public void RunSingleResearch(Landscape landscape, ResearchParameters parameters, IOperator op, string instanceId,
string inputPath, string outputPath, Action <string, float> callback, string connectionId, float step)
{
string path = outputPath + @"\" + instanceId + "_" + op.GetId() + ".csv";
if (File.Exists(path))
{
callback(connectionId, step * (parameters.RandomWalkNumber));
return;
}
StringBuilder dataBuilder = new StringBuilder();
int threadNumber = Environment.ProcessorCount;
Thread[] rwThreads = new Thread[threadNumber];
int tasksPerThread = parameters.RandomWalkNumber / threadNumber;
for (int i = 0; i < threadNumber; ++i)
{
int start = i * tasksPerThread;
int end = (i + 1) * tasksPerThread;
if (i == threadNumber - 1)
{
end += parameters.RandomWalkNumber % threadNumber;
}
rwThreads[i] = new Thread(() =>
{
DoRandomWalkThread(start, end, landscape, parameters, op,
dataBuilder, callback, connectionId, step);
});
rwThreads[i].Start();
}
foreach (Thread thread in rwThreads)
{
thread.Join();
}
File.Create(path).Close();
using (TextWriter tw = new StreamWriter(path))
{
tw.WriteLine(CSV_HEADER);
tw.Write(dataBuilder.ToString());
}
}
public static async Task <Chromosome> DoMa(string filename, IOperator op)
{
var problem = new VrptwProblemReader().ReadFromFile(filename);
var crossovers = new List <CrossoverOperator>();
var mutations = new List <MutationOperator> {
};
if (op is CrossoverOperator)
{
crossovers.Add(op as CrossoverOperator);
}
else
{
mutations.Add(op as MutationOperator);
}
var parameters = new Parameters()
{
ChromosomeFactory = new SolutionFactory(),
ConvergenceLimit = 0.15f,
CrossoverOperators = crossovers,
CrossoverProbability = 0.8f,
EliteChildrenCount = 2,
Fitness = new FitnessFunction(200000, 1),
FitnessStrategy = FitnessStrategy.MINIMIZE,
GeneCount = problem.GeneCount(),
Heuristics = new SimulatedAnnealing(),
HeuristicsParameters = new float[] { 100, 0.90f, 50 },
MaxIterations = 300,
MutationOperators = mutations,
MutationProbability = 0.2f,
PopulationSize = 200,
PreservedChromosomesNumber = 2,
Selection = new TournamentSelection()
};
MemeticAlgorithm ma = new MemeticAlgorithm();
Chromosome sol = await ma.Run(problem, parameters, CallBackMa, op.GetId(), true);
return(sol);
}
