private void DoMutation(Individual_NSGA individual)
{
double rnd, delta1, delta2, mut_pow, deltaq;
double y, yl, yu, val, xy;
Individual_NSGA x = new Individual_NSGA(individual);
for (int var = 0; var < individual.N_gens; var++)
{
if (m_Random.NextDouble() <= mutationProb)
{
y = x.Values[var];
yl = x.GetLowerBound();
yu = x.GetUpperBound();
delta1 = (y - yl) / (yu - yl);
delta2 = (yu - y) / (yu - yl);
rnd = m_Random.NextDouble();
mut_pow = 1.0 / (eta_m + 1.0);
if (rnd <= 0.5)
{
xy = 1.0 - delta1;
val = 2.0 * rnd + (1.0 - 2.0 * rnd) * (Math.Pow(xy, (distributionIndex + 1.0)));
deltaq = Math.Pow(val, mut_pow) - 1.0;
}
else
{
xy = 1.0 - delta2;
val = 2.0 * (1.0 - rnd) + 2.0 * (rnd - 0.5) * (Math.Pow(xy, (distributionIndex + 1.0)));
deltaq = 1.0 - (Math.Pow(val, mut_pow));
}
y = y + deltaq * (yu - yl);
if (y < yl)
{
y = yl;
}
if (y > yu)
{
y = yu;
}
x.Values[var] = y;
//x.SetValue(var, y);
}
}
}
private Individual_NSGA[] DoCrossover(double crossoverProbability, Individual_NSGA parent1, Individual_NSGA parent2)
{
Individual_NSGA[] offSpring = new Individual_NSGA[2];
offSpring[0] = new Individual_NSGA(parent1);
offSpring[1] = new Individual_NSGA(parent2);
int i;
double rand;
double y1, y2, yL, yu;
double c1, c2;
double alpha, beta, betaq;
double valueX1, valueX2;
Individual_NSGA x1 = new Individual_NSGA(parent1);
Individual_NSGA x2 = new Individual_NSGA(parent2);
Individual_NSGA offs1 = new Individual_NSGA(offSpring[0]);
Individual_NSGA offs2 = new Individual_NSGA(offSpring[1]);
int numberOfVariables = x1.N_gens;
if (m_Random.NextDouble() <= crossoverProbability)
{
for (i = 0; i < numberOfVariables; i++)
{
valueX1 = x1.Values[i];
valueX2 = x2.Values[i];
if (m_Random.NextDouble() <= 0.5)
{
if (Math.Abs(valueX1 - valueX2) > EPS)
{
if (valueX1 < valueX2)
{
y1 = valueX1;
y2 = valueX2;
}
else
{
y1 = valueX2;
y2 = valueX1;
}
yL = x1.GetLowerBound(); //lowerBound
yu = x1.GetUpperBound(); //upperBound
rand = m_Random.NextDouble();
beta = 1.0 + (2.0 * (y1 - yL) / (y2 - y1));
alpha = 2.0 - Math.Pow(beta, -(distributionIndex + 1.0));
if (rand <= (1.0 / alpha))
{
betaq = Math.Pow((rand * alpha), (1.0 / (distributionIndex + 1.0)));
}
else
{
betaq = Math.Pow((1.0 / (2.0 - rand * alpha)), (1.0 / (distributionIndex + 1.0)));
}
c1 = 0.5 * ((y1 + y2) - betaq * (y2 - y1));
beta = 1.0 + (2.0 * (yu - y2) / (y2 - y1));
alpha = 2.0 - Math.Pow(beta, -(distributionIndex + 1.0));
if (rand <= (1.0 / alpha))
{
betaq = Math.Pow((rand * alpha), (1.0 / (distributionIndex + 1.0)));
}
else
{
betaq = Math.Pow((1.0 / (2.0 - rand * alpha)), (1.0 / (distributionIndex + 1.0)));
}
c2 = 0.5 * ((y1 + y2) + betaq * (y2 - y1));
if (c1 < yL)
{
c1 = yL;
}
if (c2 < yL)
{
c2 = yL;
}
if (c1 > yu)
{
c1 = yu;
}
if (c2 > yu)
{
c2 = yu;
}
if (m_Random.NextDouble() <= 0.5)
{
offs1.Values[i] = c2;
offs2.Values[i] = c1;
//offs1.SetValue(i, c2);
//offs2.SetValue(i, c1);
}
else
{
offs1.Values[i] = c1;
offs2.Values[i] = c2;
//offs1.SetValue(i, c1);
//offs2.SetValue(i, c2);
}
}
else
{
offs1.Values[i] = valueX1;
offs2.Values[i] = valueX2;
//offs1.SetValue(i, valueX1);
//offs2.SetValue(i, valueX2);
}
}
else
{
offs1.Values[i] = valueX2;
offs2.Values[i] = valueX1;
//offs1.SetValue(i, valueX2);
//offs2.SetValue(i, valueX1);
}
}
}
return(offSpring);
}