// Optimization process
private async void btStart_Clicked(object sender, RoutedEventArgs e)
{
btStart.Visibility = Visibility.Collapsed;
btStop.Visibility = Visibility.Visible;
cvPage.Children.Clear();
OptMethod(method);
if (uniqueTest)
{
btRecreat.Visibility = Visibility.Collapsed;
tbResults.Text = "";
OptMethod(method);
if (routingTest == null)
{
routingTest = new RoutingTest(this.Optimizer, new RouteTable(pointCount), this.cvPage, this.tbResults);
}
routingTest.optimizer = this.Optimizer;
await Task.Run(() => this.routingTest.Optimize());
}
else if (method != 20)
{
tbResults.Text = "Initial parameters: " + List(InitialParameters) + "\r\n" +
"Lower bounds for the parameters: " + List(lbp) + "\r\n" +
"Upper bounds for the parameters: " + List(ubp) + "\r\n";
var x = Task.Run(() => (Optimizer.Optimize()));
var Results = await x;
tbResults.Text = tbResults.Text + "Initial fitness: " + Results.InfoList[InfoTypes.InitialFitness] + "\r\n" +
"Final parameters: " + List(Results.OptimizedParameters) + "\r\n" +
"Final fitness: " + $"{Results.InfoList[InfoTypes.FinalFitness],10:F6}" + "\r\n" +
"Number of generations: " + Results.InfoList[InfoTypes.Generations] + "\r\n" +
"Number of fitness evaluations: " + Results.InfoList[InfoTypes.Evaluations] + "\r\n" +
"Best affinities in each generation: " + List((ArrayList)Results.InfoList[InfoTypes.Affinities]);
}
else
{
tbResults.Text = "";
Result[,] results = new Result[9, 2];
var x = Task.Run(() => (Optimizer.Optimize()));
var Results = await x;
tbResults.Text += "Final Method: " + ((Type)Results.InfoList[InfoTypes.SelectAlgType]).Name + "\n\n";
foreach (Result item in (ArrayList)Results.InfoList[InfoTypes.SelectAlgInfos])
{
if (item.InfoList.ContainsKey(InfoTypes.SelectAlgFitness))
{
tbResults.Text += ((int)item.InfoList[InfoTypes.SelectAlgNum] + 1) + ". " + ((Type)item.InfoList[InfoTypes.AlgType]).Name + " fitness: " + $"{item.InfoList[InfoTypes.SelectAlgFitness],10:F30}" + "\n\n";
}
}
}
btStart.Visibility = Visibility.Visible;
if (btRecreat.Visibility == Visibility.Collapsed && this.uniqueTest)
{
btRecreat.Visibility = Visibility.Visible;
}
btStop.Visibility = Visibility.Collapsed;
}
private void SetFitnessFunction(FitnessFunctionDelegate argDelegate)
{
//uncheck
miXXplusYY.IsChecked = false;
miTripod.IsChecked = false;
miAlpine.IsChecked = false;
miGriewank.IsChecked = false;
miRosenbrock.IsChecked = false;
//set
ffd = argDelegate;
//preview
cvPage.Children.Clear();
if (Preview)
{
Optimizer = new PreviewDisplayer
{
LowerParamBounds = new ArrayList {
-100, -100
},
UpperParamBounds = new ArrayList {
100, 100
},
FitnessFunction = ffd
};
Optimizer.GenerationCreated += this.ShowAntibodies;
Optimizer.Optimize();
}
}
protected override void CreateNextGeneration()
{
ArrayList AllAlgInfos = new ArrayList();
Result[] result = new Result[3];
for (int l = 0; l < 3; l++)
{
Result[,] results = new Result[9, 2];
for (int j = 0; j < 9; j++)
