/// <summary>
/// Perform the mutation operation
/// </summary>
/// <param name="probability">Mutation probability</param>
/// <param name="solution">The solution to mutate</param>
private void DoMutation(double probability, Solution solution)
{
XReal x = new XReal(solution);
for (int var = 0; var < solution.Variable.Length; var++)
{
if (JMetalRandom.NextDouble() < probability)
{
double rand = JMetalRandom.NextDouble();
double tmp;
if (rand <= 0.5)
{
tmp = Delta(x.GetUpperBound(var) - x.GetValue(var), perturbation.Value);
tmp += x.GetValue(var);
}
else
{
tmp = Delta(x.GetLowerBound(var) - x.GetValue(var), perturbation.Value);
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);
}
}
}
/// <summary>
/// Performs the operation
/// </summary>
/// <param name="obj">Object representing a SolutionSet. This solution set must be an instancen <code>AdaptiveGridArchive</code></param>
/// <returns>the selected solution</returns>
public override object Execute(object obj)
{
try
{
AdaptiveGridArchive archive = (AdaptiveGridArchive)obj;
int selected;
int hypercube1 = archive.Grid.RandomOccupiedHypercube();
int hypercube2 = archive.Grid.RandomOccupiedHypercube();
if (hypercube1 != hypercube2)
{
if (archive.Grid.GetLocationDensity(hypercube1) < archive.Grid.GetLocationDensity(hypercube2))
{
selected = hypercube1;
}
else if (archive.Grid.GetLocationDensity(hypercube2) <
archive.Grid.GetLocationDensity(hypercube1))
{
selected = hypercube2;
}
else
{
if (JMetalRandom.NextDouble() < 0.5)
{
selected = hypercube2;
}
else
{
selected = hypercube1;
}
}
}
else
{
selected = hypercube1;
}
int bas = JMetalRandom.Next(0, archive.Size() - 1);
int cnt = 0;
while (cnt < archive.Size())
{
Solution individual = archive.Get((bas + cnt) % archive.Size());
if (archive.Grid.Location(individual) != selected)
{
cnt++;
}
else
{
return(individual);
}
}
return(archive.Get((bas + cnt) % archive.Size()));
}
catch (InvalidCastException ex)
{
Logger.Log.Error("Exception in " + this.GetType().FullName + ".Execute()", ex);
throw new Exception("Exception in " + this.GetType().FullName + ".Execute()");
}
}
//double[] pro = new double[2];
/// <summary>
///
/// </summary>
/// <param name="cid">the id of current subproblem</param>
/// <param name="type">1 - minimum; otherwise - whole neighborhood probability</param>
public int ACOrSelection(int cid, int type)
{
int ss;
ss = neighborhood[cid].Length;
double r;
int p = neighborhood[cid][0];
Solution[] parents = new Solution[ss];
double[] fitness = new double[ss];
int indexOfmin = 0;
double sum = 0;
double[] pro = new double[ss];
double a1 = 0;
double a2 = 0;
if (type == 1)
{
for (int i = 0; i < ss; i++)
{
parents[i] = population.Get(neighborhood[cid][i]);
fitness[i] = FitnessFunction(parents[i], lambda[cid]);
if (fitness[i] < FitnessFunction(population.Get(p), lambda[cid]))
{
indexOfmin = i;
}
p = neighborhood[cid][indexOfmin];
}
}
else
{
for (int i = 0; i < ss; i++)
{
parents[i] = population.Get(neighborhood[cid][i]);
fitness[i] = 1 / FitnessFunction(parents[i], lambda[cid]);
sum = sum + fitness[i];
}
for (int j = 0; j < ss; j++)
{
pro[j] = fitness[j] / sum;
}
r = JMetalRandom.NextDouble();
for (int k = 0; k < pro.Length; k++)
{
a2 = a2 + pro[k];
if (r < a2 && r >= a1)
{
p = neighborhood[cid][k];
break;
}
a1 = a1 + pro[k];
}
}
return(p);
}
/// <summary>
/// Calculates the delta value used in NonUniform mutation operator
/// </summary>
/// <param name="y"></param>
/// <param name="bMutationParameter"></param>
/// <returns></returns>
private double Delta(double y, double bMutationParameter)
{
double rand = JMetalRandom.NextDouble();
int it, maxIt;
it = currentIteration.Value;
maxIt = maxIterations;
return(y * (1.0 - Math.Pow(rand, Math.Pow((1.0 - it / (double)maxIt), bMutationParameter))));
}
/// <summary>
/// DoMutation method
/// </summary>
/// <param name="realProbability"></param>
/// <param name="binaryProbability"></param>
/// <param name="solution"></param>
private void DoMutation(double realProbability, double binaryProbability, Solution solution)
{
double rnd, delta1, delta2, mut_pow, deltaq;
double y, yl, yu, val, xy;
XReal x = new XReal(solution);
Binary binaryVariable = (Binary)solution.Variable[1];
// Polynomial mutation applied to the array real
for (int var = 0; var < x.Size(); var++)
{
if (JMetalRandom.NextDouble() <= realProbability)
{
y = x.GetValue(var);
yl = x.GetLowerBound(var);
yu = x.GetUpperBound(var);
delta1 = (y - yl) / (yu - yl);
delta2 = (yu - y) / (yu - yl);
rnd = JMetalRandom.NextDouble();
mut_pow = 1.0 / (eta_m + 1.0);
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, mut_pow) - 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, mut_pow));
}
y = y + deltaq * (yu - yl);
if (y < yl)
{
y = yl;
}
if (y > yu)
{
y = yu;
}
x.SetValue(var, y);
}
}
// BitFlip mutation applied to the binary part
for (int i = 0; i < binaryVariable.NumberOfBits; i++)
{
if (JMetalRandom.NextDouble() < binaryProbability)
{
binaryVariable.Bits.Flip(i);
