public void CrowdingTestSinglePoint()
{
double[] point = new double[2];
point[0] = 0;
point[1] = 0;
double[][] front = { point };
double dist = Crowding.Calculate(front, new double[, ] {
{ 0, 1 }, { 0, 1 }
});
Assert.AreEqual(double.PositiveInfinity, dist);
}
private void Analyze()
{
ResultsScatterPlot = new ParetoFrontScatterPlot(solutions.Select(x => x.Fitness).ToArray(), solutions.Select(x => x.Mean.ToArray()).ToArray(), ResultsScatterPlot.ParetoFront, ResultsScatterPlot.Objectives, ResultsScatterPlot.ProblemSize);
ResultsSolutions = solutions.Select(x => x.Mean.ToArray()).ToMatrix();
var problem = Problem as MultiObjectiveTestFunctionProblem;
if (problem == null)
{
return;
}
var front = NonDominatedSelect.GetDominatingVectors(solutions.Select(x => x.Fitness), problem.ReferencePoint.CloneAsArray(), Problem.Maximization, true).ToArray();
if (front.Length == 0)
{
return;
}
var bounds = problem.Bounds.CloneAsMatrix();
ResultsCrowding = Crowding.Calculate(front, bounds);
ResultsSpacing = Spacing.Calculate(front);
ResultsGenerationalDistance = problem.BestKnownFront != null?GenerationalDistance.Calculate(front, problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN;
ResultsInvertedGenerationalDistance = problem.BestKnownFront != null?InvertedGenerationalDistance.Calculate(front, problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN;
ResultsHypervolume = Hypervolume.Calculate(front, problem.ReferencePoint.CloneAsArray(), Problem.Maximization);
ResultsBestHypervolume = Math.Max(ResultsHypervolume, ResultsBestHypervolume);
ResultsDifferenceBestKnownHypervolume = ResultsBestKnownHypervolume - ResultsBestHypervolume;
ResultsBestHypervolumeDataLine.Values.Add(ResultsBestHypervolume);
ResultsHypervolumeDataLine.Values.Add(ResultsHypervolume);
ResultsCrowdingDataLine.Values.Add(ResultsCrowding);
ResultsGenerationalDistanceDataLine.Values.Add(ResultsGenerationalDistance);
ResultsInvertedGenerationalDistanceDataLine.Values.Add(ResultsInvertedGenerationalDistance);
ResultsSpacingDataLine.Values.Add(ResultsSpacing);
ResultsHypervolumeDifferenceDataLine.Values.Add(ResultsDifferenceBestKnownHypervolume);
Problem.Analyze(
solutions.Select(x => (Optimization.Individual) new SingleEncodingIndividual(Problem.Encoding, new Scope {
Variables = { new Variable(Problem.Encoding.Name, x.Mean) }
})).ToArray(),
solutions.Select(x => x.Fitness).ToArray(),
Results,
random);
}
public void CrowdingTestDiagonal()
{
double[] point = new double[2];
point[0] = 0;
point[1] = 0;
double[] point1 = new double[2];
point1[0] = 0.5;
point1[1] = 0.5;
double[] point2 = new double[2];
point2[0] = 1;
point2[1] = 1;
double[][] front = { point, point1, point2 };
double dist = Crowding.Calculate(front, new double[, ] {
{ 0, 1 }, { 0, 1 }
});
Assert.AreEqual(2, dist);
}
public void CrowdingTestDiamond()
{
double[] point = new double[2];
point[0] = 0;
point[1] = 0;
double[] point1 = new double[2];
point1[0] = 1;
point1[1] = 1.5;
double[] point2 = new double[2];
point2[0] = 3;
point2[1] = 0.5;
double[] point3 = new double[2];
point3[0] = 4;
point3[1] = 2;
double[][] front = { point, point1, point2, point3 };
double dist = Crowding.Calculate(front, new double[, ] {
{ 0, 4 }, { 0, 2 }
});
Assert.AreEqual(1.5, dist);
}
public void CrowdingTestDeltoid()
{
double[] point = new double[2];
point[0] = 0;
point[1] = 0;
double[] point1 = new double[2];
point1[0] = 0.00000001; //points should not be exactly equal because sorting behaviour could change result
point1[1] = 1.00000001;
double[] point2 = new double[2];
point2[0] = 1;
point2[1] = 0;
double[] point3 = new double[2];
point3[0] = 4;
point3[1] = 4;
double[][] front = { point, point1, point2, point3, };
double dist = Crowding.Calculate(front, new double[, ] {
{ 0, 4 }, { 0, 4 }
});
Assert.AreEqual(1.25, dist);
}
public void CrowdingTestEmptyFront()
{
double[][] front = { };
Crowding.Calculate(front, null);
}
