//instrukcja do przycisku reset
private void resetB_Click(object sender, EventArgs e)
{
i = 0;
j = 0;
avvelocity = new float[1];
avfitness = new float[1];
avbfitness = new float[1];
List <Particle> itemList = new List <Particle>();
population = new Populacja(populationestore.dim);
population = (Populacja)populationestore.Clone();
foreach (Particle item in populationestore.population)
{
Particle tmp = new Particle(populationestore.dim);
Particle.copy(item, tmp);
population.population.Add(tmp);
}
model = new Thread(GenGraph2);
model.IsBackground = true;
model.Start();
List <Populacja> tmp1 = new PSO(numberIterations, inertiaw, c1, c2, r1r2, linearinertia).PSOALG(population);
this.functionButtonSet(false);
animace = new Thread(ShowParticleMove);
animace.IsBackground = true;
animace.Start(tmp1);
}
private void ShowParticleOnGraph(Populacja tmp, int size = 10)
{
double best = tmp.population.Min(x => x.fitnessValue);
foreach (Particle item in tmp.population)
{
ILArray <float> coords = new float[3];
coords[0] = (float)item.position[0];
coords[1] = (float)item.position[1];
coords[2] = (float)item.fitnessValue;// +1000;
ILPoints bod = surface.Add(Shapes.Point);
//surface.Colormap = Colormaps.Hot;
if (item.fitnessValue == best)
{
bod.Color = Color.Red;
bod.Size = 10;
}
else
{
bod.Color = Color.Black;
bod.Size = size;
}
bod.Positions.Update(coords);
surface.Add(bod);
}
}
public static Populacja Copy()
{
Populacja item = new Populacja();
item.maxX = maxX;
item.minX = minX;
item.populationSize = populationSize;
item.type = type;
item.dim = dim;
return(item);
}
private void GenerateGraph()
{
Invoke(new Action(() => ilgraf.Visible = true));
MaxEpoch = new Populacja(MaxEpochSize, Funkcje.FunctionName.type);
MaxEpoch.SetRangeOfPopulation();
MaxEpoch.GenerateGraphPopulation();
MaxEpoch.ObliczPopulFitness();
population = new Populacja(PopulationSize, dim, Funkcje.FunctionName.type);
population.SetRangeOfPopulation();
population.GeneratePopulation(dim);
population.ObliczPopulFitness();
float[] newData = new float[MaxEpoch.population.Count * 3];
Parallel.For(0, MaxEpoch.population.Count, i =>
{
newData[i] = (float)MaxEpoch.population[i].fitnessValue; // Z
newData[i + MaxEpoch.population.Count] = (float)MaxEpoch.population[i].position[0]; // X
newData[i + 2 * MaxEpoch.population.Count] = (float)MaxEpoch.population[i].position[1]; // Y
});
int size = (int)Math.Sqrt(MaxEpoch.population.Count);
data = ILNumerics.ILMath.array(newData, size, size, 3);
surface = new ILSurface(data);
surface.Fill.Markable = false;
surface.Wireframe.Markable = false;
//surface.Colormap = Colormaps.Copper;
ILColorbar color = new ILColorbar()
{
Location = new PointF(.96f, 0.1f)
};
surface.Children.Add(color);
this.ShowParticleOnGraph(population);
//this.ShowGraphChart(population);
//this.ShowCharts(population);
//this.RefreshModel();
}
private void StartBtn_Click(object sender, EventArgs e)
{
//string f = FunctionSelectionCombo.SelectedItem.ToString();
//if (!String.IsNullOrEmpty(funkcja)&&!funkcja.Equals("Proszę wybrać funkcję do optymalizacji"))
//{
// ileCzastek = Convert.ToInt16(ParticleQuantityUpDown.Value);
// maxEpochs = Convert.ToInt16(MaxEpochUpDown.Value);
// optymalizacja = new PSO(dziedzinyFunkcji[funkcja], ileCzastek, maxEpochs, funkcja);
// MessageBox.Show(string.Format("Znalezione minimum to {0} z błędem {1}", PSO.PSOSolution().Item1, PSO.PSOSolution().Item2),"Rezultat optymalizacji",MessageBoxButtons.OK,MessageBoxIcon.Information);
