// Runs several mTSP instances for several set of parameters. For each instance,
// the set of parameters that provided the best solution is stored along with the number
// of times it provided the best solution.
private static void calibrateGA(bool fitnessConsidersPartitions, int numberOfGenerations)
{
double bestCost = double.MaxValue;
int bestPop = -1;
double bestMut = -1.0, bestCross = -1.0;
int currPop;
double currMut, currCross;
var results = new Dictionary<Tuple<int, double, double>, int>();
string outputFile = !fitnessConsidersPartitions ? "output_strat1.txt" : "output_strat2.txt";
// Initializing the dictionary with the results stored from previous simulations.
results = Utils.getStoredResults(outputFile);
// Running the instances.
for (int i = 0; i < NUMBER_OF_TEST_CASES_CALIBRATION; i++)
{
// Initializing values for costs and generating cities.
bestCost = double.MaxValue;
CITIES = Utils.CreateCities().ToList();
// Generating a random value between 2 and 7 for the number of drones.
Random rnd = new Random();
NUMBER_OF_DRONES = rnd.Next(2, 8);
// j is the step multiplier for the mutation values.
for (int j = 0; j < 5; j++)
{
currMut = j * 0.05;
//k is the step multiplier for the crossover values.
for (int k = 0; k < 6; k++)
{
currCross = k * 0.2;
// m is the step multiplier for the population values.
for (int m = 1; m <= 4; m++)
{
currPop = m * 50;
// With the parameters already set, we finally run the GA.
Console.WriteLine("Running test case {0}", i + 1);
AlgorithmExecutor executor = new AlgorithmExecutor(
CITIES, DEPOT, NUMBER_OF_DRONES, numberOfGenerations,
currPop, currCross, currMut, fitnessConsidersPartitions, true /* isCalibration */);
executor.RunGAInstance();
// Since the operation above is synchronous, we can access here the solution.
double currCost = AlgorithmExecutor.getSolution();
// If the current set of parameters provided the best solution so far,
// we keep it stored.
if (currCost < bestCost)
{
bestCost = currCost;
bestMut = currMut;
bestCross = currCross;
bestPop = currPop;
}
}
}
}
// Assigning the best solution found for the current instance to its
// corresponding set of parameters.
Tuple<int, double, double> currKey = new Tuple<int, double, double>(bestPop, bestCross, bestMut);
if (!results.ContainsKey(currKey))
results[currKey] = 1;
else results[currKey]++;
// Updating the calibration output file with the newfound result.
Utils.updateCalibrationOutputFile(outputFile, results);
}
// After the calibration, print the results to the console.
Utils.printResultsFromCalibration(results);
}
// Runs the GA with the given parameters.
private static void runGAWithParams(
string fileName,
int numberOfDrones,
bool fitnessConsidersPartitions,
int population,
double crossover,
double mutation,
int numberOfGenerations)
{
NUMBER_OF_DRONES = numberOfDrones;
var lines = File.ReadAllLines(fileName + ".txt");
foreach (var line in lines)
{
string[] coords = line.Split(' ');
if (coords[0].Equals("1"))
{
DEPOT = new City("Depot", double.Parse(coords[1]), double.Parse(coords[2]));
}
else
{
CITIES.Add(new City("City " + coords[0], double.Parse(coords[1]), double.Parse(coords[2])));
}
}
AlgorithmExecutor executor = new AlgorithmExecutor(
CITIES, DEPOT, NUMBER_OF_DRONES, numberOfGenerations, population, crossover, mutation,
fitnessConsidersPartitions, false /* isCalibration */);
executor.RunGAInstance();
// Write the results from the simulation with the given number of drones
// to the specified file.
Utils.writeResultsToFile(fileName, numberOfDrones);
CITIES.Clear();
}
