//works
public void Mutate(Trevel individual)
{
Random rand = new Random();
if (rand.NextDouble() < this.mutationRate)
{
int first = (int)(rand.NextDouble() * individual.listOfCities.Count);
int second = (int)(rand.NextDouble() * individual.listOfCities.Count);
// Console.WriteLine("Start: " + first + " end: " + second);
while (first == second)
{
first = (int)(rand.NextDouble() * individual.listOfCities.Count);
second = (int)(rand.NextDouble() * individual.listOfCities.Count);
}
Location[] arr = individual.listOfCities.ToArray();
Location temp = arr[first];
arr[first] = arr[second];
arr[second] = temp;
individual.listOfCities = arr.ToList();
}
}
public void saveTheBest(Trevel bestInGen)
{
if ((this.theBest == null))
{
this.theBest = bestInGen;
}
else if (this.theBest.fitness > bestInGen.fitness)
{
this.theBest = bestInGen;
}
}
public Trevel findTheFittiestInGeneration(List <Trevel> pop)
{
Trevel best = pop[0];
foreach (Trevel temp in pop)
{
if (temp.fitness < best.fitness)
{
best = temp;
}
}
return(best);
}
public GenericAlgorithmForTSP(int maxgeneration, int maxInOneGeneration, Trevel listOfCities, Location s)
{
this.Generations = new List <Generation>();
this.MaxInOneGeneration = maxInOneGeneration;
this.MaxGenerations = maxgeneration;
this.places = listOfCities;
this.start = s;
this.places.listOfCities.Remove(s);
}
//works
public Trevel crossOver(Trevel parent1, Trevel parent2)
{
Trevel child = new Trevel();
Random rand = new Random();
int start = (int)(rand.NextDouble() * parent1.listOfCities.Count);
int end = (int)(rand.NextDouble() * parent1.listOfCities.Count);
Location[] childLoc = new Location[parent1.listOfCities.Count];
for (int i = 0; i < childLoc.Length; i++)
{
childLoc[i] = null;
}
for (int i = 0; i < parent1.listOfCities.Count; i++)
{
if ((start < end) && (i > start) && (i < end))
{
childLoc[i] = parent1.listOfCities[i];
}
else if ((end < start) && (i > end) && (i < start))
{
childLoc[i] = parent1.listOfCities[i];
}
}
for (int i = 0; i < parent2.listOfCities.Count; i++)
{
if (!childLoc.Contains(parent2.listOfCities[i]))
{
for (int j = 0; j < childLoc.Length; j++)
{
if (childLoc[j] == null)
{
childLoc[j] = parent2.listOfCities[i];
break;
}
}
}
}
child.listOfCities = childLoc.ToList();
return(child);
}
public void generateFirstgeneration()
{
Generation init = new Generation();
// new population
Trevel pop = new Trevel();
Random random = new Random();
List <KeyValuePair <int, int> > listOfAddedPlaces = new List <KeyValuePair <int, int> >();
for (int i = 0; i < MaxInOneGeneration; i++)
{
//generate some random id
foreach (Location item in places.listOfCities)
{
listOfAddedPlaces.Add(new KeyValuePair <int, int>(random.Next(), item.ID));
}
var sortedByRandomValue = from item in listOfAddedPlaces
orderby item.Key
select item;
//pop.listOfCities.Add(this.start);
foreach (KeyValuePair <int, int> item in sortedByRandomValue)
{
pop.listOfCities.Add(places.listOfCities.Find(x => x.ID == item.Value));
}
//pop.listOfCities.Add(this.start);
init.population.Add(pop);
listOfAddedPlaces = new List <KeyValuePair <int, int> >();
pop = new Trevel();
}
this.Generations.Add(init);
}
public static void readListOfCitiesFromFile(string filename, Trevel tour)
{
using (StreamReader fileReader = new StreamReader(filename))
{
string line = string.Empty;
int numberOfCities = 0;
string[] words;
Location current;
Console.WriteLine("Information about input data:");
for (int i = 0; i < 6; i++)
{
Console.WriteLine(line = fileReader.ReadLine());
words = line.Split(' ');
if (words[0] == "DIMENSION:")
{
// Console.WriteLine("Read:" + arr[1]);\
numberOfCities = Int32.Parse(words[1]);
}
}
for (int i = 0; i < numberOfCities; i++)
{
line = fileReader.ReadLine();
words = line.Split(" ");
current = new Location(Int32.Parse(words[0]), Double.Parse(words[1]), Double.Parse(words[2]));
// Console.WriteLine("Id: " + current.ID + " x: " + current.X + " y: " + current.Y);
tour.listOfCities.Add(current);
}
}
}
public int CompareTo(Object n)
{
Trevel par = (Trevel)n;
return((int)(Math.Round(this.fitness, 0) - Math.Round(par.fitness, 0)));
}
public void Selection_CreationOfOffspring_And_MutateNewIndividuals(int numberOfGeneration)
{
//we will create new generation
Generation newGeneration = new Generation();
List <Trevel> currentPopulation = this.Generations[numberOfGeneration].population;
double totalFitness = 0.0;
//calculate fitness for every indiviual
foreach (Trevel trevel in currentPopulation)
{
totalFitness += 1 / trevel.calculateFitness(this.start);
}
double previous = 0;
//calculate probability
foreach (Trevel temp in currentPopulation)
{
temp.probability = ((1 / temp.fitness / totalFitness)) + previous;
previous = temp.probability;
}
Random roullete = new Random();
List <Trevel> parents = new List <Trevel>();
while (newGeneration.population.Count < this.MaxInOneGeneration - (int)((currentPopulation.Count / 100.0) * 5))
{
while (parents.Count < 2)
{
Trevel temp = this.roullete(numberOfGeneration);
if (temp != null)
{
parents.Add(temp);
}
}
if (parents.Count == 2 && parents[0] != parents[1])
{
//create offspring
//and add new individual to new generation
newGeneration.population.Add(crossOver(parents[0], parents[1]));
}
previous = 0;
//calculate probability
foreach (Trevel temp in currentPopulation)
{
temp.probability = ((1 / temp.fitness / totalFitness)) + previous;
previous = temp.probability;
}
parents = new List <Trevel>();
}
Trevel bestInGen = findTheFittiestInGeneration(currentPopulation);
//to save the best if it is better than someone form earlyer solution
this.saveTheBest(bestInGen);
//mutation on new individuals
foreach (Trevel ind in newGeneration.population)
{
Mutate(ind);
}
//add beast 5 % offparents into newgeneration
for (int i = 0; i < (int)((currentPopulation.Count / 100.0) * 5); i++)
{
currentPopulation.Remove(bestInGen);
newGeneration.population.Add(bestInGen);
bestInGen = findTheFittiestInGeneration(currentPopulation);
}
this.Generations.Add(newGeneration);
}
