParameter.ValidValues.OfType <VRPSimilarityCalculator>().First();
ss.SimilarityCalculator = similarityCalculator;
ss.MaximumIterations.Value = 2;
ss.PopulationSize.Value = 20;
ss.ReferenceSetSize.Value = 10;
ss.Seed.Value = 0;
return(ss);
#endregion
}
private void bwVehicleRouting_DoWork(object sender, DoWorkEventArgs e)
{
BackgroundWorker worker = (BackgroundWorker)sender;
algorithmStatus.Text = "Press ESC to cancel";
VehicleRoutingProblem problem = new VehicleRoutingProblem(routingCustomers, routingVehicles);
VehicleRoutingSolution startSolution = new VehicleRoutingSolution(problem);
Swap swap = new Swap(problem.Dimension, 1);
Shift shift = new Shift(problem.Dimension, 2);
TwoOpt twoOpt = new TwoOpt(problem.Dimension, 3);
List <Operator> operations = new List <Operator> {
swap, shift, twoOpt
};
MultistartParameters multistartParameters = (MultistartParameters)multistartOptions.Clone();
LocalDescentParameters ldParameters = new LocalDescentParameters()
{
Name = "VEHICLE LD",
Seed = seed,
DetailedOutput = true,
Operators = operations
};
SimulatedAnnealingParameters saParameters = new SimulatedAnnealingParameters()
{
Name = "VEHICLE SA",
InitProbability = 0.4,
TemperatureCooling = 0.95,
MinCostDeviation = 10E-3,
Seed = seed,
DetailedOutput = true,
Operators = operations
};
StackedParameters ssParameters = new StackedParameters()
{
Name = "B",
DetailedOutput = true,
OptimizationAlgorithms = new Type[] { typeof(LocalDescent), typeof(SimulatedAnnealing), typeof(LocalDescent) },
Parameters = new OptimizationParameters[] { ldParameters, saParameters, ldParameters }
};
switch (optimizerType)
{
case 0:
{
multistartParameters.Parameters = ldParameters;
multistartParameters.OptimizationAlgorithm = typeof(LocalDescent);
}
break;
case 1:
{
multistartParameters.Parameters = saParameters;
multistartParameters.OptimizationAlgorithm = typeof(SimulatedAnnealing);
}
break;
case 2:
{
saParameters.InitProbability = 0.002;
saParameters.MinCostDeviation = 10E-2;
multistartParameters.Parameters = ssParameters;
multistartParameters.OptimizationAlgorithm = typeof(StackedSearch);
}
break;
case 3:
{
saParameters.InitProbability = 0.01;
saParameters.MinCostDeviation = 10E-2;
multistartParameters.InstancesNumber = 3;
multistartParameters.Parameters = ssParameters;
multistartParameters.OptimizationAlgorithm = typeof(StackedSearch);
}
break;
}
vehicleRoutingOptimizer = new ParallelMultistart(multistartParameters);
toRenderBackground = false;
foreach (ISolution solution in vehicleRoutingOptimizer.Minimize(startSolution))
{
if (worker.CancellationPending)
{
vehicleRoutingOptimizer.Stop();
e.Cancel = true;
}
if (e.Cancel)
{
solution.IsFinal = false;
}
worker.ReportProgress(0);
}
}
private void ParameterizeProblem(VehicleRoutingProblem problem)
{
//set all parameters of the problem, e.g.