}
}
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="size">Size of the array</param>
/// <param name="problem"></param>
public ArrayReal(int size, Problem problem)
{
this.problem = problem;
Size = size;
Array = new double[Size];
for (int i = 0; i < Size; i++)
{
double val = JMetalRandom.NextDouble() * (this.problem.UpperLimit[i] - this.problem.LowerLimit[i]) + this.problem.LowerLimit[i];
Array[i] = val;
}
}
/// <summary>
///
/// </summary>
/// <param name="list">the set of the indexes of selected mating parents</param>
/// <param name="cid">the id of current subproblem</param>
/// <param name="type">1 - neighborhood; otherwise - whole population</param>
public int ACOrSelection2(int cid, int type, double[] pro)
{
double r;
int b = neighborhood[cid][0];
Solution[] parents = new Solution[t];
Solution[] parent = new Solution[populationSize];
double[] fitness = new double[t];
double[] fit = new double[populationSize];
Dictionary <int, double> tmp = new Dictionary <int, double>();
Dictionary <int, double> resultSort = new Dictionary <int, double>();
double a1 = 0;
double a2 = 0;
r = JMetalRandom.NextDouble();
for (int k = 0; k < pro.Length; k++)
{
a2 = a2 + pro[k];
if (r < a2 && r >= a1)
{
b = k;
break;
}
a1 = a1 + pro[k];
}
if (type == 1)
{
for (int i = 0; i < t; i++)
{
parents[i] = population.Get(neighborhood[cid][i]);
fitness[i] = FitnessFunction(parents[i], lambda[cid]);
tmp.Add(neighborhood[cid][i], fitness[i]);
}
}
else
{
for (int i = 0; i < populationSize; i++)
{
parent[i] = population.Get(i);
fit[i] = FitnessFunction(parent[i], lambda[cid]);
tmp.Add(i, fit[i]);
}
}
resultSort = tmp.OrderBy(Data => Data.Value).ToDictionary(keyvalue => keyvalue.Key, keyvalue => keyvalue.Value);
int[] person = resultSort.Keys.ToArray();
return(person[b]);
}
/// <summary>
/// Perform the mutation operation
/// </summary>
/// <param name="probability">Mutation probability</param>
/// <param name="solution">The solution to mutate</param>
private void DoMutation(Solution solution)
{
double rnd, delta1, delta2, mut_pow, deltaq;
double y, yl, yu, val, xy;
mutationProbability = mutationProbability - solution.NumberofReplace * 0.001;
if (mutationProbability <= 0.1)
{
mutationProbability = 0.1;
}
XReal x = new XReal(solution);
for (int var = 0; var < solution.NumberOfVariables(); var++)
{
if (JMetalRandom.NextDouble() <= mutationProbability)
{
y = x.GetValue(var);
yl = x.GetLowerBound(var);
yu = x.GetUpperBound(var);
delta1 = (y - yl) / (yu - yl);
delta2 = (yu - y) / (yu - yl);
rnd = JMetalRandom.NextDouble();
mut_pow = 1.0 / (eta_m + 1.0);
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, mut_pow) - 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, mut_pow));
}
y = y + deltaq * (yu - yl);
if (y < yl)
{
y = yl;
}
if (y > yu)
{
y = yu;
}
x.SetValue(var, y);
}
}
}
/// <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 a <code>Solution</code> using the diversification generation method
/// described in the scatter search template.
/// </summary>
/// <returns></returns>
public Solution DiversificationGeneration()
{
Solution solution;
solution = new Solution(this.Problem);
XReal wrapperSolution = new XReal(solution);
double value;
int range;
for (int i = 0; i < this.Problem.NumberOfVariables; i++)
{
sumOfReverseFrequencyValues[i] = 0;
for (int j = 0; j < numberOfSubranges; j++)
{
reverseFrequency[j][i] = sumOfFrequencyValues[i] - frequency[j][i];
sumOfReverseFrequencyValues[i] += reverseFrequency[j][i];
}
if (sumOfReverseFrequencyValues[i] == 0)
{
range = JMetalRandom.Next(0, numberOfSubranges - 1);
}
else
{
value = JMetalRandom.Next(0, sumOfReverseFrequencyValues[i] - 1);
range = 0;
while (value > reverseFrequency[range][i])
{
value -= reverseFrequency[range][i];
range++;
}
}
frequency[range][i]++;
sumOfFrequencyValues[i]++;
double low = this.Problem.LowerLimit[i] + range * (this.Problem.UpperLimit[i] -
this.Problem.LowerLimit[i]) / numberOfSubranges;
double high = low + (this.Problem.UpperLimit[i] -
this.Problem.LowerLimit[i]) / numberOfSubranges;
value = JMetalRandom.NextDouble(low, high);
wrapperSolution.SetValue(i, value);
}
return(solution);
}
/// <summary>
/// Perform the mutation operation
/// </summary>
/// <param name="probability">Mutation probability</param>
/// <param name="solution">The solution to mutate</param>
private void DoMutation(double probability, Solution solution)
{
XReal v = new XReal(solution);
try
{
if ((solution.Type.GetType() == typeof(BinarySolutionType)) ||
(solution.Type.GetType() == typeof(BinaryRealSolutionType)))
{
for (int i = 0; i < solution.Variable.Length; i++)
{
for (int j = 0; j < ((Binary)solution.Variable[i]).NumberOfBits; j++)
{
if (JMetalRandom.NextDouble() < probability)
{
((Binary)solution.Variable[i]).Bits.Flip(j);
}
}
}
for (int i = 0; i < solution.Variable.Length; i++)
{
((Binary)solution.Variable[i]).Decode();
}
}
else
{ // Integer representation
for (int i = 0; i < solution.Variable.Length; i++)