{
for (int i = 0; i < 2; i++)
{
switch (i)
{
case 0:
StoppingType = StoppingType.GenerationNumber;
break;
case 1:
StoppingType = StoppingType.EvaluationNumber;
break;
case 2:
StoppingType = StoppingType.PerformanceTreshold;
break;
}
OptMethod(j);
Result Results = Optimizer.Optimize();
Results.InfoList.Add(InfoTypes.AlgType, Optimizer.GetType());
Results.InfoList.Add(InfoTypes.SelectAlgNum, l);
results[j, i] = Results;
AllAlgInfos.Add(Results);
}
}
result[l] = BestCalc(results);
}
int index = -1;
double id = double.MaxValue;
for (int i = 0; i < result.Length; i++)
{
if ((double)result[i].InfoList[InfoTypes.SelectAlgFitness] < id)
{
id = (double)result[i].InfoList[InfoTypes.SelectAlgFitness];
index = i;
}
}
Info.Add(InfoTypes.SelectAlgType, result[index].InfoList[InfoTypes.AlgType]);
Info.Add(InfoTypes.SelectAlgResult, result[index]);
Info.Add(InfoTypes.SelectAlgInfos, AllAlgInfos);
Stop = true;
}
private async void OptimizeButton_Click(object sender, RoutedEventArgs e)
{
/*if (fuzzySystem.Type == FuzzyType.Sugeno && CheckOutput.IsChecked == true)
* {
* MessageBox.Show("Output optimization is not valid for Sugeno-type fuzzy systems.", "Warning", MessageBoxButton.OK, MessageBoxImage.Warning);
* return;
* }*/
// Disable the optimize button and the optimization parameters
OptimizeButton.IsEnabled = false;
UseButton.IsEnabled = false;
RadioBaseRatio.IsEnabled = false;
RadioRefPoint.IsEnabled = false;
CheckInput.IsEnabled = false;
CheckOutput.IsEnabled = false;
// Initialize variables
ArrayList initParams = new ArrayList();
ArrayList lbp = new ArrayList();
ArrayList ubp = new ArrayList();
// Set up initial parameters
if (CheckInput.IsChecked == true)
{
foreach (InputOutput input in fuzzySystem.Inputs)
{
foreach (MembershipFunction MF in input.MFs)
{
if (RadioRefPoint.IsChecked == true)
{
initParams.Add((double)(MF.GetReferencePoint()));
lbp.Add((double)(input.Range[0]));
ubp.Add((double)(input.Range[1]));
}
if (RadioBaseRatio.IsChecked == true)
{
initParams.Add((double)(MF.GetTrapBaseRatio()));
lbp.Add(0.0);
ubp.Add(0.95);
}
}
}
}
if (CheckOutput.IsChecked == true)
{
foreach (InputOutput output in fuzzySystem.Outputs)
{
foreach (MembershipFunction MF in output.MFs)
{
if (RadioRefPoint.IsChecked == true)
{
initParams.Add((double)(MF.GetReferencePoint()));
lbp.Add((double)(output.Range[0]));
ubp.Add((double)(output.Range[1]));
}
if (RadioBaseRatio.IsChecked == true)
{
initParams.Add((double)(MF.GetTrapBaseRatio()));
lbp.Add(0.0);
ubp.Add(0.95);
}
}
}
}
bool[] ints = new bool[initParams.Count];
// Set up the optimizer object depending on the settings
switch (MethodId)
{
case 0: //Firework
Optimizer = new Firework
{
// Number of particles in the swarm.
NumberOfElements = NA,
m = m,
ymax = double.MaxValue,
a = a,
b = b,
Amax = Amax,
mhat = mhat,
Slow = false
};
break;
case 1: //Particle Swarm
Optimizer = new ParticleSwarm
{
// Number of particles in the swarm.
