/// <summary>
///
/// </summary>
/// <param name="x"></param>
/// <returns></returns>
public double EvalG(XReal x)
{
double g = 0.0;
for (int i = 1; i < x.GetNumberOfDecisionVariables(); i++)
{
g += x.GetValue(i);
}
double constant = (9.0 / (NumberOfVariables - 1));
g = constant * g;
g = g + 1.0;
return(g);
}
/// <summary>
/// /Update the speed of each particle
/// </summary>
/// <param name="iter"></param>
/// <param name="miter"></param>
private void ComputeSpeed(int iter, int miter)
{
double r1, r2, W, C1, C2;
double wmax, wmin;
XReal bestGlobal;
for (int i = 0; i < swarmSize; i++)
{
XReal particle = new XReal(particles.Get(i));
XReal bestParticle = new XReal(best[i]);
//Select a global best for calculate the speed of particle i, bestGlobal
Solution one, two;
int pos1 = JMetalRandom.Next(0, Leaders.Size() - 1);
int pos2 = JMetalRandom.Next(0, Leaders.Size() - 1);
one = Leaders.Get(pos1);
two = Leaders.Get(pos2);
if (crowdingDistanceComparator.Compare(one, two) < 1)
{
bestGlobal = new XReal(one);
}
else
{
bestGlobal = new XReal(two);
//Params for velocity equation
}
r1 = JMetalRandom.NextDouble(r1Min, r1Max);
r2 = JMetalRandom.NextDouble(r2Min, r2Max);
C1 = JMetalRandom.NextDouble(C1Min, C1Max);
C2 = JMetalRandom.NextDouble(C2Min, C2Max);
W = JMetalRandom.NextDouble(WMin, WMax);
wmax = WMax;
wmin = WMin;
for (int var = 0; var < particle.GetNumberOfDecisionVariables(); var++)
{
//Computing the velocity of this particle
speed[i][var] = VelocityConstriction(ConstrictionCoefficient(C1, C2) * (InertiaWeight(iter, miter, wmax, wmin) * speed[i][var] + C1 * r1 * (bestParticle.GetValue(var) - particle.GetValue(var)) + C2 * r2 * (bestGlobal.GetValue(var) - particle.GetValue(var))),
deltaMax,
deltaMin,
var,
i);
}
}
}
/// <summary>
/// Returns the value of the ZDT1 function G.
/// </summary>
/// <param name="x">Solution</param>
/// <returns></returns>
private double EvalG(XReal x)
{
double g = 0;
double tmp;
for (int i = 1; i < x.GetNumberOfDecisionVariables(); i++)
{
tmp = x.GetValue(i);
g += tmp;
}
double constant = (9.0 / (NumberOfVariables - 1));
g = constant * g;
g = g + 1;
return(g);
}
/// <summary>
/// Returns the distance between two solutions in the search space.
/// </summary>
/// <param name="solutionI">The first <code>Solution</code>.</param>
/// <param name="solutionJ">The second <code>Solution</code>.</param>
/// <returns>The distance between solutions.</returns>
public 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);
}
//-> Return the euclidean distance
return(Math.Sqrt(distance));
}
private Solution DoMutation(double probability, Solution parent)
{
Solution current = new Solution(parent);
Solution child;
child = new Solution(current);
XReal xCurrent = new XReal(current);
XReal xChild = new XReal(child);
int numberOfVariables = xCurrent.GetNumberOfDecisionVariables();
randStdNormal = new double[numberOfVariables];
for (int j = 0; j < numberOfVariables; j++)
{
double value;
// value = xParent2.GetValue(j) + f * (xParent0.GetValue(j) - xParent1.GetValue(j));
double u1 = JMetalRandom.NextDouble(0, 1);
double u2 = JMetalRandom.NextDouble(0, 1);
if (JMetalRandom.NextDouble() <= probability)
{
randStdNormal[j] = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2); //random normal(0,1)
value = xCurrent.GetValue(j) + zeta * xCurrent.GetStdDev(j) * randStdNormal[j];
if (value < xChild.GetLowerBound(j))
{
value = xChild.GetLowerBound(j);
//value = JMetalRandom.NextDouble(xChild.GetLowerBound(j), xChild.GetUpperBound(j));
}
if (value > xChild.GetUpperBound(j))
{
value = xChild.GetUpperBound(j);
//value = JMetalRandom.NextDouble(xChild.GetLowerBound(j), xChild.GetUpperBound(j));
}
xChild.SetValue(j, value);
}
}
return(child);
}
/// <summary>
/// Update the position of each particle
/// </summary>
private void ComputeNewPositions()
{
for (int i = 0; i < swarmSize; i++)
{
XReal particle = new XReal(particles.Get(i));
for (int var = 0; var < particle.GetNumberOfDecisionVariables(); var++)
{
particle.SetValue(var, particle.GetValue(var) + speed[i][var]);
if (particle.GetValue(var) < this.Problem.LowerLimit[var])
{
particle.SetValue(var, this.Problem.LowerLimit[var]);
speed[i][var] = speed[i][var] * ChVel1;
}
if (particle.GetValue(var) > this.Problem.UpperLimit[var])
{
particle.SetValue(var, this.Problem.UpperLimit[var]);
speed[i][var] = speed[i][var] * ChVel2;
}
}
}
}
/// <summary>
/// Perform the crossover operation.