{
if (JMetalRandom.NextDouble() < probability)
{
int value = JMetalRandom.Next(
(int)v.GetLowerBound(i),
(int)v.GetUpperBound(i));
v.SetValue(i, value);
}
}
}
}
catch (Exception ex)
{
Logger.Log.Error("Error in " + this.GetType().FullName + ".DoMutation()", ex);
Console.WriteLine("Error in " + this.GetType().FullName + ".DoMutation()");
throw new Exception("Exception in " + this.GetType().FullName + ".DoMutation()");
}
}
/// <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>
public Solution[] DoCrossover(double?probability, Solution parent1, Solution parent2)
{
Solution[] offSpring = new Solution[2];
offSpring[0] = new Solution(parent1);
offSpring[1] = new Solution(parent2);
try
{
if (JMetalRandom.NextDouble() < probability)
{
for (int i = 0; i < parent1.Variable.Length; i++)
{
Binary p1 = (Binary)parent1.Variable[i];
Binary p2 = (Binary)parent2.Variable[i];
for (int bit = 0; bit < p1.NumberOfBits; bit++)
{
if (p1.Bits[bit] != p2.Bits[bit])
{
if (JMetalRandom.NextDouble() < 0.5)
{
((Binary)offSpring[0].Variable[i])
.Bits[bit] = p2.Bits[bit];
((Binary)offSpring[1].Variable[i])
.Bits[bit] = p1.Bits[bit];
}
}
}
}
//7. Decode the results
for (int i = 0; i < offSpring[0].Variable.Length; i++)
{
((Binary)offSpring[0].Variable[i]).Decode();
((Binary)offSpring[1].Variable[i]).Decode();
}
}
}
catch (InvalidCastException ex)
{
Logger.Log.Error("Error in " + this.GetType().FullName + ".DoCrossover()", ex);
Console.WriteLine("Error in " + this.GetType().FullName + ".DoCrossover()");
throw new Exception("Exception in " + this.GetType().FullName + ".DoCrossover()");
}
return(offSpring);
}
/// <summary>
/// This method executes the operator represented by the current object.
/// </summary>
/// <param name="obj">A Solution object.</param>
public override object Execute(object obj)
{
var solutionSet = obj as SolutionSet;
var solutions = solutionSet.SolutionsList;
var bestPerformance = solutions.OrderBy(s => s.Objective [0]).Last();
var bestArea = solutions.OrderBy(s => s.Objective [1]).First();
if (JMetalRandom.NextDouble() > 0.5)
{
return(DeepCopy(bestPerformance));
}
else
{
return(DeepCopy(bestArea));
}
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="numberOfBits">Length of the bit string</param>
public Binary(int numberOfBits)
{
this.NumberOfBits = numberOfBits;
this.Bits = new BitSet(numberOfBits);
for (int i = 0; i < numberOfBits; i++)
{
if (JMetalRandom.NextDouble() < 0.5)
{
this.Bits[i] = true;
}
else
{
this.Bits[i] = false;
}
}
}
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>
/// Performs the operation
/// </summary>
/// <param name="obj">Object representing a SolutionSet</param>
/// <returns>The selected solution</returns>
public override object Execute(object obj)
{
SolutionSet population = (SolutionSet)obj;
if (index == 0) //Create the permutation
{
a = (new PermutationUtility()).IntPermutation(population.Size());
}
Solution solution1, solution2;
solution1 = population.Get(a[index]);
solution2 = population.Get(a[index + 1]);
index = (index + 2) % population.Size();
int flag = dominance.Compare(solution1, solution2);
if (flag == -1)
{
return(solution1);
}
else if (flag == 1)
{
return(solution2);
}
else if (solution1.CrowdingDistance > solution2.CrowdingDistance)
{
return(solution1);
}
else if (solution2.CrowdingDistance > solution1.CrowdingDistance)
{
return(solution2);
}
else if (JMetalRandom.NextDouble() < 0.5)
{
return(solution1);
}
else
{
return(solution2);
}
}
/// <summary>
/// Performs the operation
/// </summary>
/// <param name="probability">Mutation probability</param>
/// <param name="solution">The solution to mutate</param>
private void DoMutation(double probability, Solution solution)
{
int[] permutation;
int permutationLength;
if (solution.Type.GetType() == typeof(PermutationSolutionType))
{
permutationLength = ((Permutation)solution.Variable[0]).Size;
permutation = ((Permutation)solution.Variable[0]).Vector;
if (JMetalRandom.NextDouble() < probability)
{
int pos1;
int pos2;
pos1 = JMetalRandom.Next(0, permutationLength - 1);
pos2 = JMetalRandom.Next(0, permutationLength - 1);
while (pos1 == pos2)
{
if (pos1 == (permutationLength - 1))
{
pos2 = JMetalRandom.Next(0, permutationLength - 2);
}
else
{
pos2 = JMetalRandom.Next(pos1, permutationLength - 1);
}
}
// swap
int temp = permutation[pos1];
permutation[pos1] = permutation[pos2];
permutation[pos2] = temp;
}
}
else
{
Logger.Log.Error("Exception in " + this.GetType().FullName + ".DoMutation()");
Console.WriteLine("Exception in " + this.GetType().FullName + ".DoMutation()");
throw new Exception("Exception in " + this.GetType().FullName + ".DoMutation()");
}
}
private List <int> TourSelection(int depth)
{
// selection based on utility
List <int> selected = new List <int>();
List <int> candidate = new List <int>();
for (int k = 0; k < Problem.NumberOfObjectives; k++)
{
selected.Add(k); // WARNING! HERE YOU HAVE TO USE THE WEIGHT PROVIDED BY QINGFU (NOT SORTED!!!!)
