static void Main(string[] args)
{
TSPFileLoader fileLoader = new TSPFileLoader();
fileLoader.LoadAndListTSPFiles();
TSPProblem problem = fileLoader.SelectAndLoadFile();
//problem.PrintDistanceMatrix();
Console.WriteLine("\nRandom Algorithm");
Algorithm randomAlgorithm = new RandomAlgorithm(problem);
randomAlgorithm.PerformAlgorithm();
printAlgorithmResults(randomAlgorithm);
Console.WriteLine("\nGreedy Algorithm");
Algorithm greedyAlgorithm = new GreedyAlgorithm(problem);
greedyAlgorithm.PerformAlgorithm();
printAlgorithmResults(greedyAlgorithm);
GeneticAlgorithmParameters parameters = readParameters();
Algorithm geneticAlgorithm = new GeneticAlgorithm(problem)
{
Parameters = parameters,
Selection = new TournamentSelection(),
Crossover = new OrderedCrossover(),
Mutation = new InversionMutation()
};
geneticAlgorithm.PerformAlgorithm();
printAlgorithmResults(geneticAlgorithm);
}
private IList <Individual> generateGreedyPopulation(int populationSize)
{
IList <Individual> individuals = new List <Individual>(populationSize);
GreedyAlgorithm greedyAlgorithm = new GreedyAlgorithm(problem);
IList <Individual> greedyIndividuals = greedyAlgorithm.PerformAlgorithm();
individuals = individuals.Concat(greedyIndividuals.OrderBy(x => x.Fitness).Take(populationSize / 2)).ToList();
for (int i = greedyIndividuals.Count; i < populationSize; i++)
{
individuals.Add(problem.GenerateRandomIndividual());
}
return(individuals);
}
