public void DoMutation(double probability, Solution solution)
{
XReal x = new XReal(solution);
for (int var = 0; var < solution.DecisionVariables.Length; var++)
{
if (PseudoRandom.Instance().NextDouble() < probability)
{
double rand = PseudoRandom.Instance().NextDouble();
double tmp = (rand - 0.5)*_perturbation;
tmp += x.GetValue(var);
if (tmp < x.GetLowerBound(var))
{
tmp = x.GetLowerBound(var);
}
else if (tmp > x.GetUpperBound(var))
{
tmp = x.GetUpperBound(var);
}
x.SetValue(var, tmp);
}
}
}
public override void Evaluate(Solution solution)
{
XReal x = new XReal(solution);
double[] f = new double[NumberOfObjectives];
f[0] = x.GetValue(0);
double g = (EvalG(x));
double h = EvalH(f[0], g);
f[1] = h*g;
solution.Objective[0] = f[0];
solution.Objective[1] = f[1];
}
private void DoMutation(double probability, Solution solution)
{
XReal x = new XReal(solution);
for (int var = 0; var < solution.NumberOfVariables; var++)
{
if (PseudoRandom.Instance().NextDouble() <= probability)
{
double y = x.GetValue(var);
double yl = x.GetLowerBound(var);
double yu = x.GetUpperBound(var);
double delta1 = (y - yl)/(yu - yl);
double delta2 = (yu - y)/(yu - yl);
double rnd = PseudoRandom.Instance().NextDouble();
double mutPow = 1.0/(EtaMDefault + 1.0);
double xy;
double deltaq;
double val;
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, mutPow) - 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, mutPow));
}
y = y + deltaq*(yu - yl);
if (y < yl)
{
y = yl;
}
if (y > yu)
{
y = yu;
}
x.SetValue(var, y);
}
}
}
private double EvalG(XReal x)
{
double g = 0.0;
for (int i = 1; i < x.GetNumberOfDecisionVariables(); i++)
{
g += x.GetValue(i);
}
double constante = (9.0/(NumberOfVariables - 1));
g = constante*g;
g = g + 1.0;
return g;
}
public override Object Execute(Object obj)
{
var parameters = (Object[]) obj;
var current = (Solution) parameters[0];
var parent = parameters[1] as Solution[];
if (parent == null)
{
throw new Exception("parents in passed object do not exist");
}
if ((Array.Find(ValidTypes, n => n == parent[0].SolutionType.GetType()) == null) ||
(Array.Find(ValidTypes, n => n == parent[1].SolutionType.GetType()) == null) ||
(Array.Find(ValidTypes, n => n == parent[2].SolutionType.GetType()) == null))
{
throw new Exception("the solutions are not of the right type. The type should be 'Real' or 'ArrayReal', but"
+ parent[0].SolutionType.GetType() + "and"
+ parent[1].SolutionType.GetType() + "and"
+ parent[2].SolutionType.GetType() + "are obtained");
}
int numberOfVariables = current.NumberOfVariables;
int jrand = PseudoRandom.Instance().Next(numberOfVariables - 1);
var child = new Solution(current);
XReal xParent0 = new XReal(parent[0]);
XReal xParent1 = new XReal(parent[1]);
XReal xParent2 = new XReal(parent[2]);
XReal xCurrent = new XReal(current);
XReal xChild = new XReal(child);
switch (DeVariant)
{
case "rand/1/bin":
case "best/1/bin":
for (var j = 0; j < numberOfVariables; j++)
{
if (PseudoRandom.Instance().NextDouble() < Cr || j == jrand)
{
double value = xParent2.GetValue(j) + F*(xParent0.GetValue(j) -
xParent1.GetValue(j));
if (value < xChild.GetLowerBound(j))
{
value = xChild.GetLowerBound(j);
}
if (value > xChild.GetUpperBound(j))
{
value = xChild.GetUpperBound(j);
}
xChild.SetValue(j, value);
}
else
{
double value = xCurrent.GetValue(j);
xChild.SetValue(j, value);
}
}
break;
case "rand/1/exp":
case "best/1/exp":
for (var j = 0; j < numberOfVariables; j++)
{
if (PseudoRandom.Instance().NextDouble() < Cr || j == jrand)
{
double value = xParent2.GetValue(j) + F*(xParent0.GetValue(j) -
xParent1.GetValue(j));
if (value < xChild.GetLowerBound(j))
{
value = xChild.GetLowerBound(j);
}
if (value > xChild.GetUpperBound(j))
{
value = xChild.GetUpperBound(j);
}
xChild.SetValue(j, value);
}
else
{
Cr = 0.0;
double value = xCurrent.GetValue(j);
xChild.SetValue(j, value);
}
}
break;
case "current-to-rand/1":
case "current-to-best/1":
for (var j = 0; j < numberOfVariables; j++)
{
if (PseudoRandom.Instance().NextDouble() < Cr || j == jrand)
{
double value = xCurrent.GetValue(j) + K*(xParent2.GetValue(j) -
xCurrent.GetValue(j)) +
F*(xParent0.GetValue(j) - xParent1.GetValue(j));
if (value < xChild.GetLowerBound(j))
{
value = xChild.GetLowerBound(j);
}
if (value > xChild.GetUpperBound(j))
{
value = xChild.GetUpperBound(j);
}
xChild.SetValue(j, value);
}
}