}
for (int n = Problem.NumberOfObjectives; n < populationSize; n++)
{
candidate.Add(n); // set of unselected weights
}
while (selected.Count < (int)(populationSize / 5.0))
{
int bestIdd = (int)(JMetalRandom.NextDouble() * candidate.Count);
int i2;
int bestSub = candidate[bestIdd];
int s2;
for (int i = 1; i < depth; i++)
{
i2 = (int)(JMetalRandom.NextDouble() * candidate.Count);
s2 = candidate[i2];
if (utility[s2] > utility[bestSub])
{
bestIdd = i2;
bestSub = s2;
}
}
selected.Add(bestSub);
candidate.Remove(bestIdd);
}
return(selected);
}
/// <summary>
/// Performs the operation
/// </summary>
/// <param name="obj">Object representing a SolutionSet</param>
/// <returns>The selected solution</returns>
public override object Execute(object obj)
{
SolutionSet solutionSet = (SolutionSet)obj;
Solution solution1, solution2;
solution1 = solutionSet.Get(JMetalRandom.Next(0, solutionSet.Size() - 1));
solution2 = solutionSet.Get(JMetalRandom.Next(0, solutionSet.Size() - 1));
if (solutionSet.Size() >= 2)
{
while (solution1 == solution2)
{
solution2 = solutionSet.Get(JMetalRandom.Next(0, solutionSet.Size() - 1));
}
}
int flag = comparator.Compare(solution1, solution2);
if (flag == -1)
{
return(solution1);
}
else if (flag == 1)
{
return(solution2);
}
else
if (JMetalRandom.NextDouble() < 0.5)
{
return(solution1);
}
else
{
return(solution2);
}
}
public override SolutionSet Execute()
{
QualityIndicator.QualityIndicator indicators = null; // QualityIndicator object
int requiredEvaluations = 0; // Use in the example of use of the
// indicators object (see below)
evaluations = 0;
iteration = 0;
JMetalCSharp.Utils.Utils.GetDoubleValueFromParameter(this.InputParameters, "q", ref q);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "iterationsNumber", ref iterationsNumber);
JMetalCSharp.Utils.Utils.GetStringValueFromParameter(this.InputParameters, "dataDirectory", ref dataDirectory);
JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators);
Logger.Log.Info("POPSIZE: " + populationSize);
Console.WriteLine("POPSIZE: " + populationSize);
population = new SolutionSet(populationSize);
indArray = new Solution[Problem.NumberOfObjectives];
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "T", ref t);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "nr", ref nr);
JMetalCSharp.Utils.Utils.GetDoubleValueFromParameter(this.InputParameters, "delta", ref delta);
neighborhood = new int[populationSize][];
for (int i = 0; i < populationSize; i++)
{
neighborhood[i] = new int[t];
}
z = new double[Problem.NumberOfObjectives];
//znad = new double[Problem.NumberOfObjectives];
lambda = new double[populationSize][];
for (int i = 0; i < populationSize; i++)
{
lambda[i] = new double[Problem.NumberOfObjectives];
}
crossover = this.Operators["crossover"];
mutation = this.Operators["mutation"];
double[] pro_T = new double[t];
double[] pro_A = new double[populationSize];
pro_T = GerPro(t);
pro_A = GerPro(populationSize);
/*string dir = "Result/MOEAD_ACOR/ZDT4_Real/Record/Replace/3nd_Dynamic2_nr16";
* if (Directory.Exists(dir))
* {
* Console.WriteLine("The directory {0} already exists.", dir);
* }
* else
* {
* Directory.CreateDirectory(dir);
* Console.WriteLine("The directory {0} was created.", dir);
* }*/
//Step 1. Initialization
//Step 1.1 Compute euclidean distances between weight vectors and find T
InitUniformWeight();
InitNeighborhood();
//Step 1.2 Initialize population
InitPoputalion();
//Step 1.3 Initizlize z
InitIdealPoint();
//Step 2 Update
do
{
int[] permutation = new int[populationSize];
Utils.RandomPermutation(permutation, populationSize);
if (crossover.ToString() == "JMetalCSharp.Operators.Crossover.DifferentialEvolutionCrossover")
{
for (int i = 0; i < populationSize; i++)
{
int n = permutation[i]; // or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta) // if (rnd < realb)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
MatingSelection(p, n, 2, type);
// STEP 2.2. Reproduction
Solution child;
Solution[] parents = new Solution[3];
parents[0] = population.Get(p[0]);
parents[1] = population.Get(p[1]);
parents[2] = population.Get(n);
// Apply DE crossover
child = (Solution)crossover.Execute(new object[] { population.Get(n), parents });
// Apply mutation
mutation.Execute(child);
// Evaluation
Problem.Evaluate(child);
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(child);
// STEP 2.5. Update of solutions
UpdateProblem(child, n, type);
}
}
else if (crossover.ToString() == "JMetalCSharp.Operators.Crossover.ACOR")
{
Solution[] parents = new Solution[2];
int t = 0;
for (int i = 0; i < populationSize; i++)
{
int n = permutation[i];
// or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. ACOR selection based on probability
if (rnd < delta) // if (rnd < realb)
{
type = 1; // minmum
//parents[0] = population.Get(ACOrSelection2(n, type, pro_T));
}
else
{
type = 2; // whole neighborhood probability
//parents[0] = population.Get(ACOrSelection2(n, type, pro_A));
}
GetStdDev(neighborhood);
//GetStdDev1(neighborhood, type);
//List<int> p = new List<int>();
//MatingSelection(p, n, 1, type);
// STEP 2.2. Reproduction
Solution child;
parents[0] = population.Get(ACOrSelection(n, type));
parents[1] = population.Get(n);
//parents[0] = population.Get(p[0]);
// Apply ACOR crossover
child = (Solution)crossover.Execute(parents);
child.NumberofReplace = t;
// // Apply mutation
mutation.Execute(child);
// Evaluation
Problem.Evaluate(child);
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(child);
// STEP 2.5. Update of solutions
t = UpdateProblemWithReplace(child, n, 1);
