/**
* Constructor. Creates a new instance of Spea2Fitness for a given <code>SolutionSet</code>.
*
* @param solutionSet The <code>SolutionSet</code>
*/
public Spea2Fitness(SolutionSet solutionSet) {
distance = distance_.distanceMatrix(solutionSet);
solutionSet_ = solutionSet;
for (int i = 0; i < solutionSet_.size(); i++) {
solutionSet_.get(i).setLocation(i);
} // for
} // Spea2Fitness
/** Assigns fitness for all the solutions. */
public void fitnessAssign() {
double[] strength = new double[solutionSet_.size()];
double[] rawFitness = new double[solutionSet_.size()];
double kDistance;
// Calculate the strength value
// strength(i) = |{j | j <- SolutionSet and i dominate j}|
for (int i = 0; i < solutionSet_.size(); i++) {
for (int j = 0; j < solutionSet_.size(); j++) {
if (dominance_.compare(solutionSet_.get(i), solutionSet_.get(j)) == -1) {
strength[i] += 1.0;
} // if
} // for
} // for
// Calculate the raw fitness
// rawFitness(i) = |{sum strenght(j) | j <- SolutionSet and j dominate i}|
for (int i = 0; i < solutionSet_.size(); i++) {
for (int j = 0; j < solutionSet_.size(); j++) {
if (dominance_.compare(solutionSet_.get(i), solutionSet_.get(j)) == 1) {
rawFitness[i] += strength[j];
} // if
} // for
} // for
// Add the distance to the k-th individual. In the reference paper of SPEA2,
// k = sqrt(population.size()), but a value of k = 1 recommended. See
// http://www.tik.ee.ethz.ch/pisa/selectors/spea2/spea2_documentation.txt
int k = 1;
for (int i = 0; i < distance.length; i++) {
Arrays.sort(distance[i]);
kDistance = 1.0 / (distance[i][k] + 2.0); // Calcule de D(i) distance
// population.get(i).setFitness(rawFitness[i]);
solutionSet_.get(i).setFitness(rawFitness[i] + kDistance);
} // for
} // fitnessAsign
/** @param args the command line arguments -- modelname, alg_name, evaluation_times, [runid] */
public static void main(String[] args) throws Exception {
try {
String name = args[0];
URL location = Main.class.getProtectionDomain().getCodeSource().getLocation();
String loc = location.toString();
String project_path =
loc.substring(5, loc.lastIndexOf("SPL/")) + "SPL/"; // with '/' at the end
String fm = project_path + "dimacs_data/" + name + ".dimacs";
String augment = fm + ".augment";
String dead = fm + ".dead";
String mandatory = fm + ".mandatory";
String seed = fm + ".richseed";
String opfile = fm + ".sipop";
Problem p = new ProductLineProblem(fm, augment, mandatory, dead, seed);
// Problem p = new ProductLineProblemNovelPrep(fm, augment, mandatory, dead, seed,
// opfile);
// GroupedProblem.grouping((ProductLineProblem) p, 100); System.exit(0);
Algorithm a;
int evaluation_times = Integer.parseInt(args[2]);
String alg_name = args[1];
String runid = "";
if (args.length >= 4) {
runid = args[3];
}
switch (alg_name) {
case "IBEA":
a = new SPL_SettingsIBEA(p).configureICSE2013(evaluation_times);
break;
case "SIPIBEA":
a = new SPL_SettingsIBEA(p).configureSIPIBEA(evaluation_times);
break;
case "SPEA2":
a = new SPL_SettingsEMOs(p).configureSPEA2(evaluation_times);
break;
case "NSGA2":
a = new SPL_SettingsEMOs(p).configureNSGA2(evaluation_times);
break;
case "IBEASEED":
a = new SPL_SettingsIBEA(p).configureIBEASEED(evaluation_times);
break;
case "SATIBEA":
// a = new SPL_SettingsIBEA(p).configureICSE15(1000, fm, ((ProductLineProblem)
// p).getNumFeatures(),
// ((ProductLineProblem) p).getConstraints());
a =
new SPL_SettingsIBEA(p)
.configureSATIBEA(
evaluation_times,
fm,
((ProductLineProblem) p).getNumFeatures(),
((ProductLineProblem) p).getConstraints());
break;
default:
a = new SPL_SettingsIBEA(p).configureICSE2013(evaluation_times);
}
long start = System.currentTimeMillis();
SolutionSet pop = a.execute();
float total_time = (System.currentTimeMillis() - start) / 1000.0f;
String file_tag =
name + "_" + alg_name + '_' + evaluation_times / 1000 + "k_" + runid + ".txt";
String file_path = project_path + "j_res/" + file_tag;
File file = new File(file_path);
if (!file.exists()) {
file.createNewFile();
}
FileWriter fw = new FileWriter(file.getAbsoluteFile());
BufferedWriter bw = new BufferedWriter(fw);
for (int i = 0; i < pop.size(); i++) {
Variable v = pop.get(i).getDecisionVariables()[0];
bw.write((Binary) v + "\n");
System.out.println("Conf" + (i + 1) + ": " + (Binary) v + " ");
}
bw.write("~~~\n");
for (int i = 0; i < pop.size(); i++) {
Variable v = pop.get(i).getDecisionVariables()[0];
for (int j = 0; j < pop.get(i).getNumberOfObjectives(); j++) {
bw.write(pop.get(i).getObjective(j) + " ");
System.out.print(pop.get(i).getObjective(j) + " ");
}
bw.write("\n");
System.out.println("");
}
System.out.println(total_time);
bw.write("~~~\n" + total_time + "\n");
bw.close();
fw.close();
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* Gets 'size' elements from a population of more than 'size' elements using for this de
* enviromentalSelection truncation
*
* @param size The number of elements to get.