NumberOfElements = NA,
c0 = c0,
// Multiplication factor for the distance to the personal best position.
cp = cp,
// Multiplication factor for the distance to the global best position.
cg = cg,
Slow = false
};
break;
case 2: //Clonal generation
if (cgn > NA)
{
MessageBox.Show("Number of antibodies selected for cloning at the end of each generation greater than the number of antibodies.", "ERROR", MessageBoxButton.OK);
OptimizeButton.IsEnabled = true;
return;
}
Optimizer = new ClonalGeneration
{
// Size of the antibody pool.
NumberOfElements = NA,
// Number of antibodies selected for cloning.
NumberSelectedForCloning = cgn,
// Parameter determining the number of clones created for an antibody that was selected for cloning. (0,1]
Beta = beta,
// Number of antibodies created with random parameters in each new generation.
RandomAntibodiesPerGeneration = d,
// Mutation coefficient (0,1].
Ro = ro,
Slow = false
};
break;
case 3:
Optimizer = new GeneticAlgorithm
{
// Size of the individual pool.
NumberOfElements = NA,
// Number of parents in each generation.
ParentsInEachGeneration = P,
// The probability of mutation.
MutationProbability = pm,
// The number of crossovers in each generation.
CrossoverPerGeneration = crossoverCount,
Slow = false
};
break;
case 4: //Clonal generation
if (cgn > NA)
{
MessageBox.Show("Number of antibodies selected for cloning at the end of each generation greater than the number of antibodies.", "ERROR", MessageBoxButton.OK);
OptimizeButton.IsEnabled = true;
return;
}
Optimizer = new ClonalGenerationLocal
{
// Size of the antibody pool.
NumberOfElements = NA,
// Number of antibodies selected for cloning.
NumberSelectedForCloning = cgn,
// Parameter determining the number of clones created for an antibody that was selected for cloning. (0,1]
Beta = beta,
// Number of antibodies created with random parameters in each new generation.
RandomAntibodiesPerGeneration = d,
// Mutation coefficient (0,1].
Ro = ro,
// Local searches per generation
LocalSearchesPerGeneration = cgln,
Slow = false
};
break;
}
// Set common parameters
Optimizer.InitialParameters = initParams;
Optimizer.LowerParamBounds = lbp;
Optimizer.UpperParamBounds = ubp;
// Bounds are required before setting this value
if (MethodId == 4)
{
((ClonalGenerationLocal)Optimizer).StepSizeRelative = cglssr;
}
Optimizer.Integer = ints;
Optimizer.FitnessFunction = FitnessFunctionMSE;
Optimizer.StoppingNumberOfGenerations = StopGenerationNumber;
Optimizer.StoppingNumberOfEvaluations = StopAffinityEvaluation;
Optimizer.StoppingFitnessTreshold = StopFitnessTreshold;
Optimizer.StoppingType = StopType;
// Output string
string outstr = "Initial parameters: " + List(initParams) + "\r\n";
// Open progress window and begin the optimization
ProgressWindow = new OptimizationProgressWindow();
ProgressWindow.OptWindow = this;
ProgressWindow.Show();
if (IoputMfWindows != null)
{
foreach (MFsPreviewWindow window in IoputMfWindows)
{
window.Close();
}
}
IoputMfWindows = new List <MFsPreviewWindow>();
foreach (InputOutput input in fuzzySystem.Inputs)
{
IoputMfWindows.Add(new MFsPreviewWindow(input));
IoputMfWindows[IoputMfWindows.Count - 1].Show();