}
}
else
{
// Create the offSpring solutionSet
SolutionSet offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
int n = permutation[i]; // or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta) // if (rnd < realb)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
MatingSelection(p, n, 2, type);
parents[0] = population.Get(p[0]);
parents[1] = population.Get(p[1]);
if (iteration < iterationsNumber)
{
//obtain parents
Solution[] offSpring = (Solution[])crossover.Execute(parents);
//Solution child;
mutation.Execute(offSpring[0]);
mutation.Execute(offSpring[1]);
/*if(rnd < 0.5)
* {
* child = offSpring[0];
* }
* else
* {
* child = offSpring[1];
* }*/
Problem.Evaluate(offSpring[0]);
Problem.Evaluate(offSpring[1]);
Problem.EvaluateConstraints(offSpring[0]);
Problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(offSpring[0]);
UpdateReference(offSpring[1]);
// STEP 2.5. Update of solutions
UpdateProblem(offSpring[0], n, type);
UpdateProblem(offSpring[1], n, type);
}
}
}
/*string filevar = dir + "/VAR" + iteration;
* string filefun = dir + "/FUN" + iteration;
* population.PrintVariablesToFile(filevar);
* population.PrintObjectivesToFile(filefun);*/
iteration++;
if ((indicators != null) && (requiredEvaluations == 0))
{
double HV = indicators.GetHypervolume(population);
if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume))
{
requiredEvaluations = evaluations;
}
}
} while (iteration < iterationsNumber);
Logger.Log.Info("ITERATION: " + iteration);
Console.WriteLine("ITERATION: " + iteration);
// Return as output parameter the required evaluations
SetOutputParameter("evaluations", requiredEvaluations);
Result = population;
//return population;
// Return the first non-dominated front
Ranking rank = new Ranking(population);
//Result = rank.GetSubfront(0);
return(Result);
}
public override SolutionSet Execute()
{
double contrDE = 0;
double contrSBX = 0;
double contrPol = 0;
double contrTotalDE = 0;
double contrTotalSBX = 0;
double contrTotalPol = 0;
double[] contrReal = new double[3];
contrReal[0] = contrReal[1] = contrReal[2] = 0;
IComparer <Solution> dominance = new DominanceComparator();
IComparer <Solution> crowdingComparator = new CrowdingComparator();
Distance distance = new Distance();
Operator selectionOperator;
//Read parameter values
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
//Init the variables
population = new SolutionSet(populationSize);
evaluations = 0;
selectionOperator = Operators["selection"];
Offspring[] getOffspring;
int N_O; // number of offpring objects
getOffspring = ((Offspring[])GetInputParameter("offspringsCreators"));
N_O = getOffspring.Length;
contribution = new double[N_O];
contributionCounter = new int[N_O];
contribution[0] = (double)(populationSize / (double)N_O) / (double)populationSize;
for (int i = 1; i < N_O; i++)
{
contribution[i] = (double)(populationSize / (double)N_O) / (double)populationSize + (double)contribution[i - 1];
}
for (int i = 0; i < N_O; i++)
{
Console.WriteLine(getOffspring[i].Configuration());
Console.WriteLine("Contribution: " + contribution[i]);
}
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
newSolution.Location = i;
population.Add(newSolution);
}
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 1); i++)
{
if (evaluations < maxEvaluations)
{
Solution individual = new Solution(population.Get(JMetalRandom.Next(0, populationSize - 1)));
int selected = 0;
bool found = false;
Solution offSpring = null;
double rnd = JMetalRandom.NextDouble();
for (selected = 0; selected < N_O; selected++)
{
if (!found && (rnd <= contribution[selected]))
{
if ("DE" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population, i);
contrDE++;
}
else if ("SBXCrossover" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population);
contrSBX++;
}
else if ("PolynomialMutation" == getOffspring[selected].Id)
{
offSpring = ((PolynomialMutationOffspring)getOffspring[selected]).GetOffspring(individual);
contrPol++;
}
else
{
Logger.Log.Error("Error in NSGAIIAdaptive. Operator " + offSpring + " does not exist");
Console.WriteLine("Error in NSGAIIAdaptive. Operator " + offSpring + " does not exist");
}
offSpring.Fitness = (int)selected;
found = true;
}
}
Problem.Evaluate(offSpring);
offspringPopulation.Add(offSpring);
evaluations += 1;
}
}
// Create the solutionSet union of solutionSet and offSpring
union = ((SolutionSet)population).Union(offspringPopulation);
// Ranking the union
Ranking ranking = new Ranking(union);
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, Problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
}
//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
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
front.Sort(new CrowdingComparator());
for (int k = 0; k < remain; k++)
{
population.Add(front.Get(k));
}
remain = 0;
}
// CONTRIBUTION CALCULATING PHASE
// First: reset contribution counter
for (int i = 0; i < N_O; i++)
{
contributionCounter[i] = 0;
}
// Second: determine the contribution of each operator
for (int i = 0; i < population.Size(); i++)
{
if ((int)population.Get(i).Fitness != -1)
{
contributionCounter[(int)population.Get(i).Fitness] += 1;
}
population.Get(i).Fitness = -1;
}
contrTotalDE += contributionCounter[0];
contrTotalSBX += contributionCounter[1];
contrTotalPol += contributionCounter[2];
int minimumContribution = 2;
int totalContributionCounter = 0;
for (int i = 0; i < N_O; i++)
{
if (contributionCounter[i] < minimumContribution)
{
contributionCounter[i] = minimumContribution;
}
totalContributionCounter += contributionCounter[i];