//problem.MaximizationParameter = false;
}
protected GamsSolverWrapper(VehicleRoutingProblem <TClient> problemData)
: this(problemData, @"C:\Program Files (x86)\GAMS22.2\gams.exe")
{
}
protected GamsSolverWrapper(VehicleRoutingProblem <TClient> problemData, string gamsSolverPath)
{
ProblemData = problemData;
GamsSolverPath = gamsSolverPath;
}
static void Main(string[] args)
{
//VehicleRoutingProblem vrProblem = new VehicleRoutingProblem(4, 2);
//VehicleRoutingSolution vrSolution = new VehicleRoutingSolution(vrProblem);
//Console.WriteLine(vrSolution.ToString());
//Console.WriteLine(vrSolution.CostValue);
//Console.ReadLine();
//return;
//FloorplanProblem problem = new FloorplanProblem(100);
//FloorplanProblem problem = FloorplanProblem.Load("500.vlsi");
//FloorplanSolution solution = new FloorplanSolution(problem);
//Swap swap = new Swap(problem.Dimension, 10);
//Shift shift = new Shift(problem.Dimension, 1);
//EmptyLeafMove eLeaf = new EmptyLeafMove(problem.Dimension, 5);
//FullLeafMove fLeaf = new FullLeafMove(problem.Dimension, 5);
//FullNodeMove fnode = new FullNodeMove(problem.Dimension, 5);
//List<Operator> operations = new List<Operator> { swap, fLeaf };
//TspProblem problem = new TspProblem(200);
//TspSolution solution = new TspSolution(problem);
VehicleRoutingProblem problem = new VehicleRoutingProblem(225, 10);
VehicleRoutingSolution solution = new VehicleRoutingSolution(problem);
Swap swap = new Swap(problem.Dimension, 1);
Shift shift = new Shift(problem.Dimension, 2);
TwoOpt twoOpt = new TwoOpt(problem.Dimension, 3);
List <Operator> operations = new List <Operator> {
swap, shift, twoOpt
};
MultistartParameters multistartOptions = new MultistartParameters()
{
Name = "P",
InstancesNumber = 5,
RandomizeStart = false,
DetailedOutput = true,
OutputFrequency = 100,
};
LocalDescentParameters ldParameters = new LocalDescentParameters()
{
Name = "LD",
DetailedOutput = true,
Seed = 0,
Operators = operations,
IsSteepestDescent = false
};
SimulatedAnnealingParameters saParameters = new SimulatedAnnealingParameters()
{
Name = "SA",
InitProbability = 0.5,
TemperatureCooling = 0.98,
MinCostDeviation = 0,
UseWeightedNeighborhood = false,
DetailedOutput = false,
Seed = 0,
Operators = operations,
};
MultistartParameters pldParameters = (MultistartParameters)multistartOptions.Clone();
pldParameters.OptimizationAlgorithm = typeof(LocalDescent);
pldParameters.Parameters = ldParameters;
MultistartParameters psaParameters = (MultistartParameters)multistartOptions.Clone();
psaParameters.OptimizationAlgorithm = typeof(SimulatedAnnealing);
psaParameters.Parameters = saParameters;
StackedParameters ssParameters = new StackedParameters()
{
Name = "B",
DetailedOutput = true,
OptimizationAlgorithms = new Type[] { typeof(LocalDescent), typeof(SimulatedAnnealing), typeof(LocalDescent) },
Parameters = new OptimizationParameters[] { ldParameters, saParameters, ldParameters }
};
StackedParameters sspParameters = new StackedParameters()
{
Name = "B",
DetailedOutput = false,
OptimizationAlgorithms = new Type[] { typeof(LocalDescent), typeof(ParallelMultistart), typeof(LocalDescent) },
Parameters = new OptimizationParameters[] { ldParameters, psaParameters, ldParameters }
};
LocalDescent ld = new LocalDescent(ldParameters);
SimulatedAnnealing sa = new SimulatedAnnealing(saParameters);
ParallelMultistart pld = new ParallelMultistart(pldParameters);
ParallelMultistart psa = new ParallelMultistart(psaParameters);
StackedSearch ss = new StackedSearch(ssParameters);
StackedSearch ssp = new StackedSearch(sspParameters);
MultistartParameters pssParameters = (MultistartParameters)multistartOptions.Clone();
pssParameters.DetailedOutput = false;
pssParameters.RandomizeStart = true;
pssParameters.OptimizationAlgorithm = typeof(StackedSearch);
pssParameters.Parameters = ssParameters;
ParallelMultistart pss = new ParallelMultistart(pssParameters);
IOptimizationAlgorithm optimizer = sa;
ISolution bestSolution = solution;
foreach (ISolution s in optimizer.Minimize(solution.Shuffle(0)))
{
//if (s.IsCurrentBest)
Console.WriteLine("\t{0}, {1:f}s, {2}, {3}, {4}, {5}, {6}", s.CostValue, s.TimeInSeconds, s.IterationNumber, s.IsCurrentBest, s.IsFinal, bestSolution.CostValue - s.CostValue, s.InstanceTag);
bestSolution = s;
}
Console.WriteLine(bestSolution.TimeInSeconds + "s");
Console.WriteLine(bestSolution.IterationNumber + " iterations");
//var groups = optimizer.SearchHistory.GroupBy(s => s.OperatorTag).Select(s => new { Operator = s.Key, Count = s.Count() });
//var dictionary = groups.ToDictionary(g => g.Operator, g => g.Count);
//foreach (var o in groups)
//{
// Console.WriteLine("{0} = {1}", o.Operator, o.Count);
//}
//Console.WriteLine("Done");
foreach (var b in DrawSolution(bestSolution, optimizer))
{
b?.Save("solution" + DateTime.Now.Millisecond + ".jpg");
}
Console.ReadLine();
}