/// </summary>
/// <param name="probability">Crossover probability</param>
/// <param name="parent1">The first parent</param>
/// <param name="parent2">The second parent</param>
/// <returns>An array containing the two offsprings</returns>
private Solution[] DoCrossover(double probability, Solution parent1, Solution parent2)
{
Solution[] 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 (JMetalRandom.NextDouble() <= probability)
{
for (i = 0; i < numberOfVariables; i++)
{
valueX1 = x1.GetValue(i);
valueX2 = x2.GetValue(i);
if (JMetalRandom.NextDouble() <= 0.5)
{
if (Math.Abs(valueX1 - valueX2) > EPS)
{
if (valueX1 < valueX2)
{
y1 = valueX1;
y2 = valueX2;
}
else
{
y1 = valueX2;
y2 = valueX1;
}
yL = x1.GetLowerBound(i);
yu = x1.GetUpperBound(i);
rand = JMetalRandom.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 (JMetalRandom.NextDouble() <= 0.5)
{
offs1.SetValue(i, c2);
offs2.SetValue(i, c1);
}
else
{
offs1.SetValue(i, c1);
offs2.SetValue(i, c2);
}
}
else
{
offs1.SetValue(i, valueX1);
offs2.SetValue(i, valueX2);
}
}
else
{
offs1.SetValue(i, valueX2);
offs2.SetValue(i, valueX1);
}
}
}
return(offSpring);
}
private IntergenSolution[] DoCrossover(double probability, IntergenSolution parent1, IntergenSolution parent2)
{
IntergenSolution[] offSpring = new IntergenSolution[2];
offSpring[0] = new IntergenSolution(parent1);
offSpring[1] = new IntergenSolution(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 (JMetalRandom.NextDouble() <= probability)
{
for (i = 0; i < numberOfVariables; i++)
{
valueX1 = x1.GetValue(i);
valueX2 = x2.GetValue(i);
if (JMetalRandom.NextDouble() <= 0.5)
{
if (Math.Abs(valueX1 - valueX2) > EPS)
{
if (valueX1 < valueX2)
{
y1 = valueX1;
y2 = valueX2;
}
else
{
y1 = valueX2;
y2 = valueX1;
}
yL = x1.GetLowerBound(i);
yu = x1.GetUpperBound(i);
rand = JMetalRandom.NextDouble();
beta = 1.0 + (2.0 * (y1 - yL) / (y2 - y1));
//Console.WriteLine("Beta1" + beta);
alpha = 2.0 - Math.Pow(beta, -(distributionIndex + 1.0));
if (rand <= (1.0 / alpha))
{
betaq = Math.Pow((rand * alpha), (1.0 / (distributionIndex + 1.0)));
/* if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* } */
}
else
{
betaq = Math.Pow((1.0 / (2.0 - rand * alpha)), (1.0 / (distributionIndex + 1.0)));
/* var betax = Math.Pow((1.0 / (2.0 - rand * alpha)), (1.0 / (distributionIndex + 1.0)));
* if (double.IsNaN(betax))
* {
* Console.WriteLine("NAN BETAx");
* }
* if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* }
*/
}
/*
* if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* } */
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)));
/*if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* } */
}
else
{
betaq = Math.Pow((1.0 / (2.0 - rand * alpha)), (1.0 / (distributionIndex + 1.0)));
/*if (double.IsNaN(betaq))
* {
* Console.WriteLine("NAN BETAq");
* }*/
}
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 (double.IsNaN(c2) || double.IsNaN(c1))
* {
* Console.WriteLine("Setting NAN");
* } */
if (JMetalRandom.NextDouble() <= 0.5)
{
offs1.SetValue(i, c2);
offs2.SetValue(i, c1);
}
else
{
offs1.SetValue(i, c1);
offs2.SetValue(i, c2);
}
}
else
{
offs1.SetValue(i, valueX1);
offs2.SetValue(i, valueX2);
}
}
else
{
offs1.SetValue(i, valueX2);
offs2.SetValue(i, valueX1);
}
}
}
/*
* var o1 = offSpring[0];
* XReal values = new XReal(o1);
* for (int u = 0; u < values.Size(); u++)
* {
*
* if (double.IsNaN(values.GetValue(u)))
* {
*
* Console.WriteLine("crossover NAN1");
* }
* }
*
* var s = offSpring[1];
* XReal values2 = new XReal(s);
* for (int u = 0; u < values2.Size(); u++)
* {
* if (double.IsNaN(values2.GetValue(u)))
* {
* Console.WriteLine("crossover NAN2");
* }
* } */
return(offSpring);
}
/// <summary>
/// Executes the operation
/// </summary>
/// <param name="obj">An object containing an array of three parents</param>
/// <returns>An object containing the offSprings</returns>
