/**
* Evaluates a solution
*
* @param solution The solution to evaluate
*/
public void evaluate(Solution solution) {
double fitness1, fitness2;
int x, y;
fitness1 = 0.0;
fitness2 = 0.0;
for (int i = 0; i < (numberOfCities_); i++) {
for (int j = 0; j < (numberOfCities_); j++) {
x = ((Permutation) solution.getDecisionVariables().variables_[0]).vector_[i];
y = ((Permutation) solution.getDecisionVariables().variables_[0]).vector_[j];
fitness1 += distanceMatrix_[i][j] * flujo1[x][y];
}
}
for (int i = 0; i < (numberOfCities_); i++) {
for (int j = 0; j < (numberOfCities_); j++) {
x = ((Permutation) solution.getDecisionVariables().variables_[0]).vector_[i];
y = ((Permutation) solution.getDecisionVariables().variables_[0]).vector_[j];
fitness2 += distanceMatrix_[i][j] * flujo2[x][y];
}
}
solution.setObjective(0, fitness1);
solution.setObjective(1, fitness2);
} // evaluate
/**
* Evaluates a solution
*
* @param solution The solution to evaluate
* @throws JMException
*/
public void evaluate(Solution solution) throws JMException {
DecisionVariables gen = solution.getDecisionVariables();
double[] x = new double[numberOfVariables_];
double[] f = new double[numberOfObjectives_];
double[] theta = new double[numberOfObjectives_ - 1];
int k = numberOfVariables_ - numberOfObjectives_ + 1;
for (int i = 0; i < numberOfVariables_; i++) x[i] = gen.variables_[i].getValue();
double g = 0.0;
for (int i = numberOfVariables_ - k; i < numberOfVariables_; i++)
g += java.lang.Math.pow(x[i], 0.1);
double t = java.lang.Math.PI / (4.0 * (1.0 + g));
theta[0] = x[0] * java.lang.Math.PI / 2;
for (int i = 1; i < (numberOfObjectives_ - 1); i++) theta[i] = t * (1.0 + 2.0 * g * x[i]);
for (int i = 0; i < numberOfObjectives_; i++) f[i] = 1.0 + g;
for (int i = 0; i < numberOfObjectives_; i++) {
for (int j = 0; j < numberOfObjectives_ - (i + 1); j++) f[i] *= java.lang.Math.cos(theta[j]);
if (i != 0) {
int aux = numberOfObjectives_ - (i + 1);
f[i] *= java.lang.Math.sin(theta[aux]);
} // if
} // for
for (int i = 0; i < numberOfObjectives_; i++) solution.setObjective(i, f[i]);
} // evaluate
/** @param individual */
void updateReference(Solution individual) {
for (int n = 0; n < problem_.getNumberOfObjectives(); n++) {
if (individual.getObjective(n) < z_[n]) {
z_[n] = individual.getObjective(n);
indArray_[n] = individual;
}
}
} // updateReference
double fitnessFunction(Solution individual, double[] lambda) {
double fitness;
fitness = 0.0;
if (functionType_.equals("_TCHE1")) {
double maxFun = -1.0e+30;
for (int n = 0; n < problem_.getNumberOfObjectives(); n++) {
double diff = Math.abs(individual.getObjective(n) - z_[n]);
double feval;
if (lambda[n] == 0) {
feval = 0.0001 * diff;
} else {
feval = diff * lambda[n];
}
if (feval > maxFun) {
maxFun = feval;
}
} // for
fitness = maxFun;
} // if
else if (functionType_.equals("_AGG")) {
double sum = 0.0;
for (int n = 0; n < problem_.getNumberOfObjectives(); n++) {
sum += (lambda[n]) * individual.getObjective(n);
}
fitness = sum;
} else if (functionType_.equals("_PBI")) {
double d1, d2, nl;
double theta = 5.0;
d1 = d2 = nl = 0.0;
for (int i = 0; i < problem_.getNumberOfObjectives(); i++) {
d1 += (individual.getObjective(i) - z_[i]) * lambda[i];
nl += Math.pow(lambda[i], 2.0);
}
nl = Math.sqrt(nl);
d1 = Math.abs(d1) / nl;
for (int i = 0; i < problem_.getNumberOfObjectives(); i++) {
d2 += Math.pow((individual.getObjective(i) - z_[i]) - d1 * (lambda[i] / nl), 2.0);
}
d2 = Math.sqrt(d2);
fitness = (d1 + theta * d2);
} else {
System.out.println("MOEAD.fitnessFunction: unknown type " + functionType_);
System.exit(-1);
}
return fitness;
} // fitnessEvaluation
/**
* Evaluates a solution
*
* @param solution The solution to evaluate
* @throws Exception
*/
public void evaluate(Solution solution) throws Exception {
ProblemVariables decisionVariables = solution.getProblemVariables();
