public Individual Step()
{
if (xBest == null)
{
xBest = _individuals[rnd.Next(nt - 1)];
}
var x0 = xBest;
if (T > Tf)
{
for (int i = 0; i < nt; i++)
{
var x = _individuals[rnd.Next(nt - 1)];
delta = x.Z - x0.Z;
if (delta < 0)
{
if (x.Z < xBest.Z)
{
xBest = x;
}
}
else
{
r = (float)rnd.NextDouble();
if (r < Math.Exp(-delta / T))
{
x0 = x;
}
}
}
T *= alpha;
Distance();
}
return xBest;
}
public Graph()
{
Individuals = new List<Individual>();
bestIndividual = null;
}
private void NextIndividuals()
{
int count = NumberOfIndividuals;
if (bestIndividual != null)
{
count = NumberOfIndividuals - 1;
bestIndividual.Id = count;
Individuals.Add(bestIndividual);
//UpdatePoints(count, bestIndividual);
//points.Positions.Update(count, 1, new float[] { bestIndividual.X, bestIndividual.Y, bestIndividual.Z });
}
Random random = new Random();
for (int i = 0; i < count; i++)
{
var x = (float)NextDouble(random, newXmin, newXmax);
var y = (float)NextDouble(random, newYmin, newYmax);
if (OnlyIntegers)
{
x = (float)Math.Round(x);
y = (float)Math.Round(y);
}
TestFunctions testFunctions = new TestFunctions();
var z = RealizeMethod(new double[] { x, y });
Individual individual = new Individual { Id = i, X = x, Y = y, Z = z };
Individuals.Add(individual);
//UpdatePoints(i, individual);
//points.Positions.Update(i, 1, new float[] { individual.X, individual.Y, individual.Z });
}
}
private void UpdatePoints(int startColumn, Individual individual)
{
points.Positions.Update(startColumn, 1, new float[] { individual.X, individual.Y, individual.Z });
}
public void SetIndividuals()
{
switch (Algorithm)
{
case Algorithms.None:
bestIndividual = null;
Reset();
Individuals.Clear();
NextIndividuals();
break;
case Algorithms.BlindSearch:
bestIndividual = GetBestIndividual();
Individuals.Clear();
NextIndividuals();
break;
case Algorithms.SimulatedAnnealing:
simulatedAnnealing.Set(Individuals, Min, Max);
bestIndividual = simulatedAnnealing.Step();
newXmin = simulatedAnnealing.newXmin;
newXmax = simulatedAnnealing.newXmax;
newYmin = simulatedAnnealing.newYmin;
newYmax = simulatedAnnealing.newYmax;
Individuals.Clear();
NextIndividuals();
break;
case Algorithms.DifferentialEvolution:
differentialEvolution.Step(Individuals, MethodInfo, Min, Max);
Individuals.Clear();
Individuals = differentialEvolution.newIndividuals;
break;
case Algorithms.SOMA:
soma.Step(Individuals, MethodInfo, Min, Max);
Individuals.Clear();
Individuals = soma.newIndividuals;
break;
}
if (Individuals.Any())
{
var i = 0;
foreach (var item in Individuals)
{
UpdatePoints(i++, item);
}
}
ToFitness();
}
