public TSPResult Run()
{
var result = _genetic.OptimizeForBestIndividual();
if (_use2opt)
{
result = Optimizer2Opt.Optimize(result);
}
var fitness = _tspModule.GetFitnessHistory();
return(new TSPResult(fitness, result)
{
ResolveInEpoch = _tspModule.ResolveCountInEpoch,
RandomizedResolveInEpoch = _tspModule.RandomizedResolveCountInEpoch,
ResolvePercentInEpoch = _tspModule.ConflictResolvesPercent,
RandomizedResolvePercentInEpoch = _tspModule.RandomResolvesPercent,
DifferencesInEpoch = _tspModule.DifferencesInEpoch.ToArray()
});
}
public static PathFindingResult GetPathAndFitness(int[] order, OptimizationParameters optimizationParameters,
CancellationToken ct, Random random)
{
IPathFinder algorithmPathFinding = optimizationParameters.OptimizationMethod switch
{
OptimizationMethod.Permutations => new Permutations(optimizationParameters),
OptimizationMethod.NearestNeighbor => new NearestNeighbor(optimizationParameters),
OptimizationMethod.GeneticAlgorithm => new GeneticPathFinding(order, optimizationParameters,
(population) =>
{
double[] fitness = new double[population.Length];
for (int i = 0; i < population.Length; i++)
{
fitness[i] = Fitness.CalculateFitness(population[i]);
}
return(fitness);
}, ct, random),
_ => throw new ArgumentException("Incorrect optimization method in config file")
};
int[] objectOrder = algorithmPathFinding.FindShortestPath(order);
if (optimizationParameters.Use2opt)
{
objectOrder = Optimizer2Opt.Optimize(objectOrder);
}
double pathLength = Fitness.CalculateFitness(objectOrder);
if (optimizationParameters.ResultToFile)
{
Log log = new Log(optimizationParameters);
log.SaveResult(objectOrder, pathLength);
}
return(new PathFindingResult()
{
Path = objectOrder,
Fitness = pathLength
});
}
}