}
foreach (InputOutput output in fuzzySystem.Outputs)
{
IoputMfWindows.Add(new MFsPreviewWindow(output));
IoputMfWindows[IoputMfWindows.Count - 1].Show();
}
Optimizer.GenerationCreated += OnNewGeneration;
// <DELAYER>
/*var z = Task.Run(() => (delayer()));
* int y = await z;*/
// </DELAYER>
var x = Task.Run(() => (Optimizer.Optimize()));
var Results = await x;
ProgressWindow.Close();
// Add results to output string
outstr += "Initial MSE: " + Results.InfoList[InfoTypes.InitialFitness] + "\r\n" +
"Final parameters: " + List(Results.OptimizedParameters) + "\r\n" +
"Final MSE: " + $"{Results.InfoList[InfoTypes.FinalFitness],10:F6}" + "\r\n" +
"Number of generations: " + Results.InfoList[InfoTypes.Generations] + "\r\n" +
"Number of fitness evaluations: " + Results.InfoList[InfoTypes.Evaluations] + "\r\n" +
"Best affinities in each generation: " + List((ArrayList)Results.InfoList[InfoTypes.Affinities]) + "\r\n" +
"Optimization time in milliseconds: " + Results.InfoList[InfoTypes.ExecutionTime];
fuzzySystem = GetModifiedFuzzySystem(Results.OptimizedParameters);
FuzzyBox.Text = fuzzySystem.ToString();
// Display results and reenable the optimize button
MessageBox.Show(outstr, "Optimization finished", MessageBoxButton.OK);
OptimizeButton.IsEnabled = true;
UseButton.IsEnabled = true;
RadioBaseRatio.IsEnabled = true;
RadioRefPoint.IsEnabled = true;
CheckInput.IsEnabled = true;
CheckOutput.IsEnabled = true;
}
public void Optimize()
{
Result result = null;
StreamWriter dok = new StreamWriter(this.optimizer.GetType().ToString() + ".txt");
Application.Current.Dispatcher.Invoke((Action)(() =>
{
foreach (Car car in cars)
{
tbResults.Text += car.index + ". Car MaxDailyPath: " + car.MaxDailyPath + " MaxPathPoint: " + car.MaxPathPoint + " MaxPathDistance: " + car.MaxPathDistance + "\n";
dok.WriteLine(car.index + ". Car MaxDailyPath: " + car.MaxDailyPath + " MaxPathPoint: " + car.MaxPathPoint + " MaxPathDistance: " + car.MaxPathDistance + "\n");
}
dok.WriteLine();
tbResults.Text += "\n";
for (int i = 0; i < this.fullTable.routeTable.Count; i++)
{
Dictionary <int, User> user = this.fullTable.routeTable[i];
tbResults.Text += i + " |";
dok.Write(i + " |");
foreach (int key in user.Keys)
{
tbResults.Text += "=>" + key + " : " + $"{user[key].km,10:F2}" + "-" + user[key].dem + "|";
dok.Write("=>" + key + " : " + $"{user[key].km,10:F2}" + "-" + user[key].dem + "|");
}
dok.WriteLine();
tbResults.Text += "\n";
}
tbResults.Text += "\n";
dok.WriteLine();
}), DispatcherPriority.Send, null);
double avgAllFinalFitness = 0.0, avgAllInitFitness = 0.0, avgAllTime = 0.0;
for (int i = 0; i < 3; i++)
{
double avgTime = 0.0, avgFitness = 0.0, initAvgFitness = 0.0;
double avgEarchTime = 0.0;
foreach (Car car in this.cars)
{
car.reset();
}
double sumAvgEarchTime = 0.0;
ArrayList pathLinesTmp = new ArrayList();
pathLinesTmp.AddRange(pathLines);
double pathDistance = 0.0;
avgTime = DateTime.UtcNow.Subtract(new DateTime(1970, 1, 1)).TotalSeconds;
int g = 0;
while (pathLinesTmp.Count > 0)
{
ArrayList path = (ArrayList)pathLinesTmp[g % pathLinesTmp.Count];
Car car = this.searchCar(path.Count, pathDistance);
