// Calculates the fitness of a chromosome.
public static double CalculateFitness(Chromosome chromosome)
{
var distanceToTravel = 0.0;
if (!FITNESS_CONSIDERS_PARTITIONS)
{
distanceToTravel = CalculateDistance(chromosome);
}
else
{
var greedy_mTSP =
new GreedyAlgorithm(Utils.fittestToCitiesList(CITIES, chromosome), DEPOT, NUMBER_OF_DRONES);
distanceToTravel = greedy_mTSP.solve().Item1;
}
return 1 - distanceToTravel / 10000;
}
static void ga_OnRunComplete(object sender, GaEventArgs e)
{
var fittest = getFittestChromosome(e.Population);
// Call the algorithm to generate the mTSP solution.
var greedy_mTSP = new GreedyAlgorithm(Utils.fittestToCitiesList(CITIES, fittest), DEPOT, NUMBER_OF_DRONES);
solution = greedy_mTSP.solve().Item1;
partitionPoints = greedy_mTSP.solve().Item2;
timeElapsed = STOPWATCH.ElapsedMilliseconds;
fittestChromosome = fittest;
Console.WriteLine("Final solution (MinMax cost): {0}", solution);
Console.WriteLine("Time elapsed: {0} ms", STOPWATCH.ElapsedMilliseconds);
}
// Callback for when a generation completes its iteration.
private static void ga_OnGenerationComplete(object sender, GaEventArgs e)
{
// We print in the console the current results of the algorithm for every 100 generations
// when not in calibration mode. When calibration, we print only for the last generation.
if ((!IS_CALIBRATION && e.Generation % 100 == 0) || e.Generation == NUMBER_OF_GENERATIONS)
{
var fittest = getFittestChromosome(e.Population);
var distanceToTravel = 0.0;
if (!FITNESS_CONSIDERS_PARTITIONS)
{
distanceToTravel = CalculateDistance(fittest);
}
else
{
var greedy_mTSP =
new GreedyAlgorithm(Utils.fittestToCitiesList(CITIES, fittest), DEPOT, NUMBER_OF_DRONES);
distanceToTravel = greedy_mTSP.solve().Item1;
}
Console.WriteLine("Generation: {0}, Fitness: {1}, Distance: {2}", e.Generation, fittest.Fitness, distanceToTravel);
}
}