double[] f = new double[numberOfObjectives_];
f[0] = decisionVariables.variables[0].getValue();
double g = this.evalG(decisionVariables);
double h = this.evalH(f[0], g);
f[1] = h * g;
solution.setObjective(0, f[0]);
solution.setObjective(1, f[1]);
} // evaluate
/**
* Evaluates a solution
*
* @param solution The solution to evaluate
* @throws JMException
*/
public void evaluate(Solution solution) throws JMException {
Variable[] variable = solution.getDecisionVariables();
double[] f = new double[numberOfObjectives_];
double x1 = variable[0].getValue();
double x2 = variable[1].getValue();
f[0] = 2.0 + (x1 - 2.0) * (x1 - 2.0) + (x2 - 1.0) * (x2 - 1.0);
f[1] = 9.0 * x1 - (x2 - 1.0) * (x2 - 1.0);
solution.setObjective(0, f[0]);
solution.setObjective(1, f[1]);
} // evaluate
/**
* Evaluates a solution
*
* @param solution The solution to evaluate
* @throws JMException
*/
public void evaluate(Solution solution) throws JMException {
Variable[] gen = solution.getDecisionVariables();
Vector<Double> x = new Vector<Double>(numberOfVariables_);
Vector<Double> y = new Vector<Double>(numberOfObjectives_);
for (int i = 0; i < numberOfVariables_; i++) {
x.addElement(gen[i].getValue());
y.addElement(0.0);
} // for
LZ09_.objective(x, y);
for (int i = 0; i < numberOfObjectives_; i++) solution.setObjective(i, y.get(i));
} // evaluate
// -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
@Override
public void evaluate(Solution solution) {
double fitness0 = 0.0;
double fitness1 = 0.0;
double fitness2 = 0.0;
double fitness3 = 0.0;
boolean verificador;
int x;
int y;
// percorre o vetor de solucoes
for (int i = 0; i < numberOfElements_; i++) {
// pega o id da classe
x = ((Permutation) solution.getDecisionVariables()[0]).vector_[i];
// percorre as colunas da matrix de dependencia
for (int k = 0; k < numberOfElements_; k++) {
// verifica se existe dependencia de x para k
if (dependency_matrix_[x][k] == 1) {
verificador = false;
// verifica se a classe já exite
for (int j = 0; j <= i; j++) {
y = ((Permutation) solution.getDecisionVariables()[0]).vector_[j];
if (y == k) {
verificador = true;
}
}
// adiciona os valores ao fitnesse se a classe não tiver sido testada ainda
if (verificador == false) {
fitness0 += attribute_coupling_matrix_[x][k];
fitness1 += method_coupling_matrix_[x][k];
fitness2 += method_return_type_matrix_[x][k];
fitness3 += method_param_type_matrix_[x][k];
}
}
}
}
// System.out.println(fitness0+" "+fitness1+" "+fitness2+" "+fitness3);
solution.setObjective(0, fitness0);
solution.setObjective(1, fitness1);
solution.setObjective(2, fitness2);
solution.setObjective(3, fitness3);
}
/**
* Evaluates a solution
*
* @param solution The solution to evaluate
* @throws JMException
*/
public void evaluate(Solution solution) throws JMException {
XReal x = new XReal(solution);
double[] f = new double[numberOfObjectives_];
double sum1 = 0.0;
for (int var = 0; var < numberOfVariables_; var++) {
sum1 +=
StrictMath.pow(
x.getValue(var) - (1.0 / StrictMath.sqrt((double) numberOfVariables_)), 2.0);
}
double exp1 = StrictMath.exp((-1.0) * sum1);
f[0] = 1 - exp1;
double sum2 = 0.0;
for (int var = 0; var < numberOfVariables_; var++) {
sum2 +=
StrictMath.pow(
x.getValue(var) + (1.0 / StrictMath.sqrt((double) numberOfVariables_)), 2.0);
}
double exp2 = StrictMath.exp((-1.0) * sum2);
f[1] = 1 - exp2;
solution.setObjective(0, f[0]);
solution.setObjective(1, f[1]);
} // evaluate
/**
* Evaluates the constraint overhead of a solution
*
* @param solution The solution
* @throws JMException
*/
public void evaluateConstraints(Solution solution) throws JMException {
double[] constraint = new double[this.getNumberOfConstraints()];
double x1 = solution.getDecisionVariables()[0].getValue();
double x2 = solution.getDecisionVariables()[1].getValue();
