public static string solveGreedy(TSP problem, int startCity)
{
List <int> toVisit = new List <int>();
for (int i = 1; i < problem.Dimention + 1; i++)
{
if (i != startCity)
{
toVisit.Add(i);
}
}
int currentCity = startCity;
int nextcity = -1;
string path = startCity + " ";
for (int i = 0; i < problem.Dimention - 1; i++)
{
float curmin = Single.MaxValue;
foreach (int city in toVisit)
{
if ((problem.Distances[currentCity, city] < curmin))
{
nextcity = city;
curmin = problem.Distances[currentCity, city];
}
}
toVisit.Remove(nextcity);
currentCity = nextcity;
path += currentCity + " ";
}
float length = problem.pathlength(path);
return(path);
}
static void Main(string[] args)
{
TSP problem0 = new TSP();
TSP problem1 = new TSP();
TSP problem2 = new TSP();
TSP problem3 = new TSP();
TSP problem4 = new TSP();
TSP problem5 = new TSP();
//Uwaga: należy wprowadzić prawidłową ścieżkę do pliku
problem0.importFromFile(@"/Users/aggami/Downloads/TSP/berlin11_modified.tsp");
problem1.importFromFile(@"/Users/aggami/Downloads/TSP/berlin52.tsp");
problem2.importFromFile(@"/Users/aggami/Downloads/TSP/kroA100.tsp");
problem3.importFromFile(@"/Users/aggami/Downloads/TSP/kroA150.tsp");
problem4.importFromFile(@"/Users/aggami/Downloads/TSP/kroA200.tsp");
problem5.importFromFile(@"/Users/aggami/Downloads/TSP/fl417.tsp");
//AlgorytmEwolucyjny algorytm = new AlgorytmEwolucyjny(problem2, 500, 300, "TNM");
EvolutionaryAlgorithmExperiments.testHistoryGenerations(problem2, 500, 300);
//EvolutionaryAlgorithmExperiments.finalTestAfter(new TSP[] { problem1, problem2, problem3, problem4, problem5 }, new int[] { 250, 500, 700, 850, 1000 }, new int[] { 250, 500, 700, 850, 1000 });
//EvolutionaryAlgorithmExperiments.testMutationProbs(problem1, 200, 200, 20, new double[] { 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.6, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testMutationProbs(problem2, 450, 450, 20, new double[] { 0.1, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.6, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testSelectionMethods(problem1, 150, 150);
//EvolutionaryAlgorithmExperiments.testSelectionMethods(problem2, 300, 300);
//EvolutionaryAlgorithmExperiments.testSelectionMethods(problem3, 500, 500);
//EvolutionaryAlgorithmExperiments.testSelectionMethods(problem4, 550, 550);
//EvolutionaryAlgorithmExperiments.testSelectionMethods(problem5, 700, 700);
//EvolutionaryAlgorithmExperiments.testRoulette(problem2, 500, 500);
//EvolutionaryAlgorithmExperiments.testRoulette(problem3, 700, 700);
//EvolutionaryAlgorithmExperiments.testRoulette(problem4, 800, 800);
//EvolutionaryAlgorithmExperiments.testRoulette(problem5, 1100, 1100);
//EvolutionaryAlgorithmExperiments.testHistoryGenerations(problem2, 500, 300);
//EvolutionaryAlgorithmExperiments.testHistoryGenerations(problem2, 300, 500);
//EvolutionaryAlgorithmExperiments.testHistoryGenerations(problem2, 500, 500);
//EvolutionaryAlgorithmExperiments.testHistoryGenerations(problem3, 700, 500);
//EvolutionaryAlgorithmExperiments.testHistoryGenerationsRou(problem3, 500, 700);
//EvolutionaryAlgorithmExperiments.testHistoryGenerations(problem3, 700, 700);
//EvolutionaryAlgorithmExperiments.testDifferentAlgorithms(new TSP[] { problem1, problem2, problem3, problem4, problem5 }, 700, 700);
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem2, 500, 500, 20, new double[] { 0.1, 0.2, 0.3, 0.5, 0.7, 0.8,0.85, 0.9, 1 });
