public static void Write(OutputData outputData, string path)
{
try
{
XmlSerializer serializer = new XmlSerializer(typeof(OutputData));
FileStream stream = new FileStream(@path, FileMode.Create);
serializer.Serialize(stream, outputData);
stream.Close();
}
catch (Exception ex)
{
ex.ToString();
}
}
private void tODOErgsSpeichernToolStripMenuItem_Click(object sender, EventArgs e)
{
if (_algorithm != null)
{
SaveFileDialog sfd = new SaveFileDialog();
sfd.Title = "Speichern ...";
sfd.Filter = sfd.Filter = "xml files (*.xml)|*.xml";
if (sfd.ShowDialog() == DialogResult.OK)
{
string path = sfd.FileName;
_outputData = _algorithm.GetOutputData();
OutputWriter.Write(_outputData, sfd.FileName);
}
}
else
{
MessageBox.Show(this, "Noch keine Ausgabedaten vorhanden", "Fehler", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
}
private void TryToSolveTSP()
{
//Zeichenform akzeptiert keine Mausklicks mehr
_drawForm.Invoke(new FormDisablementInvoker(_drawForm.IsAlgoRunning), true);
for (int iter = 0; iter < _iterCount; iter++)
{
//Überprüfen der Abbruchkriterien
if (_bestTourGloabl <= _optTourLength)
{
_owner.Invoke(new StopTimerInvoker(_owner.StopTimer));
MessageBox.Show("Optimale Tour gefunden");
return;
}
if (_bestTourGloabl <= _minTourLength)
{
_owner.Invoke(new StopTimerInvoker(_owner.StopTimer));
MessageBox.Show("Gewüsnchte Tour gefunden");
return;
}
//Werte der GUI und Bestwerte der Iteration aktualisieren
_owner.Invoke(new IterCountInvoker(_owner.SetNumberOfIters), iter + 1);
_owner.Invoke(new OutputWindowInvoker(_owner.SetBestTourIter), "0");
_owner.Invoke(new OutputWindowInvoker(_owner.SetAVRIter), "0");
_bestTourIter = double.PositiveInfinity;
_avrTourIter = 0;
List <City> currentWay = new List <City>();
//für jede Ameise
for (int iterAnt = 0; iterAnt < _antList.Count; iterAnt++)
{
if (_stopNow)
{
return;
}
Ant ant = _antList[iterAnt];
ArrayList currentWayAsArray = new ArrayList();
_owner.Invoke(new AntCountInvoker(_owner.SetNumberOfAnts), iterAnt + 1);
while (_antList[iterAnt].cityList.Count > 0)
{
//enthält den Wert der attraktivsten Strecke
double highestLikeliness = .0;
//enthält den Key zu der Stadt, die auf der attraktivsten Strecke liegt
int keyToHighestLikeliness = -1;
//für jede noch nicht besuchte Stadt der Ameise die likeliness berechnen
foreach (City cityToGo in _antList[iterAnt].cityList.Values)
{
// Nenner der Formel
double sum = .0;
int smallForNenner = -1;
int bigForNenner = -1;
//Berechnung des Nenners
foreach (City c in _antList[iterAnt].cityList.Values)
{
smallForNenner = c.key;
bigForNenner = _antList[iterAnt].city;
CheckIndices(ref smallForNenner, ref bigForNenner);
sum += Math.Pow(_cityList[smallForNenner][bigForNenner].phero, _alpha)
* Math.Pow(_cityList[smallForNenner][bigForNenner].atractivity, _beta);
if (sum == 0)
{
}
}
int small = cityToGo.key;
int big = _antList[iterAnt].city;
CheckIndices(ref small, ref big);
double likeliness = (Math.Pow(_cityList[small][big].phero, _alpha)
* Math.Pow(_cityList[small][big].atractivity, _beta))
/ (sum);
if (!((Double)likeliness).Equals(Double.NaN))
{
if (highestLikeliness <= likeliness)
{
highestLikeliness = likeliness;
keyToHighestLikeliness = cityToGo.key;
}
}
else
{
keyToHighestLikeliness = _antList[iterAnt].cityList.Keys[0];
}
}//Ende der Auswahl der nächsten Stadt
// Update walked distance
if (keyToHighestLikeliness >= 0)
{
int keyFrom = _antList[iterAnt].city;
int keyTo = keyToHighestLikeliness;
CheckIndices(ref keyFrom, ref keyTo);
double dist = _cityList[keyFrom][keyTo].distance;
// Ameise in neue Stadt setzen und neue Stadt aus Ameisenstädte löschen
// distance addieren
ant.walkedDistance += dist;
ant.city = keyToHighestLikeliness;
currentWay.Add(_cityList[keyFrom][keyTo]);
ant.DeleteCityFromList(keyToHighestLikeliness);
_antList[iterAnt] = ant;
int x = _tsp.Koords[keyToHighestLikeliness].X;
int y = _tsp.Koords[keyToHighestLikeliness].Y;
currentWayAsArray.Add(new Point(x, y));
}
}//Ameise ist alle Städte einmal durchgelaufen
int smallCity = ant.city;
int bigCity = ant.firstCity;
CheckIndices(ref smallCity, ref bigCity);
double newDist = _cityList[smallCity][bigCity].distance;
ant.walkedDistance += newDist;
ant.city = ant.firstCity;
int x2 = _tsp.Koords[ant.city].X;
int y2 = _tsp.Koords[ant.city].Y;
currentWayAsArray.Add(new Point(x2, y2));
_antList[iterAnt] = ant;;
// GUI aktualisieren
if (_antList[iterAnt].walkedDistance < _bestTourGloabl)
{
_bestWay = currentWayAsArray;
}
UpdateLength(iterAnt, iter);
_drawForm.Invoke(new UpdateWayByAntInvoker(_drawForm.ShowCurrentWay), currentWayAsArray, _bestWay);
//Pheromonupdate ausführen
for (int row = 1; row <= _cityList.Count; row++)
{
for (int col = row + 1; col < _cityList[row].Values.Count; col++)
{
City city = _cityList[row][col];
double deltaTau = .0;
if (currentWay.Contains(city))
{
deltaTau = _q / ant.walkedDistance;
}
city.phero = (1 - _rho) *
city.phero + deltaTau;
_cityList[row][col] = city;
}
}
}//Alle Ameisen sind für diese Iteration durchgelaufen
InitAntsAfterIter();
}//Alle Iterationen sind beendet
_owner.Invoke(new StopTimerInvoker(_owner.StopTimer));
MessageBox.Show("Alle Iterationen durchlaufen");
_drawForm.Invoke(new FormDisablementInvoker(_drawForm.IsAlgoRunning), false);
_outputData = new OutputData(_tau, _q, _rho, _alpha, _beta, _antCount, _iterCount,
_bestWay, _bestTourGloabl, _avrTourGloabl, _owner);
}