if (car != null)
{
avgEarchTime = DateTime.Now.Millisecond;
ArrayList pathTmp = new ArrayList();
foreach (int point in path)
{
pathTmp.Add((double)point);
}
this.optimizer.InitialParameters = pathTmp;
if (optimizer is ParticleSwarm)
{
((ParticleSwarm)this.optimizer).reset();
}
if (optimizer is ArtificialBeeColony)
{
((ArtificialBeeColony)this.optimizer).reset();
}
fullTable.pathList = path.Cast <int>().ToList();
this.optimizer.FitnessFunction = fullTable.FitnessFunction;
ArrayList finalParam = new ArrayList();
car.Process = Task <Result> .Factory.StartNew(() => optimizer.Optimize());
result = car.Process.Result;
double finalFitness = (double)result.InfoList[InfoTypes.FinalFitness];
if (car.MaxPathDistance < finalFitness)
{
pathDistance = finalFitness;
continue;
}
else
{
car.MaxPathDistance -= finalFitness;
if (car.MaxPathDistance < 0)
{
car.MaxPathDistance = 0.0;
}
pathDistance = 0.0;
}
g++;
Application.Current.Dispatcher.Invoke((Action)(() =>
{
tbResults.Text += i + " - " + g + " " + "Car ID: " + car.index + " Initial positions: " + List(fullTable.Encrypt(pathTmp)) + "\r\n" +
"Initial fitness: " + $"{result.InfoList[InfoTypes.InitialFitness],10:F5}" + "\r\n" +
"Final positions: " + List(fullTable.Encrypt(result.OptimizedParameters)) + "\r\n" +
"Final fitness: " + $"{finalFitness,10:F5}" + "\r\n\n";
ShowRoutingAntibodies(fullTable.Encrypt(result.OptimizedParameters));
}), DispatcherPriority.Send, null);
dok.Write("\n" + i + " - " + g + " " + "Car ID: " + car.index + " Initial positions: " + List(fullTable.Encrypt(pathTmp)) + "\r\n" +
"Initial fitness: " + $"{result.InfoList[InfoTypes.InitialFitness],10:F5}" + "\r\n" +
"Final positions: " + List(fullTable.Encrypt(result.OptimizedParameters)) + "\r\n" +
"Final fitness: " + $"{finalFitness,10:F5}" + "\r\n\n");
sumAvgEarchTime += avgEarchTime - DateTime.Now.Millisecond;
avgFitness += finalFitness;
initAvgFitness += (double)result.InfoList[InfoTypes.InitialFitness];
car.Process = null;
car.CurrentDailyPath++;
}
else
{
Debug.WriteLine("Nincs alkalmas jármű! fent maradt utak száma: " + pathLinesTmp.Count);
break;
}
}
avgAllFinalFitness += avgFitness / g;
avgAllInitFitness += initAvgFitness / g;
sumAvgEarchTime /= pathLines.Count;
avgTime = DateTime.UtcNow.Subtract(new DateTime(1970, 1, 1)).TotalSeconds - avgTime;
avgAllTime += avgTime;
}
Application.Current.Dispatcher.Invoke((Action)(() => {
tbResults.Text += "Initial Avg fitness: " + $"{avgAllInitFitness / 3,10:F5} \r\n";
tbResults.Text += "Final Avg fitness: " + $"{avgAllFinalFitness / 3,10:F5} \r\n";
tbResults.Text += "Final Avg time: " + $"{avgAllTime / 3,10:F3} second \r\n\n";
tbResults.Text += "Final Optimum ratio: " + $"{100 * (1 - (avgAllFinalFitness / 3) / (avgAllInitFitness / 3)),10:F3} % \r\n\n";
}), DispatcherPriority.Send, null);
dok.Write("\n\nInitial Avg fitness: " + $"{avgAllInitFitness / 3,10:F5} \r\n");
dok.Write("Final Avg fitness: " + $"{avgAllFinalFitness / 3,10:F5} \r\n");
dok.Write("Final Avg time: " + $"{avgAllTime / 3,10:F3} second \r\n\n");
dok.Write("Final Optimum ratio: " + $"{100 * (1 - (avgAllFinalFitness / 3) / (avgAllInitFitness / 3)),10:F3} % \r\n\n");
dok.Close();
}