*/
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).getFitness() < 1.0) {
aux.add(solutionSet_.get(i));
solutionSet_.remove(i);
} else {
i++;
} // if
} // while
if (aux.size() < size) {
Comparator 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[][] distance = distance_.distanceMatrix(aux);
List<List<DistanceNode>> distanceList = new LinkedList<List<DistanceNode>>();
for (int pos = 0; pos < aux.size(); pos++) {
aux.get(pos).setLocation(pos);
List<DistanceNode> distanceNodeList = new ArrayList<DistanceNode>();
for (int ref = 0; ref < aux.size(); ref++) {
if (pos != ref) {
distanceNodeList.add(new DistanceNode(distance[pos][ref], ref));
} // if
} // for
distanceList.add(distanceNodeList);
} // for
for (int q = 0; q < distanceList.size(); q++) {
Collections.sort(distanceList.get(q), distanceNodeComparator);
} // for
while (aux.size() > size) {
double minDistance = Double.MAX_VALUE;
int toRemove = 0;
i = 0;
Iterator<List<DistanceNode>> iterator = distanceList.iterator();
while (iterator.hasNext()) {
List<DistanceNode> dn = iterator.next();
if (dn.get(0).getDistance() < minDistance) {
toRemove = i;
minDistance = dn.get(0).getDistance();
// i y toRemove have the same distance to the first solution
} else if (dn.get(0).getDistance() == minDistance) {
int k = 0;
while ((dn.get(k).getDistance() == distanceList.get(toRemove).get(k).getDistance())
&& k < (distanceList.get(i).size() - 1)) {
k++;
}
if (dn.get(k).getDistance() < distanceList.get(toRemove).get(k).getDistance()) {
toRemove = i;
} // if
} // if
i++;
} // while
int tmp = aux.get(toRemove).getLocation();
aux.remove(toRemove);
distanceList.remove(toRemove);
Iterator<List<DistanceNode>> externIterator = distanceList.iterator();
while (externIterator.hasNext()) {
Iterator<DistanceNode> interIterator = externIterator.next().iterator();
while (interIterator.hasNext()) {
if (interIterator.next().getReference() == tmp) {
interIterator.remove();
continue;
} // if
} // while
} // while
} // while
return aux;
} // environmentalSelection
/**
* @author Juanjo This method selects N solutions from a set M, where N <= M using the same method
* proposed by Qingfu Zhang, W. Liu, and Hui Li in the paper describing MOEA/D-DRA (CEC 09
* COMPTETITION) An example is giving in that paper for two objectives. If N = 100, then the
* best solutions attenting to the weights (0,1), (1/99,98/99), ...,(98/99,1/99), (1,0) are
* selected.
* <p>Using this method result in 101 solutions instead of 100. We will just compute 100 even
* distributed weights and used them. The result is the same
* <p>In case of more than two objectives the procedure is: 1- Select a solution at random 2-
* Select the solution from the population which have maximum distance to it (whithout
* considering the already included)
* @param n: The number of solutions to return
* @return A solution set containing those elements
*/
SolutionSet finalSelection(int n) throws JMException {
SolutionSet res = new SolutionSet(n);
if (problem_.getNumberOfObjectives() == 2) { // subcase 1
double[][] intern_lambda = new double[n][2];
for (int i = 0; i < n; i++) {
double a = 1.0 * i / (n - 1);
intern_lambda[i][0] = a;
intern_lambda[i][1] = 1 - a;
} // for
// we have now the weights, now select the best solution for each of them
for (int i = 0; i < n; i++) {
Solution current_best = population.get(0);
int index = 0;
double value = fitnessFunction(current_best, intern_lambda[i]);
for (int j = 1; j < n; j++) {
double aux =
fitnessFunction(
population.get(j), intern_lambda[i]); // we are looking the best for the weight i
if (aux < value) { // solution in position j is better!
value = aux;
current_best = population.get(j);
}
}
res.add(new Solution(current_best));
}
} else { // general case (more than two objectives)
Distance distance_utility = new Distance();
int random_index = PseudoRandom.randInt(0, population.size() - 1);
// create a list containing all the solutions but the selected one (only references to them)
List<Solution> candidate = new LinkedList<Solution>();
candidate.add(population.get(random_index));
for (int i = 0; i < population.size(); i++) {
if (i != random_index) {
candidate.add(population.get(i));
}
} // for
while (res.size() < n) {
int index = 0;
Solution selected = candidate.get(0); // it should be a next! (n <= population size!)
double distance_value =
distance_utility.distanceToSolutionSetInObjectiveSpace(selected, res);
int i = 1;
while (i < candidate.size()) {
Solution next_candidate = candidate.get(i);
double aux =
distance_value =
distance_utility.distanceToSolutionSetInObjectiveSpace(next_candidate, res);
if (aux > distance_value) {
distance_value = aux;
index = i;
}
i++;
}
// add the selected to res and remove from candidate list
res.add(new Solution(candidate.remove(index)));
} //
}
return res;
}