}
if (totalContributionCounter == 0)
{
for (int i = 0; i < N_O; i++)
{
contributionCounter[i] = 10;
}
}
// Third: calculating contribution
contribution[0] = contributionCounter[0] * 1.0
/ (double)totalContributionCounter;
for (int i = 1; i < N_O; i++)
{
contribution[i] = contribution[i - 1] + 1.0 * contributionCounter[i] / (double)totalContributionCounter;
}
}
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}
/// <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 containig 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);
try
{
if (JMetalRandom.NextDouble() < probability)
{
if ((parent1.Type.GetType() == typeof(BinarySolutionType)) ||
(parent1.Type.GetType() == typeof(BinaryRealSolutionType)))
{
//1. Compute the total number of bits
int totalNumberOfBits = 0;
for (int i = 0; i < parent1.Variable.Length; i++)
{
totalNumberOfBits += ((Binary)parent1.Variable[i]).NumberOfBits;
}
//2. Calculate the point to make the crossover
int crossoverPoint = JMetalRandom.Next(0, totalNumberOfBits - 1);
//3. Compute the encodings.variable containing the crossoverPoint bit
int variable = 0;
int acountBits = ((Binary)parent1.Variable[variable]).NumberOfBits;
while (acountBits < (crossoverPoint + 1))
{
variable++;
acountBits += ((Binary)parent1.Variable[variable]).NumberOfBits;
}
//4. Compute the bit into the selected encodings.variable
int diff = acountBits - crossoverPoint;
int intoVariableCrossoverPoint = ((Binary)parent1.Variable[variable]).NumberOfBits - diff;
//5. Make the crossover into the gene;
Binary offSpring1, offSpring2;
offSpring1 = (Binary)parent1.Variable[variable].DeepCopy();
offSpring2 = (Binary)parent2.Variable[variable].DeepCopy();
for (int i = intoVariableCrossoverPoint; i < offSpring1.NumberOfBits; i++)
{
bool swap = offSpring1.Bits[i];
offSpring1.Bits[i] = offSpring2.Bits[i];
offSpring2.Bits[i] = swap;
}
offSpring[0].Variable[variable] = offSpring1;
offSpring[1].Variable[variable] = offSpring2;
//6. Apply the crossover to the other variables
for (int i = 0; i < variable; i++)
{
offSpring[0].Variable[i] = parent2.Variable[i].DeepCopy();
offSpring[1].Variable[i] = parent1.Variable[i].DeepCopy();
}
//7. Decode the results
for (int i = 0; i < offSpring[0].Variable.Length; i++)
{
((Binary)offSpring[0].Variable[i]).Decode();
((Binary)offSpring[1].Variable[i]).Decode();
}
} // Binary or BinaryReal
else
{ // Integer representation
int crossoverPoint = JMetalRandom.Next(0, parent1.NumberOfVariables() - 1);
int valueX1;
int valueX2;
for (int i = crossoverPoint; i < parent1.NumberOfVariables(); i++)
{
valueX1 = ((Int)parent1.Variable[i]).Value;
valueX2 = ((Int)parent2.Variable[i]).Value;
((Int)offSpring[0].Variable[i]).Value = valueX2;
((Int)offSpring[1].Variable[i]).Value = valueX1;
}
}
}
}
catch (Exception ex)
{
Logger.Log.Error("Error in: " + this.GetType().FullName + ".DoCrossover()", ex);
Console.WriteLine("Error in " + this.GetType().FullName + ".DoCrossover()");
throw new Exception("Exception in " + this.GetType().FullName + ".DoCrossover()");
}
return(offSpring);
}
public bool ShouldCrossOver()
{
return(JMetalRandom.NextDouble() >= CrossOverPercentage);
}
/// <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 void Run()
{
neighborhood = parentThread.neighborhood;
Problem = parentThread.Problem;
lambda = parentThread.lambda;
population = parentThread.population;
z = parentThread.z;
indArray = parentThread.indArray;
barrier = parentThread.barrier;
int partitions = parentThread.populationSize / parentThread.numberOfThreads;
evaluations = 0;
maxEvaluations = parentThread.maxEvaluations / parentThread.numberOfThreads;
barrier.SignalAndWait();
int first;
int last;
first = partitions * id;
if (id == (parentThread.numberOfThreads - 1))
{
last = parentThread.populationSize - 1;
}
else
{
last = first + partitions - 1;
}
Logger.Log.Info("Id: " + id + " Partitions: " + partitions + " First: " + first + " Last: " + last);
Console.WriteLine("Id: " + id + " Partitions: " + partitions + " First: " + first + " Last: " + last);
do
{
for (int i = first; i <= last; i++)
{
int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < parentThread.delta)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
this.MatingSelection(p, n, 2, type);
// STEP 2.2. Reproduction
Solution child = null;
Solution[] parents = new Solution[3];
try
{
lock (parentThread)
{
parents[0] = parentThread.population.Get(p[0]);
parents[1] = parentThread.population.Get(p[1]);
parents[2] = parentThread.population.Get(n);
// Apply DE crossover
child = (Solution)parentThread.crossover.Execute(new object[] { parentThread.population.Get(n), parents });
}
// Apply mutation
parentThread.mutation.Execute(child);
// Evaluation
parentThread.Problem.Evaluate(child);
}
catch (Exception ex)
{
Logger.Log.Error(this.GetType().FullName + ".Run()", ex);
Console.WriteLine("Error in " + this.GetType().FullName + ".Run()");
}
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z
UpdateReference(child);
// STEP 2.5. Update of solutions
UpdateOfSolutions(child, n, type);
}
} while (evaluations < maxEvaluations);
long estimatedTime = Environment.TickCount - parentThread.initTime;
Logger.Log.Info("Time thread " + id + ": " + estimatedTime);
Console.WriteLine("Time thread " + id + ": " + estimatedTime);
}
public override SolutionSet Execute()
{
double contrDE = 0;
double contrSBX = 0;
double contrBLXA = 0;
double contrPol = 0;
double[] contrReal = new double[3];
contrReal[0] = contrReal[1] = contrReal[2] = 0;
IComparer <Solution> dominance = new DominanceComparator();
