// Worker thread
void SearchSolution( )
{
// create fitness function
TSPFitnessFunction fitnessFunction = new TSPFitnessFunction(map);
// create population
Population population = new Population(populationSize,
(greedyCrossover) ? new TSPChromosome(map) : new PermutationChromosome(citiesCount),
fitnessFunction,
(selectionMethod == 0) ? (ISelectionMethod) new EliteSelection( ) :
(selectionMethod == 1) ? (ISelectionMethod) new RankSelection( ) :
(ISelectionMethod) new RouletteWheelSelection( )
);
// iterations
int i = 1;
// path
double[,] path = new double[citiesCount + 1, 2];
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
// loop
while (!needToStop)
{
// run one epoch of genetic algorithm
population.RunEpoch( );
// display current path
ushort[] bestValue = ((PermutationChromosome)population.BestChromosome).Value;
for (int j = 0; j < citiesCount; j++)
{
path[j, 0] = map[bestValue[j], 0];
path[j, 1] = map[bestValue[j], 1];
}
path[citiesCount, 0] = map[bestValue[0], 0];
path[citiesCount, 1] = map[bestValue[0], 1];
mapControl.UpdateDataSeries("path", path);
// set current iteration's info
SetText(currentIterationBox, i.ToString( ));
SetText(pathLengthBox, Math.Round(fitnessFunction.PathLength(population.BestChromosome), 2).ToString( ));
// increase current iteration
i++;
//
if ((iterations != 0) && (i > iterations))
{
break;
}
}
stopWatch.Stop();
SetTextBox(Math.Round(stopWatch.Elapsed.TotalSeconds, 0) + "s lub " + Math.Round(stopWatch.Elapsed.TotalMilliseconds, 0) + "ms");
// enable settings controls
EnableControls(true);
}
// Worker thread
void SearchSolution()
{
// create fitness function
var fitnessFunction = new TSPFitnessFunction(map);
// create population
var population = new Population(populationSize,
(greedyCrossover) ? new TSPChromosome(map) : new PermutationChromosome(citiesCount),
fitnessFunction,
(selectionMethod == 0) ? new EliteSelection() :
(selectionMethod == 1) ? new RankSelection() :
(ISelectionMethod) new RouletteWheelSelection()
);
// iterations
var i = 1;
// path
var path = new double[citiesCount + 1, 2];
// loop
while (!needToStop)
{
// run one epoch of genetic algorithm
population.RunEpoch();
// display current path
var bestValue = ((PermutationChromosome)population.BestChromosome).Value;
for (var j = 0; j < citiesCount; j++)
{
path[j, 0] = map[bestValue[j], 0];
path[j, 1] = map[bestValue[j], 1];
}
path[citiesCount, 0] = map[bestValue[0], 0];
path[citiesCount, 1] = map[bestValue[0], 1];
mapControl.UpdateDataSeries("path", path);
// set current iteration's info
SetText(currentIterationBox, i.ToString());
SetText(pathLengthBox, fitnessFunction.PathLength(population.BestChromosome).ToString());
// increase current iteration
i++;
//
if ((iterations != 0) && (i > iterations))
{
break;
}
}
// enable settings controls
EnableControls(true);
}
void SearchSolution( )
{
// створення фітнес функції
TSPFitnessFunction fitnessFunction = new TSPFitnessFunction(map);
// створення популяції
Population population = new Population(populationSize,
(greedyCrossover) ? new TSPChromosome(map) : new PermutationChromosome(citiesCount),
fitnessFunction,
(selectionMethod == 0) ? (ISelectionMethod) new EliteSelection( ) :
(selectionMethod == 1) ? (ISelectionMethod) new RankSelection( ) :
(ISelectionMethod) new RouletteWheelSelection( )
);
// ітерації
int i = 1;
// петля
while (!needToStop)
{
// запус одного покоління генетичного алгоритму
population.RunEpoch( );
// відобразити поточний шлях
mapControl.Path = ((PermutationChromosome)population.BestChromosome).Value;
// встановити дані поточної ітерації
currentIterationBox.Text = i.ToString( );
pathLengthBox.Text = fitnessFunction.PathLength(population.BestChromosome).ToString( );
// наступна ітерація
i++;
if ((iterations != 0) && (i > iterations))
{
break;
}
}
EnableControls(true);
}
// Worker thread
void SearchSolution( )
{
// create fitness function
TSPFitnessFunction fitnessFunction = new TSPFitnessFunction( map );
// create population
Population population = new Population( populationSize,
