private static double IGD(List <double[]> solution, List <double[]> trueFront)
{
double sum = 0.0;
foreach (double[] v in trueFront)
{
int pos = -1;
double dist = Double.MaxValue;
for (int i = 0; i < solution.Count; i++)
{
double d = QulityIndicatorToolFunction.GetDist(v, solution[i]);
if (d < dist)
{
dist = d;
pos = i;
}
}
sum += dist;
}
return(sum / trueFront.Count);
}
private static double HV(List <double[]> solution, double[] refPoint, double[] miniPoint)
{
int numberOfObjectives = refPoint.Length;
/**
* Stores the maximum values of true pareto front.
*/
double[] maximumValues = refPoint;
/**
* Stores the minimum values of the true pareto front.
*/
double[] minimumValues = miniPoint;
/**
* Stores the normalized front.
*/
List <double[]> normalizedFront;
/**
* Stores the inverted front. Needed for minimization problems
*/
List <double[]> invertedFront;
// STEP 1. Obtain the maximum and minimum values of the Pareto front
//maximumValues = QulityIndicatorToolFunction.getMaximumValues(trueFront, numberOfObjectives);
//minimumValues = QulityIndicatorToolFunction.getMinimumValues(trueFront, numberOfObjectives);
// STEP 2. Get the normalized front
normalizedFront = QulityIndicatorToolFunction.GetNormalizedFront(solution,
maximumValues,
minimumValues);
// STEP 3. Inverse the pareto front. This is needed because of the original
//metric by Zitzler is for maximization problems
invertedFront = QulityIndicatorToolFunction.InvertedFront(normalizedFront);
// STEP4. The hypervolumen (control is passed to java version of Zitzler code)
return(QulityIndicatorToolFunction.CalculateHypervolume(invertedFront, invertedFront.Count, numberOfObjectives));
}