constraint[0] = 1.0 - (x1 * x1 + x2 * x2) / 225.0;
constraint[1] = (3.0 * x2 - x1) / 10.0 - 1.0;
double total = 0.0;
int number = 0;
for (int i = 0; i < this.getNumberOfConstraints(); i++)
if (constraint[i] < 0.0) {
number++;
total += constraint[i];
}
solution.setOverallConstraintViolation(total);
solution.setNumberOfViolatedConstraint(number);
} // evaluateConstraints
/**
* Evaluates a solution
*
* @param solution The solution to evaluate
* @throws JMException
*/
public void evaluate(Solution solution) throws JMException {
DecisionVariables decisionVariables = solution.getDecisionVariables();
double[] x = new double[numberOfVariables_];
double[] fx = new double[numberOfVariables_];
double g;
double h;
double sum;
for (int i = 0; i < numberOfVariables_; i++) x[i] = decisionVariables.variables_[i].getValue();
fx[0] = x[0];
sum = 0.0;
for (int i = 1; i < numberOfVariables_; i++) sum += (x[i] * x[i] - x[0]) * (x[i] * x[i] - x[0]);
g = 1.0 + 9.0 * sum / (numberOfVariables_ - 1.0);
h = 1.0 - Math.sqrt(x[0] / g);
fx[1] = g * h;
solution.setObjective(0, fx[0]);
solution.setObjective(1, fx[1]);
} // evaluate
/**
* Evaluates a solution.
*
* @param solution The solution to evaluate.
* @throws JMException
*/
public void evaluate(Solution solution) throws JMException {
Variable[] decisionVariables = solution.getDecisionVariables();
double[] x = new double[numberOfVariables_];
for (int i = 0; i < numberOfVariables_; i++) x[i] = decisionVariables[i].getValue();
int count1, count2, count3;
double sum1, sum2, sum3, yj, hj;
sum1 = sum2 = sum3 = 0.0;
count1 = count2 = count3 = 0;
for (int j = 3; j <= numberOfVariables_; j++) {
yj =
x[j - 1] - 2.0 * x[1] * Math.sin(2.0 * Math.PI * x[0] + j * Math.PI / numberOfVariables_);
hj = 4.0 * yj * yj - Math.cos(8.0 * Math.PI * yj) + 1.0;
if (j % 3 == 1) {
sum1 += hj;
count1++;
} else if (j % 3 == 2) {
sum2 += hj;
count2++;
} else {
sum3 += hj;
count3++;
}
}
solution.setObjective(
0,
Math.cos(0.5 * Math.PI * x[0]) * Math.cos(0.5 * Math.PI * x[1])
+ 2.0 * sum1 / (double) count1);
solution.setObjective(
1,
Math.cos(0.5 * Math.PI * x[0]) * Math.sin(0.5 * Math.PI * x[1])
+ 2.0 * sum2 / (double) count2);
solution.setObjective(2, Math.sin(0.5 * Math.PI * x[0]) + 2.0 * sum3 / (double) count3);
} // evaluate
/**
* Perform the mutation operation
*
* @param probability Mutation probability
* @param solution The solution to mutate
* @throws JMException
*/
public void doMutation(double probability, Solution solution) throws JMException {
if (PseudoRandom.randDouble() < probability) {
List<Partition> partitions =
((MusicVariableAtonal) solution.getDecisionVariables()[0]).getPartitions();
int s = partitions.size() - 1;
int melodyIndex = PseudoRandom.randInt(0, s);
Partition partition = partitions.get(melodyIndex);
List<NotePos> notePositions = partition.getNotes();
int startPartition = partition.getPosition();
int endPartition = startPartition + partition.getLength();
if (!notePositions.isEmpty() && notePositions.size() > 1) {
int position = PseudoRandom.randInt(0, notePositions.size() - 1);
NotePos note = notePositions.get(position);
int index = notePositions.indexOf(note);
if (random.nextBoolean()) {
if (index >= 1) {
NotePos prevNote = notePositions.get(index - 1);
if (prevNote.getVoice() != note.getVoice()) {
// int prevPosition = prevNote.getPosition();
int notePosition = note.getPosition();
if (!containsNoteAtPosition(notePositions, notePosition - MIN_LENGTH)
&& startPartition <= notePosition - MIN_LENGTH) {
note.setPosition(notePosition - MIN_LENGTH);
System.out.println("Shifted");
}
}
}
} else {
if (index < notePositions.size() - 1) {
NotePos nextNote = notePositions.get(index + 1);
if (nextNote.getVoice() != note.getVoice()) {
// int nextPosition = nextNote.getPosition();
int notePosition = note.getPosition();
