/// <summary>
/// Computes the HV contribution of the solutions
/// </summary>
/// <param name="solutionSet"></param>
public void ComputeHVContributions(SolutionSet solutionSet)
{
double[] contributions = new double[solutionSet.Size()];
double solutionSetHV = 0;
solutionSetHV = ComputeHypervolume(solutionSet);
for (int i = 0; i < solutionSet.Size(); i++)
{
Solution currentPoint = solutionSet.Get(i);
solutionSet.Remove(i);
if (numberOfObjectives == 2)
{
contributions[i] = solutionSetHV - Get2DHV(solutionSet);
}
else
{
Front front = new Front(solutionSet.Size(), numberOfObjectives, solutionSet);
double hv = new WFGHV(numberOfObjectives, solutionSet.Size(), referencePoint).GetHV(front);
contributions[i] = solutionSetHV - hv;
}
solutionSet.Add(i, currentPoint);
}
for (int i = 0; i < solutionSet.Size(); i++)
{
solutionSet.Get(i).CrowdingDistance = contributions[i];
}
}
/// <summary>
/// Tries to update the reference set 2 with a <code>Solution</code>
/// </summary>
/// <param name="solution">The <code>Solution</code></param>
/// <returns>true if the <code>Solution</code> has been inserted, false
/// otherwise.</returns>
public bool RefSet2Test(Solution solution)
{
double aux;
if (refSet2.Size() < refSet2Size)
{
solution.DistanceToSolutionSet = distance.DistanceToSolutionSetInSolutionSpace(solution, refSet1);
aux = distance.DistanceToSolutionSetInSolutionSpace(solution, refSet2);
if (aux < solution.DistanceToSolutionSet)
{
solution.DistanceToSolutionSet = aux;
}
refSet2.Add(solution);
return(true);
}
solution.DistanceToSolutionSet = distance.DistanceToSolutionSetInSolutionSpace(solution, refSet1);
aux = distance.DistanceToSolutionSetInSolutionSpace(solution, refSet2);
if (aux < solution.DistanceToSolutionSet)
{
solution.DistanceToSolutionSet = aux;
}
double peor = 0.0;
int index = 0;
for (int i = 0; i < refSet2.Size(); i++)
{
aux = refSet2.Get(i).DistanceToSolutionSet;
if (aux > peor)
{
peor = aux;
index = i;
}
}
if (solution.DistanceToSolutionSet < peor)
{
refSet2.Remove(index);
//Update distances in REFSET2
for (int j = 0; j < refSet2.Size(); j++)
{
aux = distance.DistanceBetweenSolutions(refSet2.Get(j), solution);
if (aux < refSet2.Get(j).DistanceToSolutionSet)
{
refSet2.Get(j).DistanceToSolutionSet = aux;
}
}
solution.UnMarked();
refSet2.Add(solution);
return(true);
}
return(false);
}
/// <summary>
/// Tries to update the reference set one with a <code>Solution</code>.
/// </summary>
/// <param name="solution">The <code>Solution</code></param>
/// <returns>true if the <code>Solution</code> has been inserted, false
/// otherwise.</returns>
public bool RefSet1Test(Solution solution)
{
bool dominated = false;
int flag;
int i = 0;
while (i < refSet1.Size())
{
flag = dominance.Compare(solution, refSet1.Get(i));
if (flag == -1)
{ //This is: solution dominates
refSet1.Remove(i);
}
else if (flag == 1)
{
dominated = true;
i++;
}
else
{
flag = equal.Compare(solution, refSet1.Get(i));
if (flag == 0)
{
return(true);
}
i++;
}
}
if (!dominated)
{
solution.UnMarked();
if (refSet1.Size() < refSet1Size)
{ //refSet1 isn't full
refSet1.Add(solution);
}
else
{
archive.Add(solution);
}
}
else
{
return(false);
}
return(true);
}
/// <summary>
/// Implements the referenceSetUpdate method.
/// </summary>
/// <param name="build">build if true, indicates that the reference has to be build for the
/// first time; if false, indicates that the reference set has to be
/// updated with new solutions</param>
public void ReferenceSetUpdate(bool build)
{
if (build)
{ // Build a new reference set
// STEP 1. Select the p best individuals of P, where p is refSet1Size.