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem3, 700, 700, 20, new double[] { 0.1, 0.2, 0.3, 0.5, 0.7, 0.8, 0.85, 0.9, 1 });
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem4, 700, 700, 20, new double[] { 0.1, 0.2, 0.3, 0.5, 0.7, 0.8, 0.85, 0.9, 1 });
//EvolutionaryAlgorithmExperiments.testGenerations(problem2, 500, new int[] { 100, 250, 300, 500, 700 });
//EvolutionaryAlgorithmExperiments.testGenerations(problem3, 700, new int[] { 250, 300, 400, 500, 700, 900 });
//EvolutionaryAlgorithmExperiments.testGenerations(problem4, 700, new int[] { 250, 400, 500, 700, 900 });
//EvolutionaryAlgorithmExperiments.testGenerations(problem5, 700, new int[] { 500, 700, 1000, 1100});
//EvolutionaryAlgorithmExperiments.testRoulette(problem2, 750, 750);
//EvolutionaryAlgorithmExperiments.testRoulette(problem3, 1000, 1000);
//EvolutionaryAlgorithmExperiments.testRoulette(problem4, 1300, 1300);
//EvolutionaryAlgorithmExperiments.testRoulette(problem5, 1300, 1300);
//EvolutionaryAlgorithmExperiments.testRoulette(problem1, 300, 300);
//EvolutionaryAlgorithmExperiments.testTournamentSize(problem1, 200, 200, new int[] {10 });
//EvolutionaryAlgorithmExperiments.testDifferentAlgorithms(new TSP[] { problem1, problem2, problem3 , problem4 , problem5 }, 300, 300);
//EvolutionaryAlgorithmExperiments.testPopulationSize(problem, new int[] { 100, 250, 400, 550, 700, 900}, 500);
//EvolutionaryAlgorithmExperiments.testTournamentSize(problem, 300, 300, new int[] { 2, 5, 10, 25, 50});
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem, 250, 250, 20, new double[] { 0, 0.2, 0.4, 0.5, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testMutationProbs(problem, 250, 250, 20, new double[] { 0.07, 0.1, 0.15, 0.2, 0.3, 0.4 });
//EvolutionaryAlgorithmExperiments.testTournamentSize(problem3, 250, 250, new int[] { 5, 10, 15, 20, 30 });
//EvolutionaryAlgorithmExperiments.testTournamentSize(problem4, 300, 300, new int[] { 5, 10, 15, 20, 30 });
//EvolutionaryAlgorithmExperiments.testTournamentSize(problem5, 300, 300, new int[] { 5, 10, 15, 20, 30 });
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem3, 250, 250, 20, new double[] { 0.2, 0.5, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem4, 300, 300, 25, new double[] { 0.2, 0.5, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem5, 300, 300, 25, new double[] { 0.2, 0.5, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testRoulette(problem5, 1000, 1000);
//EvolutionaryAlgorithmExperiments.testMutationProbs(problem1, 200, 200, 20, new double[] { 0.07, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testMutationProbs(problem3, 400, 400, 20, new double[] { 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testMutationProbs(problem4, 300, 300, 25, new double[] { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 });
// EvolutionaryAlgorithmExperiments.testMutationProbs(problem5, 300, 300, 25, new double[] { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6 });
//EvolutionaryAlgorithmExperiments.testGenerations(problem4, 300, new int[] { 100, 200, 500, 700 });
//EvolutionaryAlgorithmExperiments.testGenerations(problem5, 400, new int[] { 100, 200, 500, 700 });
//TSP problem2 = new TSP();
////Uwaga: należy wprowadzić prawidłową ścieżkę do pliku
//problem2.importFromFile(@"/Users/aggami/Downloads/TSP/kroA100.tsp");
////EvolutionaryAlgorithmExperiments.testTournamentSize(problem2, 450, 450, new int[] { 2, 5, 10, 25, 50, 100, 200 });
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem2, 450, 450, 25, new double[] {0, 0.2, 0.4, 0.5, 0.7, 0.8, 0.9 });