( greedyCrossover ) ? new TSPChromosome( map ) : new PermutationChromosome( citiesCount ),
fitnessFunction,
( selectionMethod == 0 ) ? (ISelectionMethod) new EliteSelection( ) :
( selectionMethod == 1 ) ? (ISelectionMethod) new RankSelection( ) :
(ISelectionMethod) new RouletteWheelSelection( )
);
// iterations
int i = 1;
// path
double[,] path = new double[citiesCount + 1, 2];
// loop
while ( !needToStop )
{
// run one epoch of genetic algorithm
population.RunEpoch( );
// display current path
ushort[] bestValue = ((PermutationChromosome) population.BestChromosome).Value;
for ( int j = 0; j < citiesCount; j++ )
{
path[j, 0] = map[bestValue[j], 0];
path[j, 1] = map[bestValue[j], 1];
}
path[citiesCount, 0] = map[bestValue[0], 0];
path[citiesCount, 1] = map[bestValue[0], 1];
mapControl.UpdateDataSeries( "path", path );
// set current iteration's info
SetText( currentIterationBox, i.ToString( ) );
SetText( pathLengthBox, fitnessFunction.PathLength( population.BestChromosome ).ToString( ) );
// increase current iteration
i++;
//
if ( ( iterations != 0 ) && ( i > iterations ) )
break;
}
// enable settings controls
EnableControls( true );
}
// hlavna metoda programu
static void Main(string[] args)
{
// otvorenie vstupneho suboru
System.IO.StreamReader inputfile = new System.IO.StreamReader(args[0]);
string line;
int citycounter = 0;
while ((line = inputfile.ReadLine()) != null)
{
if (line == "")
{
citycounter++;
}
}
inputfile.Close();
// deklarovanie pola koordinacii pre vsetky mesta
map = new double[citycounter, 2];
// otvorenie vstupneho suboru
inputfile = new System.IO.StreamReader(args[0]);
int city = 0;
line = "yes";
while (true)
{
line = inputfile.ReadLine();
if (line == null)
{
break;
}
map[city, 0] = System.Convert.ToDouble(line);
line = inputfile.ReadLine();
map[city, 1] = System.Convert.ToDouble(line);
line = inputfile.ReadLine();
city++;
}
inputfile.Close();
// vytvorenie ohodnocovacej fitness funkcie
TSPFitnessFunction fitnessFunction = new TSPFitnessFunction(map);
// vytvorenie uvodnej populacie genetickeho algoritmu
Population population = new Population(POPULATION_SIZE,
( CROSSOVER ) ? new TSPChromosome(map) : new PermutationChromosome(citycounter),
fitnessFunction,
(SELECTION_METHOD == 0) ? (ISelectionMethod) new EliteSelection( ) :
(SELECTION_METHOD == 1) ? (ISelectionMethod) new RankSelection( ) :
(ISelectionMethod) new RouletteWheelSelection( )
);
// poradove cislo iteracie
int i = 1;
// cyklus genetickeho algoritmu
while (true)
{
// prebehnutie jednej generacie
population.RunEpoch( );
// vypis fitness najlepsieho jedinca aktualnej generacie
System.Console.WriteLine(System.Convert.ToInt32(fitnessFunction.PathLength(population.BestChromosome)).ToString( ));
// inkrementacie premennej uchovavajucej poradove cislo generacie
i++;
// ukoncovacia podmienka
// - dopredu znamy pocet opakovani
if (i > ITERATIONS)
{
break;
}
}
string s = (string)fitnessFunction.Translate(population.BestChromosome);
StringReader reader = new StringReader(s);
line = "yes";
int from = 0;
int to = 0;
int first = 0;
System.IO.StreamWriter outputfile = new System.IO.StreamWriter(args[1]);
outputfile.Write("<center><div id=\"canvas\" style=\"position:relative;width:700px;height:700px;background:white;float:center;\"></div></center><script type=\"text/JavaScript\">var canvasDiv=document.getElementById(\"canvas\");var gr = new jsGraphics(canvasDiv);var col = new jsColor(\"red\");var pen = new jsPen(col,1);var col2 = new jsColor(\"black\");");
line = reader.ReadLine();
from = System.Convert.ToInt32(line);
first = from;
while (line != null)
{
line = reader.ReadLine();
to = System.Convert.ToInt32(line);
if (line == null)
{
to = first;
}
outputfile.WriteLine("gr.drawLine(pen,new jsPoint(" + map[from, 0] + "," + map[from, 1] + "),new jsPoint(" + map[to, 0] + "," + map[to, 1] + "));");
from = to;
}
for (int x = 0; x < citycounter; x++)
{
outputfile.WriteLine("gr.fillCircle(col2,new jsPoint(" + map[x, 0] + "," + map[x, 1] + "),4);");
}
outputfile.Write("</script>");
outputfile.Close();
}