break;
case "current-to-rand/1/bin":
case "current-to-best/1/bin":
for (var j = 0; j < numberOfVariables; j++)
{
if (PseudoRandom.Instance().NextDouble() < Cr || j == jrand)
{
double value = xCurrent.GetValue(j) + K*(xParent2.GetValue(j) -
xCurrent.GetValue(j)) +
F*(xParent0.GetValue(j) - xParent1.GetValue(j));
if (value < xChild.GetLowerBound(j))
{
value = xChild.GetLowerBound(j);
}
if (value > xChild.GetUpperBound(j))
{
value = xChild.GetUpperBound(j);
}
xChild.SetValue(j, value);
}
else
{
Cr = 0.0;
double value = xCurrent.GetValue(j);
xChild.SetValue(j, value);
}
}
break;
default:
throw new Exception("Unknown DE variant (" + DeVariant + ")");
}
return child;
}
public Solution[] DoCrossover(double probability,
Solution parent1,
Solution parent2)
{
Solution[] offSpring = new Solution[2];
offSpring[0] = new Solution(parent1);
offSpring[1] = new Solution(parent2);
XReal x1 = new XReal(parent1);
XReal x2 = new XReal(parent2);
XReal offs1 = new XReal(offSpring[0]);
XReal offs2 = new XReal(offSpring[1]);
int numberOfVariables = x1.GetNumberOfDecisionVariables();
if (PseudoRandom.Instance().NextDouble() <= probability)
{
int i;
for (i = 0; i < numberOfVariables; i++)
{
double upperValue = x1.GetUpperBound(i);
double lowerValue = x1.GetLowerBound(i);
double valueX1 = x1.GetValue(i);
double valueX2 = x2.GetValue(i);
double max;
double min;
if (valueX2 > valueX1)
{
max = valueX2;
min = valueX1;
}
else
{
max = valueX1;
min = valueX2;
}
double range = max - min;
// Ranges of the new alleles ;
double minRange = min - range*_alpha;
double maxRange = max + range*_alpha;
double random = PseudoRandom.Instance().NextDouble();
double valueY1 = minRange + random*(maxRange - minRange);
random = PseudoRandom.Instance().NextDouble();
double valueY2 = minRange + random*(maxRange - minRange);
if (valueY1 < lowerValue)
{
offs1.SetValue(i, lowerValue);
}
else if (valueY1 > upperValue)
{
offs1.SetValue(i, upperValue);
}
else
{
offs1.SetValue(i, valueY1);
}
if (valueY2 < lowerValue)
{
offs2.SetValue(i, lowerValue);
}
else if (valueY2 > upperValue)
{
offs2.SetValue(i, upperValue);
}
else
{
offs2.SetValue(i, valueY2);
}
}
}
return offSpring;
}
/** Returns the distance between two solutions in the search space.
* @param solutionI The first <code>Solution</code>.
* @param solutionJ The second <code>Solution</code>.
* @return the distance between solutions.
* @throws JMException
*/
public static double DistanceBetweenSolutions(Solution solutionI, Solution solutionJ)
{
double distance = 0.0;
XReal solI = new XReal(solutionI);
XReal solJ = new XReal(solutionJ);
double diff; //Auxiliar var
//-> Calculate the Euclidean distance
for (int i = 0; i < solI.GetNumberOfDecisionVariables(); i++)
{
diff = solI.GetValue(i) - solJ.GetValue(i);
distance += Math.Pow(diff, 2.0);
} // for
//-> Return the euclidean distance
return Math.Sqrt(distance);
}
private Solution[] DoCrossover(double probability, Solution parent1, Solution parent2)
{
var offSpring = new Solution[2];
offSpring[0] = new Solution(parent1);
offSpring[1] = new Solution(parent2);
int i;
double rand;
double y1, y2, yL, yu;
double c1, c2;
double alpha, beta, betaq;
double valueX1, valueX2;
XReal x1 = new XReal(parent1);
XReal x2 = new XReal(parent2);
XReal offs1 = new XReal(offSpring[0]);
XReal offs2 = new XReal(offSpring[1]);
int numberOfVariables = x1.GetNumberOfDecisionVariables();
if (PseudoRandom.Instance().NextDouble() <= probability)
{
for (i = 0; i < numberOfVariables; i++)
{
valueX1 = x1.GetValue(i);
valueX2 = x2.GetValue(i);
if (PseudoRandom.Instance().NextDouble() <= 0.5)
{
if (Math.Abs(valueX1 - valueX2) > Eps)
{
if (valueX1 < valueX2)
{
y1 = valueX1;
y2 = valueX2;
}
else
{
y1 = valueX2;
y2 = valueX1;
} // if
yL = x1.GetLowerBound(i);
yu = x1.GetUpperBound(i);
rand = PseudoRandom.Instance().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)));
} // if
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)));
} // if
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 (PseudoRandom.Instance().NextDouble() <= 0.5)
{
offs1.SetValue(i, c2);
offs2.SetValue(i, c1);
}
else
{
offs1.SetValue(i, c1);
offs2.SetValue(i, c2);
} // if
}
else
{
offs1.SetValue(i, valueX1);
offs2.SetValue(i, valueX2);
} // if
}
else
{
offs1.SetValue(i, valueX2);
offs2.SetValue(i, valueX1);
} // if
} // if
} // if
return offSpring;
}