//EvolutionaryAlgorithmExperiments.testMutationProbs(problem2, 450, 450, 25, new double[] { 0.07, 0.1, 0.15, 0.2, 0.3, 0.4 });
//TSP problem = new TSP();
//////Uwaga: należy wprowadzić prawidłową ścieżkę do pliku
//problem.importFromFile(@"/Users/aggami/Downloads/TSP/fl417.tsp");
//EvolutionaryAlgorithmExperiments.testPopulationSize(problem, new int[] { 100, 450, 700, 1000, 1500 }, 1000);
//EvolutionaryAlgorithmExperiments.testGenerations(problem, 750, new int[] { 100, 400, 700, 1000, 1500 });
//EvolutionaryAlgorithmExperiments.testGenerationsAndPopulations(problem, new int[] { 100, 500, 1000 }, new int[] { 100, 500, 1000 });
//EvolutionaryAlgorithmExperiments.testTournamentSize(problem, 600, 600, new int[]{ 2, 5, 10, 25, 35, 50, 70, 100});
//EvolutionaryAlgorithmExperiments.testCrossoverProbs(problem2, 600, 600, 25, new double[] { 0, 0.2,0.5, 0.7, 0.9 });
//EvolutionaryAlgorithmExperiments.testMutationProbs(problem2, 600, 600, 25, new double[] { 0.07, 0.1, 0.2, 0.3});
//////algorytm z selekcją turniejową
////AlgorytmEwolucyjny algorytmT = new AlgorytmEwolucyjny(problem, 100, 100, "TNM");
////TSPGenotype gT = algorytmT.runAlgorithm();
////Console.WriteLine("Algorytm: ");
////Console.WriteLine(gT);
//////algorytm z selekcją ruletki
////AlgorytmEwolucyjny algorytmR = new AlgorytmEwolucyjny(problem, 200, 100, "ROU");
////TSPGenotype gR = algorytmR.runAlgorithm();
////Console.WriteLine("Algorytm: ");
////Console.WriteLine(gR);
////algorytm z selekcją turniejową
//AlgorytmEwolucyjny algorytmT2 = new AlgorytmEwolucyjny(problem2, 1000, 700, "TNM");
//TSPGenotype gT2 = algorytmT2.runAlgorithm();
//Console.WriteLine("Algorytm: ");
//Console.WriteLine(gT2);
////algorytm z selekcją ruletki
//AlgorytmEwolucyjny algorytmR2 = new AlgorytmEwolucyjny(problem2, 2000, 700, "ROU");
//TSPGenotype gR2 = algorytmR2.runAlgorithm();
//Console.WriteLine("Algorytm: ");
//Console.WriteLine(gR2);
/*
* //Console.WriteLine(problem.randomSolution());
*
* string randomAlgSolution = RandomSolution.run(10000, problem);
* Console.WriteLine("Najlepsze rozwiązanie: "+randomAlgSolution+ " Wynik: "+problem.pathlength(randomAlgSolution));
*
*
*
* Console.WriteLine("Rozwiązanie zachłanne dla 2: "+GreedyAlgorithm.solveGreedy(problem, 2));
*/
//TSP problem2 = new TSP();
//problem2.importFromFile(@"/Users/aggami/Downloads/TSP/berlin52.tsp");
////Console.WriteLine(problem2.algorithmInfo());
//AlgorytmEwolucyjny algorytm2 = new AlgorytmEwolucyjny(problem2, 1000, 1000, "ROU");
//TSPGenotype g2 = algorytm2.runAlgorithm();
//Console.WriteLine("Algorytm2 : ");
//Console.WriteLine(g2);
//for (int i = 0; i < 10; i++)
//{
// TSP problem4 = new TSP();
// problem4.importFromFile(@"/Users/aggami/Downloads/TSP/kroA100.tsp");
// //Console.WriteLine(problem4.algorithmInfo());
// AlgorytmEwolucyjny algorytm4 = new AlgorytmEwolucyjny(problem4, 1000, 1000, "ROU");
// TSPGenotype g4 = algorytm4.runAlgorithm();
// Console.WriteLine("Algorytm4 : ");
// Console.WriteLine(g4);
//}
/*
* TSP problem5 = new TSP();
* problem5.importFromFile(@"/Users/aggami/Downloads/TSP/gr666.tsp");
* Console.WriteLine(problem5.algorithmInfo());
* Console.WriteLine("Rozwiązanie zachłanne dla 3: " + problem5.solveGreedy(3));
*/
/*
* TSP problem3 = new TSP();
* problem3.importFromFile(@"/Users/aggami/Downloads/TSP/nrw1379.tsp");
* //Console.WriteLine(problem3.algorithmInfo());
* Console.WriteLine("Rozwiązanie zachłanne dla 3: " + problem3.solveGreedy(3));