IComparer <Solution> crowdingComparator = new CrowdingComparator();
Distance distance = new Distance();
Operator selectionOperator;
//Read parameter values
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators);
//Init the variables
population = new SolutionSet(populationSize);
evaluations = 0;
selectionOperator = Operators["selection"];
Offspring[] getOffspring;
int N_O; // number of offpring objects
getOffspring = (Offspring[])GetInputParameter("offspringsCreators");
N_O = getOffspring.Length;
contribution = new double[N_O];
contributionCounter = new int[N_O];
contribution[0] = (double)(populationSize / (double)N_O) / (double)populationSize;
for (int i = 1; i < N_O; i++)
{
contribution[i] = (double)(populationSize / (double)N_O) / (double)populationSize + (double)contribution[i - 1];
}
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
newSolution.Location = i;
population.Add(newSolution);
}
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(2);
Solution[] parents = new Solution[2];
int selectedSolution = JMetalRandom.Next(0, populationSize - 1);
Solution individual = new Solution(population.Get(selectedSolution));
int selected = 0;
bool found = false;
Solution offSpring = null;
double rnd = JMetalRandom.NextDouble();
for (selected = 0; selected < N_O; selected++)
{
if (!found && (rnd <= contribution[selected]))
{
if ("DE" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population, selectedSolution);
contrDE++;
}
else if ("SBXCrossover" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population);
contrSBX++;
}
else if ("BLXAlphaCrossover" == getOffspring[selected].Id)
{
offSpring = getOffspring[selected].GetOffspring(population);
contrBLXA++;
}
else if ("PolynomialMutation" == getOffspring[selected].Id)
{
offSpring = ((PolynomialMutationOffspring)getOffspring[selected]).GetOffspring(individual);
contrPol++;
}
else
{
Console.Error.WriteLine("Error in NSGAIIARandom. Operator " + offSpring + " does not exist");
Logger.Log.Error("NSGAIIARandom. Operator " + offSpring + " does not exist");
}
offSpring.Fitness = (int)selected;
currentCrossover = selected;
found = true;
}
}
Problem.Evaluate(offSpring);
offspringPopulation.Add(offSpring);
evaluations += 1;
// Create the solutionSet union of solutionSet and offSpring
union = ((SolutionSet)population).Union(offspringPopulation);
// Ranking the union
Ranking ranking = new Ranking(union);
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, Problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
}
//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
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
front.Sort(new CrowdingComparator());
for (int k = 0; k < remain; k++)
{
population.Add(front.Get(k));
}
remain = 0;
}
}
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}
private bool ShouldMutate()
{
return(JMetalRandom.NextDouble() >= MutationPercentage);
}
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);
}
public override SolutionSet Execute()
{
int maxEvaluations = -1;
evaluations = 0;
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
JMetalCSharp.Utils.Utils.GetStringValueFromParameter(this.InputParameters, "dataDirectory", ref dataDirectory);
population = new SolutionSet(populationSize);
savedValues = new Solution[populationSize];
utility = new double[populationSize];
frequency = new int[populationSize];
for (int i = 0; i < utility.Length; i++)
{
utility[i] = 1.0;
frequency[i] = 0;
}
indArray = new Solution[Problem.NumberOfObjectives];
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "T", ref t);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "nr", ref nr);
JMetalCSharp.Utils.Utils.GetDoubleValueFromParameter(this.InputParameters, "delta", ref delta);
neighborhood = new int[populationSize][];
for (int i = 0; i < populationSize; i++)
{
neighborhood[i] = new int[t];
}
z = new double[Problem.NumberOfObjectives];
lambda = new double[populationSize][];
for (int i = 0; i < populationSize; i++)
{
lambda[i] = new double[Problem.NumberOfObjectives];
}
crossover = Operators["crossover"]; // default: DE crossover
mutation = Operators["mutation"]; // default: polynomial mutation
// STEP 1. Initialization
// STEP 1.1. Compute euclidean distances between weight vectors and find T
InitUniformWeight();
InitNeighborhood();
// STEP 1.2. Initialize population
InitPopulation();
// STEP 1.3. Initialize z
InitIdealPoint();
int gen = 0;
// STEP 2. Update
do
{
int[] permutation = new int[populationSize];
Utils.RandomPermutation(permutation, populationSize);
List <int> order = TourSelection(10);
for (int i = 0; i < order.Count; i++)
{
int n = order[i];
frequency[n]++;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
MatingSelection(p, n, 2, type);
// STEP 2.2. Reproduction
Solution child;
Solution[] parents = new Solution[3];
parents[0] = population.Get(p[0]);
parents[1] = population.Get(p[1]);
parents[2] = population.Get(n);
// Apply DE crossover
child = (Solution)crossover.Execute(new object[] { population.Get(n), parents });
// Apply mutation
mutation.Execute(child);
// Evaluation
Problem.Evaluate(child);
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z
UpdateReference(child);
// STEP 2.5. Update of solutions
UpdateProblem(child, n, type);
}
gen++;
if (gen % 30 == 0)
{
CompUtility();
}
} while (evaluations < maxEvaluations);
int finalSize = populationSize;
try
{
finalSize = (int)GetInputParameter("finalSize");
Logger.Log.Info("FINAL SIZE: " + finalSize);
Console.WriteLine("FINAL SIZE: " + finalSize);
}
catch (Exception ex)
{ // if there is an exception indicate it!
