@Override
public void executeMethod() throws JMException {
int[] permutation = new int[populationSize];
Utils.randomPermutation(permutation, populationSize);
List<Integer> order = tour_selection(10);
for (int i = 0; i < order.size(); i++) {
// int n = permutation[i]; // or int n = i;
int n = order.get(i); // or int n = i;
frequency_[n]++;
int type;
double rnd = PseudoRandom.randDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta_) // if (rnd < realb)
{
type = 1; // neighborhood
} else {
type = 2; // whole population
}
Vector<Integer> p = new Vector<Integer>();
matingSelection(p, n, 1, type);
// STEP 2.2. Reproduction
Solution[] parents = new Solution[2];
parents[0] = population.get(p.get(0));
parents[1] = population.get(n);
Solution[] offSpring = (Solution[]) crossoverOperator.execute(parents, (CITO_CAITO) problem_);
mutationOperator.execute(offSpring[0], (CITO_CAITO) problem_);
// mutationOperator.execute(offSpring[1], problem_);
problem_.evaluate(offSpring[0]);
problem_.evaluateConstraints(offSpring[0]);
// problem_.evaluate(offSpring[1]);
// problem_.evaluateConstraints(offSpring[1]);
evaluations++;
// 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
gen++;
if (gen % 30 == 0) {
comp_utility();
}
}
public void initNeighborhood() {
double[] x = new double[populationSize];
int[] idx = new int[populationSize];
for (int i = 0; i < populationSize; i++) {
// calculate the distances based on weight vectors
for (int j = 0; j < populationSize; j++) {
x[j] = Utils.distVector(lambda_[i], lambda_[j]);
// x[j] = dist_vector(population[i].namda,population[j].namda);
idx[j] = j;
// System.out.println("x["+j+"]: "+x[j]+ ". idx["+j+"]: "+idx[j]) ;
} // for
// find 'niche' nearest neighboring subproblems
Utils.minFastSort(x, idx, populationSize, T_);
// minfastsort(x,idx,population.size(),niche);
System.arraycopy(idx, 0, neighborhood_[i], 0, T_);
} // for
} // initNeighborhood
/**
* @param individual
* @param id
* @param type
*/
void updateProblem(Solution indiv, int id, int type) {
// indiv: child solution
// id: the id of current subproblem
// type: update solutions in - neighborhood (1) or whole population (otherwise)
int size;
int time;
time = 0;
if (type == 1) {
size = neighborhood_[id].length;
} else {
size = population.size();
}
int[] perm = new int[size];
Utils.randomPermutation(perm, size);
for (int i = 0; i < size; i++) {
int k;
if (type == 1) {
k = neighborhood_[id][perm[i]];
} else {
k = perm[i]; // calculate the values of objective function regarding the current subproblem
}
double f1, f2;
f1 = fitnessFunction(population.get(k), lambda_[k]);
f2 = fitnessFunction(indiv, lambda_[k]);
if (f2 < f1) {
population.replace(k, new Solution(indiv));
// population[k].indiv = indiv;
time++;
}
// the maximal number of solutions updated is not allowed to exceed 'limit'
if (time >= nr_) {
return;
}
}
} // updateProblem
