public void RandomSolutionShouldBeFeasible()
{
HeuristicsFactory.Register("SA", () => new SimulatedAnnealing());
MemeticAlgorithm algorithm = new MemeticAlgorithm();
Parameters parameters = new Parameters
{
PopulationSize = 30,
Selection = new RouletteSelection(),
CrossoverOperators = new List <CrossoverOperator>(),
MutationOperators = new List <MutationOperator>(),
CrossoverProbability = 0.75f,
MutationProbability = 0.05f,
Fitness = new FitnessFunction(4.0f, 1.0f),
ChromosomeFactory = new SolutionFactory(),
EliteChildrenCount = 3,
GeneCount = problem.Customers.Count,
FitnessStrategy = MemeticLibrary.Fitness.FitnessStrategy.MINIMIZE,
ConvergenceLimit = 1.0f,
PreservedChromosomesNumber = 10,
MaxIterations = 5000
};
parameters.CrossoverOperators.Add(new PartiallyMatchedCrossover());
parameters.MutationOperators.Add(new SwapOperator());
algorithm.Run(problem, parameters);
}
private void Register()
{
SelectionFactory.Register("roulette", () => new RouletteSelection());
CrossoverOperatorFactory.Register("OX", () => new OrderCrossover());
CrossoverOperatorFactory.Register("CX", () => new CycleCrossover());
CrossoverOperatorFactory.Register("PMX", () => new PartiallyMatchedCrossover());
CrossoverOperatorFactory.Register("UOX", () => new UniformBasedOrderCrossover());
MutationOperatorFactory.Register("swap", () => new SwapOperator());
MutationOperatorFactory.Register("insertion", () => new InsertionOperator());
MutationOperatorFactory.Register("inversion", () => new InversionOperator());
MutationOperatorFactory.Register("displacement", () => new DisplacementOperator());
HeuristicsFactory.Register("SA", () => new SimulatedAnnealing());
}
public void RegisterAlgorithm(string problemName, ParametersWebsite webParameters)
{
string filePath = HttpContext.Current.Server.MapPath("~/Data/Homberger/" + problemName.ToUpper() + @".txt");
VrptwProblem problem = new VrptwProblemReader().ReadFromFile(filePath);
string connecionId = Context.ConnectionId;
Register();
MemeticAlgorithm ma;
if (algorithms.ContainsKey(connecionId))
{
ma = algorithms[connecionId];
ma.Stop();
}
else
{
ma = new MemeticAlgorithm();
}
Parameters parameters = new Parameters
{
Heuristics = HeuristicsFactory.Create(webParameters.Heuristics),
PopulationSize = webParameters.PopulationSize,
Selection = SelectionFactory.Create(webParameters.Selection),
CrossoverOperators = new List <CrossoverOperator>(),
MutationOperators = new List <MutationOperator>(),
CrossoverProbability = webParameters.CrossoverProbability,
MutationProbability = webParameters.MutationProbability,
Fitness = new FitnessFunction(webParameters.FitnessRoutes, webParameters.FitnesDistance),
EliteChildrenCount = webParameters.EliteChildrenCount,
GeneCount = problem.Customers.Count,
FitnessStrategy = FitnessStrategy.MINIMIZE,
ConvergenceLimit = webParameters.ConvergenceLimit,
PreservedChromosomesNumber = webParameters.PreservedChromosomeNumber,
MaxIterations = webParameters.Iterations,
ChromosomeFactory = new SolutionFactory(),
HeuristicsParameters = webParameters.HeuristicsParameters
};
parameters.CrossoverOperators.Add(CrossoverOperatorFactory.Create(webParameters.Crossover));
parameters.MutationOperators.Add(MutationOperatorFactory.Create(webParameters.Mutation));
algorithms.TryAdd(connecionId, ma);
var result = ma.Run(problem, parameters, UpdateSolution, connecionId);
var solution = result.Result as Solution;
string outputPath = HttpContext.Current.Server.MapPath("~/Results/Memetic");
string outputFilePath = outputPath + @"/" + problem.Name + ".txt";
File.WriteAllText(outputFilePath, solution.ToFileText());
}