Logger.Log.Warn("The final size paramater has been ignored", ex);
Logger.Log.Warn("The number of solutions is " + population.Size());
Console.WriteLine("The final size paramater has been ignored");
Console.WriteLine("The number of solutions is " + population.Size());
return(population);
}
Result = FinalSelection(finalSize);
return(Result);
}
public SolutionSet Mix()
{
QualityIndicator indicators = new QualityIndicator(problem, fileRead.Qi); // QualityIndicator object
int requiredEvaluations = 0; // Use in the example of use of the
// indicators object (see below)
evaluations = 0;
iteration = 0;
populationSize = int.Parse(fileRead.Ps);
iterationsNumber = int.Parse(fileRead.Itn);
dataDirectory = "Data/Parameters/Weight";
Logger.Log.Info("POPSIZE: " + populationSize);
Console.WriteLine("POPSIZE: " + populationSize);
population = new SolutionSet(populationSize);
indArray = new Solution[problem.NumberOfObjectives];
t = int.Parse(fileRead.T);
nr = int.Parse(fileRead.Nr);
delta = double.Parse(fileRead.Delta);
gamma = double.Parse(fileRead.Gamma);
neighborhood = new int[populationSize][];
for (int i = 0; i < populationSize; i++)
{
neighborhood[i] = new int[t];
}
z = new double[problem.NumberOfObjectives];
//znad = new double[Problem.NumberOfObjectives];
lambda = new double[populationSize][];
for (int i = 0; i < populationSize; i++)
{
lambda[i] = new double[problem.NumberOfObjectives];
}
/*string dir = "Result/" + fileRead.Al + "_" + fileRead.Co + "_" + fileRead.Co2 + "/" + fileRead.Pb + "_" + fileRead.St + "/Record/SBX(" + double.Parse(fileRead.Ra).ToString("#0.00") + ")+ACOR(" + (1-double.Parse(fileRead.Ra)).ToString("#0.00") + ")";
* if (Directory.Exists(dir))
* {
* Console.WriteLine("The directory {0} already exists.", dir);
* }
* else
* {
* Directory.CreateDirectory(dir);
* Console.WriteLine("The directory {0} was created.", dir);
* }*/
//Step 1. Initialization
//Step 1.1 Compute euclidean distances between weight vectors and find T
InitUniformWeight();
InitNeighborhood();
//Step 1.2 Initialize population
InitPoputalion();
//Step 1.3 Initizlize z
InitIdealPoint();
//Step 2 Update
for (int a = 0; a < iterationsNumber; a++)
{
int[] permutation = new int[populationSize];
JMetalCSharp.Metaheuristics.MOEAD.Utils.RandomPermutation(permutation, populationSize);
Solution[] parents = new Solution[2];
int t = 0;
if (a >= double.Parse(fileRead.Ra) * iterationsNumber)
{
for (int i = 0; i < populationSize; i++)
{
int n = permutation[i];
// or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. ACOR selection based on probability
if (rnd < gamma) // if (rnd < realb)
{
type = 1; // minmum
//parents[0] = population.Get(ACOrSelection2(n, type, pro_T));
}
else
{
type = 2; // whole neighborhood probability
//parents[0] = population.Get(ACOrSelection2(n, type, pro_A));
}
GetStdDev(neighborhood);
//GetStdDev1(neighborhood, type);
//List<int> p = new List<int>();
//MatingSelection(p, n, 1, type);
// STEP 2.2. Reproduction
Solution child;
parents[0] = population.Get(ACOrSelection(n, type));
parents[1] = population.Get(n);
//parents[0] = population.Get(p[0]);
// Apply ACOR crossover
child = (Solution)crossover2.Execute(parents);
child.NumberofReplace = t;
// // Apply mutation
mutation.Execute(child);
// Evaluation
problem.Evaluate(child);
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(child);
// STEP 2.5. Update of solutions
t = UpdateProblemWithReplace(child, n, 1);
}
}
else
{
// Create the offSpring solutionSet
SolutionSet offspringPopulation = new SolutionSet(populationSize);
for (int i = 0; i < (populationSize / 2); i++)
{
int n = permutation[i]; // or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta) // if (rnd < realb)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
MatingSelection(p, n, 2, type);
parents[0] = population.Get(p[0]);
parents[1] = population.Get(p[1]);
//obtain parents
Solution[] offSpring = (Solution[])crossover1.Execute(parents);
//Solution child;
mutation.Execute(offSpring[0]);
mutation.Execute(offSpring[1]);
/*if(rnd < 0.5)
* {
* child = offSpring[0];
* }
* else
* {
* child = offSpring[1];
* }*/
problem.Evaluate(offSpring[0]);
problem.Evaluate(offSpring[1]);
problem.EvaluateConstraints(offSpring[0]);
problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(offSpring[0]);
UpdateReference(offSpring[1]);
// STEP 2.5. Update of solutions
UpdateProblem(offSpring[0], n, type);
UpdateProblem(offSpring[1], n, type);
}
//for (int i = 0; i < populationSize; i++)
//{
// int n = permutation[i]; // or int n = i;
// int type;
// double rnd = JMetalRandom.NextDouble();
// // STEP 2.1. Mating selection based on probability
// if (rnd < delta) // if (rnd < realb)
// {
// type = 1; // neighborhood
// }
// else
// {
// type = 2; // whole population
// }
// List<int> p = new List<int>();
// MatingSelection(p, n, 2, type);
// // STEP 2.2. Reproduction
// Solution child;
// Solution[] parent = new Solution[3];
// parent[0] = population.Get(p[0]);
// parent[1] = population.Get(p[1]);
// parent[2] = population.Get(n);
// // Apply DE crossover
// child = (Solution)crossover1.Execute(new object[] { population.Get(n), parent });
// // Apply mutation
// mutation.Execute(child);
// // Evaluation
// problem.Evaluate(child);
// evaluations++;
// // STEP 2.3. Repair. Not necessary
// // STEP 2.4. Update z_
// UpdateReference(child);
// // STEP 2.5. Update of solutions
// UpdateProblem(child, n, type);
//}
}
/*string filevar = dir + "/VAR" + iteration;
* string filefun = dir + "/FUN" + iteration;
* population.PrintVariablesToFile(filevar);
* population.PrintObjectivesToFile(filefun);*/
iteration++;
if ((indicators != null) && (requiredEvaluations == 0))
{
double HV = indicators.GetHypervolume(population);
if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume))
{
requiredEvaluations = evaluations;
}
}
}
Logger.Log.Info("ITERATION: " + iteration);
Console.WriteLine("ITERATION: " + iteration);
SolutionSet Result = population;
//return population;
// Return the first non-dominated front
Ranking rank = new Ranking(population);
//SolutionSet Result = rank.GetSubfront(0);
return(Result);
}