if (!containsNoteAtPosition(notePositions, notePosition + MIN_LENGTH)
&& notePosition + MIN_LENGTH <= endPartition) {
note.setPosition(notePosition + MIN_LENGTH);
System.out.println("Shifted");
}
}
}
}
}
}
}
/**
* Perform the crossover operation
*
* @param probability Crossover probability
* @param parent1 The first parent
* @param parent2 The second parent
* @return Two offspring solutions
* @throws JMException
*/
public Solution[] doCrossover(double probability, Solution parent1, Solution parent2)
throws JMException {
Solution[] offspring = new Solution[2];
offspring[0] = new Solution(parent1);
offspring[1] = new Solution(parent2);
if (PseudoRandom.randDouble() < probability) {
int crosspoint1;
int crosspoint2;
int chromosomeLength;
DecisionVariables parent1Vars;
DecisionVariables parent2Vars;
DecisionVariables offspring1Vars;
DecisionVariables offspring2Vars;
// permutationLength = ((Permutation)parent1.getDecisionVariables().variables_[0]).getLength()
// ;
chromosomeLength = parent1.getDecisionVariables().size();
parent1Vars = parent1.getDecisionVariables();
parent2Vars = parent2.getDecisionVariables();
offspring1Vars = offspring[0].getDecisionVariables();
offspring2Vars = offspring[1].getDecisionVariables();
// STEP 1: Get two cutting points
crosspoint1 = PseudoRandom.randInt(0, chromosomeLength - 1);
crosspoint2 = PseudoRandom.randInt(0, chromosomeLength - 1);
while (crosspoint2 == crosspoint1)
crosspoint2 = PseudoRandom.randInt(0, chromosomeLength - 1);
if (crosspoint1 > crosspoint2) {
int swap;
swap = crosspoint1;
crosspoint1 = crosspoint2;
crosspoint2 = swap;
} // if
// // STEP 2: Obtain the two children
//
// for(int k = 0; k < crosspoint1; k++) {
// offspring1Vars.variables_[k] = parent1Vars.variables_[k];
// offspring2Vars.variables_[k] = parent2Vars.variables_[k];
// }
//
// for(int k = crosspoint1; k < crosspoint2; k++) {
// offspring2Vars.variables_[k] = parent1Vars.variables_[k];
// offspring1Vars.variables_[k] = parent2Vars.variables_[k];
// } // for
//
// for(int k = crosspoint2; k < chromosomeLength; k++) {
// offspring1Vars.variables_[k] = parent1Vars.variables_[k];
// offspring2Vars.variables_[k] = parent2Vars.variables_[k];
// }
// STEP 2: Obtain the two children
for (int k = 0; k < crosspoint1; k++) {
offspring1Vars.variables_[k] = parent1Vars.variables_[k].deepCopy();
offspring2Vars.variables_[k] = parent2Vars.variables_[k].deepCopy();
}
for (int k = crosspoint1; k < crosspoint2; k++) {
offspring2Vars.variables_[k] = parent1Vars.variables_[k].deepCopy();
offspring1Vars.variables_[k] = parent2Vars.variables_[k].deepCopy();
} // for
for (int k = crosspoint2; k < chromosomeLength; k++) {
offspring1Vars.variables_[k] = parent1Vars.variables_[k].deepCopy();
offspring2Vars.variables_[k] = parent2Vars.variables_[k].deepCopy();
}
Comparator dominance = new DominanceComparator();
if (((dominance.compare(parent1, parent2) != -1)
&& (crosspoint2 - crosspoint1 >= chromosomeLength / 2))
|| ((dominance.compare(parent1, parent2) == -1)
&& (crosspoint2 - crosspoint1 < chromosomeLength / 2))) {
offspring[0].setDecisionVariables(offspring1Vars);
offspring[1].setDecisionVariables(offspring2Vars);
} else if (((dominance.compare(parent2, parent1) != -1)
&& (crosspoint2 - crosspoint1 >= chromosomeLength / 2))
|| ((dominance.compare(parent2, parent1) == -1)
&& (crosspoint2 - crosspoint1 < chromosomeLength / 2))) {
offspring[1].setDecisionVariables(offspring1Vars);
offspring[0].setDecisionVariables(offspring2Vars);
}
// else
// {
// Configuration.logger_.severe("TwoPointsCrossover.doCrossover: invalid " +
// "type" +
// parent1.getDecisionVariables().variables_[0].getVariableType());
// Class cls = java.lang.String.class;
// String name = cls.getName();
// throw new JMException("Exception in " + name + ".doCrossover()") ;
// } // else
}
return offspring;
} // makeCrossover