// }
// else MessageBox.Show("Nie wybrano funkcji do optymalizacji","BŁĄD!",MessageBoxButtons.OK,MessageBoxIcon.Error);
// instrukcja do przycisku start
List <Populacja> tmp = new PSO(numberIterations, inertiaw, c1, c2, r1r2, linearinertia).PSOALG(population);
this.functionButtonSet(false);
animace = new Thread(ShowParticleMove);
animace.IsBackground = true;
animace.Start(tmp);
if (thesame == true)
{
resetB.Enabled = true;
}
double[] tab = new double[testnumber];
float[] sum = new float[numberIterations];
float[] best = new float[numberIterations];
float[] worst = new float[numberIterations];
float[] bgfworst = new float[numberIterations];
float[] bgfbest = new float[numberIterations];
float[] bgfav = new float[numberIterations];
float[] globalmin = new float[testnumber];
double wynik = 0;
double bestresult = 0;
double worstresult = 0;
double percentsucess = 0;
double tmpbest = 0;
double tmpworst = 0;
for (int i = 0; i < testnumber; ++i)
{
population = new Populacja(PopulationSize, dim, Funkcje.FunctionName.type);
population.SetRangeOfPopulation(Funkcje.FunctionName.type, error);
population.GeneratePopulation(dim);
population.ObliczPopulFitness(Funkcje.FunctionName.type);
//List<Populacja> tmp = new PSO(numberIterations, inertiaw, c1, c2, r1r2, linearinertia).PSOALG(population);//numberIterations, inertiaw
tmp.Remove(tmp.Last());
tab[i] = tmp.Min((x => x.NajlepszaFitness)); //tablica wartości wyników-z tego obliczyc % sukcesów
wynik += tab[i];
globalmin[i] = (float)tmp.Min((x => x.NajlepszaFitness));
if (Math.Abs(tab[i] - tmp.First().min) < tmp.First().exitError)
{
percentsucess++;
}
if (i == 0)
{
tmpbest = tab[i];
tmpworst = tab[i];
}
int popnumber = 0;
foreach (Populacja pop in tmp)
{
float b = 0;
float c = 0;
bgfav[popnumber] += (float)pop.NajlepszaFitness / testnumber;
var scene = new ILScene();
scene.Screen.First <ILLabel>().Visible = false;
foreach (Particle item in pop.population)
{
// MessageBox.Show(a.Length.ToString()+" " +tmp.populationSize.ToString());
b += (float)item.fitnessValue;
}
c = (b / pop.population.Count) / testnumber;
sum[popnumber] += c;
if (tab[i] <= tmpbest)
{
best[popnumber] = b / pop.population.Count;
bgfbest[popnumber] = (float)pop.NajlepszaFitness;
bestresult = pop.NajlepszaFitness;
tmpbest = tab[i];
}
if (tab[i] >= tmpworst)
{
worst[popnumber] = b / pop.population.Count;
bgfworst[popnumber] = (float)pop.NajlepszaFitness;
worstresult = pop.NajlepszaFitness;
tmpworst = tab[i];
}
popnumber++;
}
}
frm.richTextBox1.AppendText("Średnie wartości funkci: " + wynik / testnumber + "\n" + "\n");
frm.richTextBox1.AppendText("Najlepsza wartość funkcji: " + bestresult + "\n" + "\n");
frm.richTextBox1.AppendText("Najgorsza wartość funkcji: " + worstresult + "\n" + "\n");
frm.richTextBox1.AppendText("Procent sukcesu: " + percentsucess / testnumber * 100 + "%" + "\n" + "\n");
var scena = new ILScene();
using (ILScope.Enter())
{
ILArray <float> AV = sum;
ILArray <float> BEST = best;
ILArray <float> WORST = worst;
ILArray <float> BGFworst = bgfworst;
ILArray <float> BGFbest = bgfbest;
ILArray <float> BGFav = bgfav;
ILArray <float> GLOBAL = globalmin;
var plot = scena.Add(new ILPlotCube()
{
ScreenRect = new RectangleF(0, 0, 1, 0.4f),
Children = { new ILLinePlot(AV.T, lineColor:Color.Yellow),
new ILLinePlot(BEST.T, lineColor:Color.Blue),
new ILLinePlot(WORST.T, lineColor:Color.Red), }