// Selection Criterium: Spea2Fitness
Solution individual;
(new Spea2Fitness(solutionSet)).FitnessAssign();
solutionSet.Sort(fitness);
// STEP 2. Build the RefSet1 with these p individuals
for (int i = 0; i < refSet1Size; i++)
{
individual = solutionSet.Get(0);
solutionSet.Remove(0);
individual.UnMarked();
refSet1.Add(individual);
}
// STEP 3. Compute Euclidean distances in SolutionSet to obtain q
// individuals, where q is refSet2Size_
for (int i = 0; i < solutionSet.Size(); i++)
{
individual = solutionSet.Get(i);
individual.DistanceToSolutionSet = distance.DistanceToSolutionSetInSolutionSpace(individual, refSet1);
}
int size = refSet2Size;
if (solutionSet.Size() < refSet2Size)
{
size = solutionSet.Size();
}
// STEP 4. Build the RefSet2 with these q individuals
for (int i = 0; i < size; i++)
{
// Find the maximumMinimunDistanceToPopulation
double maxMinimum = 0.0;
int index = 0;
for (int j = 0; j < solutionSet.Size(); j++)
{
if (solutionSet.Get(j).DistanceToSolutionSet > maxMinimum)
{
maxMinimum = solutionSet.Get(j).DistanceToSolutionSet;
index = j;
}
}
individual = solutionSet.Get(index);
solutionSet.Remove(index);
// Update distances to REFSET in population
for (int j = 0; j < solutionSet.Size(); j++)
{
double aux = distance.DistanceBetweenSolutions(solutionSet.Get(j), individual);
if (aux < individual.DistanceToSolutionSet)
{
solutionSet.Get(j).DistanceToSolutionSet = aux;
}
}
// Insert the individual into REFSET2
refSet2.Add(individual);
// Update distances in REFSET2
for (int j = 0; j < refSet2.Size(); j++)
{
for (int k = 0; k < refSet2.Size(); k++)
{
if (i != j)
{
double aux = distance.DistanceBetweenSolutions(refSet2.Get(j), refSet2.Get(k));
if (aux < refSet2.Get(j).DistanceToSolutionSet)
{
refSet2.Get(j).DistanceToSolutionSet = aux;
}
}
}
}
}
}
else
{ // Update the reference set from the subset generation result
Solution individual;
for (int i = 0; i < subSet.Size(); i++)
{
individual = (Solution)improvementOperator.Execute(subSet.Get(i));
evaluations += improvementOperator.GetEvaluations();
if (RefSet1Test(individual))
{ //Update distance of RefSet2
for (int indSet2 = 0; indSet2 < refSet2.Size(); indSet2++)
{
double aux = distance.DistanceBetweenSolutions(individual, refSet2.Get(indSet2));
if (aux < refSet2.Get(indSet2).DistanceToSolutionSet)
{
refSet2.Get(indSet2).DistanceToSolutionSet = aux;
}
}
}
else
{
RefSet2Test(individual);
}
}
subSet.Clear();
}
}
/**
* Runs of the GDE3 algorithm.
* @return a <code>SolutionSet</code> that is a set of non dominated solutions
* as a result of the algorithm execution
* @throws JMException
*/
public override SolutionSet Execute()
{
int populationSize = -1;
int maxIterations = -1;
int evaluations;
int iterations;
SolutionSet population;
SolutionSet offspringPopulation;
Distance distance;
IComparer <Solution> dominance;
Operator selectionOperator;
Operator crossoverOperator;
distance = new Distance();
dominance = new DominanceComparator();
Solution[] parent;
//Read the parameters
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxIterations", ref maxIterations);
selectionOperator = this.Operators["selection"];
crossoverOperator = this.Operators["crossover"];
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
iterations = 0;
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(this.Problem);
this.Problem.Evaluate(newSolution);
this.Problem.EvaluateConstraints(newSolution);
evaluations++;
population.Add(newSolution);
}
// Generations ...