public override object Execute(object obj)
{
object[] parameters = (object[])obj;
Solution current = (Solution)parameters[0];
Solution[] parent = (Solution[])parameters[1];
Solution child;
if (!(VALID_TYPES.Contains(parent[0].Type.GetType()) &&
VALID_TYPES.Contains(parent[1].Type.GetType()) &&
VALID_TYPES.Contains(parent[2].Type.GetType())))
{
Logger.Log.Error("Exception in " + this.GetType().FullName + ".Execute()");
throw new Exception("Exception in " + this.GetType().FullName + ".Execute()");
}
int jrand;
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);
int numberOfVariables = xParent0.GetNumberOfDecisionVariables();
jrand = JMetalRandom.Next(0, numberOfVariables - 1);
// STEP 4. Checking the DE variant
if ((deVariant == "rand/1/bin") || (deVariant == "best/1/bin"))
{
for (int j = 0; j < numberOfVariables; j++)
{
if (JMetalRandom.NextDouble(0, 1) < cr || j == jrand)
{
double value;
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;
value = xCurrent.GetValue(j);
xChild.SetValue(j, value);
}
}
}
else if ((deVariant == "rand/1/exp") || (deVariant == "best/1/exp"))
{
for (int j = 0; j < numberOfVariables; j++)
{
if (JMetalRandom.NextDouble(0, 1) < cr || j == jrand)
{
double value;
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;
double value;
value = xCurrent.GetValue(j);
xChild.SetValue(j, value);
}
}
}
else if ((deVariant == "current-to-rand/1") || (deVariant == "current-to-best/1"))
{
for (int j = 0; j < numberOfVariables; j++)
{
double value;
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 if ((deVariant == "current-to-rand/1/bin") || (deVariant == "current-to-best/1/bin"))
{
for (int j = 0; j < numberOfVariables; j++)
{
if (JMetalRandom.NextDouble(0, 1) < cr || j == jrand)
{
double value;
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
{
double value;
value = xCurrent.GetValue(j);
xChild.SetValue(j, value);
}
}
}
else if ((deVariant == "current-to-rand/1/exp") || (deVariant == "current-to-best/1/exp"))
{
for (int j = 0; j < numberOfVariables; j++)
{
if (JMetalRandom.NextDouble(0, 1) < cr || j == jrand)
{
double value;
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;
value = xCurrent.GetValue(j);
xChild.SetValue(j, value);
}
}
}
else
{
Logger.Log.Error("Exception in " + this.GetType().FullName + ".Execute()");
throw new Exception("Exception in " + this.GetType().FullName + ".Execute()");
}
return(child);
}
/// <summary>
/// Perform the crossover operation.
/// </summary>
/// <param name="probability">Crossover probability</param>
/// <param name="parent1">The first parent</param>
/// <param name="parent2">The second parent</param>
/// <returns></returns>
private Solution[] DoCrossover(double?probability, Solution parent1, Solution parent2)
{
Solution[] offSpring = new Solution[2];
offSpring[0] = new Solution(parent1);
offSpring[1] = new Solution(parent2);
double rand;
double valueY1;
double valueY2;
double valueX1;
double valueX2;
double upperValue;
double lowerValue;
XReal x1 = new XReal(parent1);
XReal x2 = new XReal(parent2);
XReal offs1 = new XReal(offSpring[0]);
XReal offs2 = new XReal(offSpring[1]);
int numberOfVaribales = x1.GetNumberOfDecisionVariables();
if (JMetalRandom.NextDouble() <= probability)
{
for (int i = 0; i < numberOfVaribales; i++)
{
double max;
double min;
double range;
double minRange;
double maxRange;
upperValue = x1.GetUpperBound(i);
lowerValue = x1.GetLowerBound(i);
valueX1 = x1.GetValue(i);
valueX2 = x2.GetValue(i);
if (valueX2 > valueX1)
{
max = valueX2;
min = valueX1;
}
else
{
max = valueX1;
min = valueX2;
}
range = max - min;
minRange = min - range * alpha;
maxRange = max + range * alpha;
rand = JMetalRandom.NextDouble();
valueY1 = minRange + rand * (maxRange - minRange);
rand = JMetalRandom.NextDouble();
valueY2 = minRange + rand * (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);
}