});
var plot1 = scena.Add(new ILPlotCube()
{
ScreenRect = new RectangleF(0, 0.33f, 1, 0.4f),
Children = { new ILLinePlot(BGFav.T, lineColor:Color.Yellow),
new ILLinePlot(BGFbest.T, lineColor:Color.Blue),
new ILLinePlot(BGFworst.T, lineColor:Color.Red), },
});
var plot2 = scena.Add(new ILPlotCube()
{
ScreenRect = new RectangleF(0, 0.66f, 1, 0.3f),
Children = { new ILLinePlot(GLOBAL.T, markerStyle: MarkerStyle.Diamond, lineColor: Color.Black) },
});
var dg2 = plot2.AddDataGroup();
dg2.Add(new ILLinePlot(GLOBAL.T, markerStyle: MarkerStyle.Diamond, lineColor: Color.Red));//,lineColor: Color.Red));
dg2.ScaleModes.YAxisScale = AxisScale.Logarithmic;
var axisY2 = plot2.Axes.Add(new ILAxis(dg2)
{
AxisName = AxisNames.YAxis,
Position = new Vector3(1, 0, 0),
Label = { Text = "osiągnięte minimum (log)", Color = Color.Red },
Ticks = { DefaultLabel = { Color = Color.Red } }
});
frm.ilgraf.Scene = scena;
frm.Show();
}
}
public List <Populacja> PSOALG(Populacja p)
{
Random ran = new Random();
double minBound = p.minX;
double maxBound = p.maxX;
double minV = -1.0 * maxBound;
double maxV = maxBound;
int iteracja = 0;
double[] NajlepszaPozycja = new double[p.dim];
double NajlepszaFitness = double.MaxValue;
List <Populacja> roj = new List <Populacja>();
roj.Add(p);
foreach (Particle item in p.population)
{
if (item.fitnessValue < NajlepszaFitness)
{
NajlepszaFitness = item.fitnessValue;
item.position.CopyTo(NajlepszaPozycja, 0);
}
p.NajlepszaPozycja = new double[p.dim];
NajlepszaPozycja.CopyTo(p.NajlepszaPozycja, 0);
p.NajlepszaFitness = NajlepszaFitness;
}
double r1, r2; // poznawczy i ogólne spułczynniki randoma
// tworzenie roju ->swam
while (iteracja < nrIteracji)
{
++iteracja;
Populacja currP2 = new Populacja(p.type);
double[] newVelocity = new double[p.dim];
double[] newPosition = new double[p.dim];
double newFitness;
foreach (Particle item in roj.Last().population)
{
if (linearinertia)
{
this.w = setw(iteracja);
}
// główna pętla PSO przejście przez wszystkie iteracje
for (int j = 0; j < item.velocity.Length; ++j)
{
if (r1r2)
{
r1 = ran.NextDouble();
r2 = ran.NextDouble();
}
else
{
r1 = 1;
r2 = 1;
}
newVelocity[j] = (w * item.velocity[j]) +
(c1 * r1 * (item.bestPosition[j] - item.position[j])) +
(c2 * r2 * (NajlepszaPozycja[j] - item.position[j]));
if (newVelocity[j] < minV)
{
newVelocity[j] = minV;
}
else if (newVelocity[j] > maxV)
{
newVelocity[j] = maxV;
}
}
newVelocity.CopyTo(item.velocity, 0);
for (int j = 0; j < item.position.Length; ++j)
{
newPosition[j] = item.position[j] + newVelocity[j];
if (newPosition[j] < minBound)
{
newPosition[j] = minBound;
}
else if (newPosition[j] > maxBound)
{
newPosition[j] = maxBound;
}
}
Particle best = new Particle(p.dim);
newPosition.CopyTo(item.position, 0);
newPosition.CopyTo(best.position, 0);
Particle.ObliczIndiwFitness(best);
newFitness = best.fitnessValue;
item.fitnessValue = newFitness;
if (newFitness < item.bestFitness)
{
newPosition.CopyTo(item.bestPosition, 0);
item.bestFitness = newFitness;
}
if (newFitness < NajlepszaFitness)
{
newPosition.CopyTo(NajlepszaPozycja, 0);
NajlepszaFitness = newFitness;
}
Particle tmp = new Particle(p.dim);
//kopia
Particle.copy(item, tmp);
currP2.population.Add(tmp);
roj.Last().NajlepszaPozycja = new double[p.dim];
NajlepszaPozycja.CopyTo(roj.Last().NajlepszaPozycja, 0);
roj.Last().NajlepszaFitness = NajlepszaFitness;
}
roj.Add(currP2);
}
return(roj);
}