*
*
*/
}
public static void testGenerations(TSP problem, int populationSize, int[] numOfGenerations)
{
string line;
TSPGenotype genotype;
var file = new System.IO.StreamWriter(@"/Users/aggami/Library/Mobile Documents/com~apple~CloudDocs/Studia/Semestr 6/SIiIW/" + problem.Name + "testGeneracjiPop=" + populationSize + ".txt");
file.AutoFlush = true;
string startLine = "Popsize; Avg; Min; Max; Stand.dev; AvgTime; ";
foreach (int ng in numOfGenerations)
{
Console.WriteLine("Start for value: " + ng);
float[] results = new float[10];
float min = Single.MaxValue;
float max = 0;
float sum = 0;
float avg = 0;
float dev = 0;
float temp;
var watch = System.Diagnostics.Stopwatch.StartNew();
line = ng.ToString() + "; ";
for (int i = 0; i < 10; i++)
{
Console.WriteLine("Run " + i);
AlgorytmEwolucyjny algorytmT = new AlgorytmEwolucyjny(problem, populationSize, ng, "TNM");
if (i == 0)
{
file.WriteLine(algorytmT.toString());
file.WriteLine(startLine);
}
genotype = algorytmT.runAlgorithm();
temp = genotype.fitness();
sum += temp;
results[i] = genotype.fitness();
if (temp > max)
{
max = temp;
}
if (temp < min)
{
min = temp;
}
}
watch.Stop();
avg = sum / 10;
for (int i = 0; i < 10; i++)
{
dev += (results[i] - avg) * (results[i] - avg);
}
dev = Convert.ToSingle(Math.Sqrt(dev / 10));
var elapsedTime = watch.ElapsedMilliseconds;
elapsedTime = elapsedTime / 10000;
line = ng + "; " + avg + "; " + min + "; " + max + "; " + dev + "; " + elapsedTime + ";";
file.WriteLine(line);
}
file.Close();
}
public static void testRoulette(TSP problem, int populationSize, int generationNum)
{
string line;
TSPGenotype genotype;
var file = new System.IO.StreamWriter(@"/Users/aggami/Library/Mobile Documents/com~apple~CloudDocs/Studia/Semestr 6/SIiIW/" + problem.Name + "testRuletki pop=" + populationSize + " gen=" + generationNum + ".txt");
AlgorytmEwolucyjny algorytmProb = new AlgorytmEwolucyjny(problem, populationSize, generationNum, "TNM");
algorytmProb.CrossoverProb = 0.9;
algorytmProb.MutationProb = 0.4;
string infoLine = algorytmProb.toString();
string startLine = "Method; Avg; Min; Max; Stand.dev; AvgTime; ";
file.WriteLine(startLine);
Console.WriteLine("Start ROU");
float[] results = new float[10];
float min = Single.MaxValue;
float max = 0;
float sum = 0;
float avg = 0;
float dev = 0;
float temp;
var watch = System.Diagnostics.Stopwatch.StartNew();
for (int i = 0; i < 10; i++)
{
Console.WriteLine("Run " + i);
AlgorytmEwolucyjny algorytmT = new AlgorytmEwolucyjny(problem, populationSize, generationNum, "ROU");
algorytmT.CrossoverProb = 0.9;
algorytmT.MutationProb = 0.4;
genotype = algorytmT.runAlgorithm();
temp = genotype.fitness();
sum += temp;
results[i] = genotype.fitness();
if (temp > max)
{
max = temp;
}
if (temp < min)
{
min = temp;
}
}
watch.Stop();
avg = sum / 10;
for (int i = 0; i < 10; i++)
{
dev += (results[i] - avg) * (results[i] - avg);
}
dev = Convert.ToSingle(Math.Sqrt(dev / 10));
var elapsedTime = watch.ElapsedMilliseconds;
elapsedTime = elapsedTime / 10000;
line = "Roulette" + "; " + avg + "; " + min + "; " + max + "; " + dev + "; " + elapsedTime + ";";
file.WriteLine(line);
file.Close();
}
public TSPGenotype(TSPGenotype parent)
{
this.problem = parent.problem;
this.genotype = (int[])parent.genotype.Clone();
}
public TSPGenotype(TSP problem)
{
this.problem = problem;
this.genotype = problem.randomSolutionInt();
}
public TSPGenotype(int[] genotype, TSP problem)
{
this.problem = problem;
this.genotype = genotype;
}