while (iterations < maxIterations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize * 2);
for (int i = 0; i < populationSize; i++)
{
// Obtain parents. Two parameters are required: the population and the
// index of the current individual
parent = (Solution[])selectionOperator.Execute(new object[] { population, i });
Solution child;
// Crossover. Two parameters are required: the current individual and the
// array of parents
child = (Solution)crossoverOperator.Execute(new object[] { population.Get(i), parent });
this.Problem.Evaluate(child);
this.Problem.EvaluateConstraints(child);
evaluations++;
// Dominance test
int result;
result = dominance.Compare(population.Get(i), child);
if (result == -1)
{ // Solution i dominates child
offspringPopulation.Add(population.Get(i));
}
else if (result == 1)
{ // child dominates
offspringPopulation.Add(child);
}
else
{ // the two solutions are non-dominated
offspringPopulation.Add(child);
offspringPopulation.Add(population.Get(i));
}
}
// Ranking the offspring population
Ranking ranking = new Ranking(offspringPopulation);
int remain = populationSize;
int index = 0;
SolutionSet front = null;
population.Clear();
// Obtain the next front
front = ranking.GetSubfront(index);
while ((remain > 0) && (remain >= front.Size()))
{
//Assign crowding distance to individuals
distance.CrowdingDistanceAssignment(front, this.Problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
} // for
//Decrement remain
remain = remain - front.Size();
//Obtain the next front
index++;
if (remain > 0)
{
front = ranking.GetSubfront(index);
}
}
// remain is less than front(index).size, insert only the best one
if (remain > 0)
{ // front contains individuals to insert
while (front.Size() > remain)
{
distance.CrowdingDistanceAssignment(front, this.Problem.NumberOfObjectives);
front.Remove(front.IndexWorst(new CrowdingComparator()));
}
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
}
remain = 0;
}
iterations++;
}
// Return the first non-dominated front
Ranking rnk = new Ranking(population);
this.Result = rnk.GetSubfront(0);
return(this.Result);
}
/// <summary>
/// Gets 'size' elements from a population of more than 'size' elements
/// using for this de enviromentalSelection truncation
/// </summary>
/// <param name="size">The number of elements to get.</param>
/// <returns></returns>
public SolutionSet EnvironmentalSelection(int size)
{
if (solutionSet.Size() < size)
{
size = solutionSet.Size();
}
// Create a new auxiliar population for no alter the original population
SolutionSet aux = new SolutionSet(solutionSet.Size());
int i = 0;
while (i < solutionSet.Size())
{
if (solutionSet.Get(i).Fitness < 1.0)
{
aux.Add(solutionSet.Get(i));
solutionSet.Remove(i);
}
else
{
i++;
}
}
if (aux.Size() < size)
{
IComparer <Solution> comparator = new FitnessComparator();
solutionSet.Sort(comparator);
int remain = size - aux.Size();
for (i = 0; i < remain; i++)
{
aux.Add(solutionSet.Get(i));
}
return(aux);
}
else if (aux.Size() == size)
{
return(aux);
}
double[][] distanceMatrix = distance.DistanceMatrix(aux);
List <List <DistanceNode> > distanceList = new List <List <DistanceNode> >();
for (int pos = 0; pos < aux.Size(); pos++)
{
aux.Get(pos).Location = pos;
List <DistanceNode> distanceNodeList = new List <DistanceNode>();
for (int refe = 0; refe < aux.Size(); refe++)
{
if (pos != refe)
{
distanceNodeList.Add(new DistanceNode(distanceMatrix[pos][refe], refe));
}
}
distanceList.Add(distanceNodeList);
}
foreach (List <DistanceNode> aDistanceList in distanceList)
{
aDistanceList.Sort(distanceNodeComparator);
}
while (aux.Size() > size)
{
double minDistance = double.MaxValue;
int toRemove = 0;
i = 0;
foreach (List <DistanceNode> dn in distanceList)
{
if (dn[0].Distance < minDistance)
{
toRemove = i;
minDistance = dn[0].Distance;
//i and toRemove have the same distance to the first solution
}
else if (dn[0].Distance == minDistance)
{
int k = 0;
while ((dn[k].Distance == distanceList[toRemove][k].Distance) &&
k < (distanceList[i].Count - 1))
{
k++;
}
if (dn[k].Distance < distanceList[toRemove][k].Distance)
{
toRemove = i;
}
}
i++;
}
int tmp = aux.Get(toRemove).Location;
aux.Remove(toRemove);
distanceList.RemoveAt(toRemove);
foreach (List <DistanceNode> aDistanceList in distanceList)
{
List <DistanceNode> interIterator = aDistanceList.ToList();
foreach (var element in interIterator)
{
if (element.Reference == tmp)
{
aDistanceList.Remove(element);
}
}
}
}
return(aux);
}
