protected void generate(String folder, List<String> files, int totalOfFiles, int level)
throws Exception {
logger.info(
"Generating Quality Indicators for "
+ folder
+ ". Finding a True Pareto-front file in the path");
File parent = new File(folder);
for (int i = 0; i < level; i++) {
parent = parent.getParentFile();
}
String approxTrueParetoFrontPath = parent.getAbsolutePath() + File.separator + "PFAPPROX";
if (!new File(approxTrueParetoFrontPath).exists()) {
throw new Exception("You must to define a true Pareto-front before");
}
SolutionSet paretoFront = SolutionSetUtils.getFromFile(approxTrueParetoFrontPath);
if (paretoFront.size() == 0) {
throw new IllegalArgumentException("The Approximate Pareto-front cannot be empty");
}
int numberOfObjectives = paretoFront.get(0).getNumberOfObjectives();
Problem p = new FakeProblem(numberOfObjectives);
QualityIndicator qi = new QualityIndicator(p, approxTrueParetoFrontPath);
for (String file : files) {
generateQualityIndicators(qi, folder, file, paretoFront, totalOfFiles);
}
}
/**
* Performs the operation
*
* @param object Object representing a SolutionSet.
* @return the best solution found
*/
public Object execute(Object object) {
SolutionSet solutionSet = (SolutionSet) object;
if (solutionSet.size() == 0) {
return null;
}
int worstSolution;
worstSolution = 0;
for (int i = 1; i < solutionSet.size(); i++) {
if (comparator_.compare(solutionSet.get(i), solutionSet.get(worstSolution)) > 0)
worstSolution = i;
} // for
return worstSolution;
} // Execute
/**
* Returns a matrix with distances between solutions in a <code>SolutionSet</code>.
*
* @param solutionSet The <code>SolutionSet</code>.
* @return a matrix with distances.
*/
public double[][] distanceMatrix(SolutionSet solutionSet) {
Solution solutionI, solutionJ;
// The matrix of distances
double[][] distance = new double[solutionSet.size()][solutionSet.size()];
// -> Calculate the distances
for (int i = 0; i < solutionSet.size(); i++) {
distance[i][i] = 0.0;
solutionI = solutionSet.get(i);
for (int j = i + 1; j < solutionSet.size(); j++) {
solutionJ = solutionSet.get(j);
distance[i][j] = this.distanceBetweenObjectives(solutionI, solutionJ);
distance[j][i] = distance[i][j];
} // for
} // for
// ->Return the matrix of distances
return distance;
} // distanceMatrix
/**
* Apply a mutation operator to some particles in the swarm
*
* @throws jmetal.util.JMException
*/
private void mopsoMutation(int actualIteration, int totalIterations) throws JMException {
for (int i = 0; i < particles_.size(); i++) {
if ((i % 6) == 0) polynomialMutation_.execute(particles_.get(i));
// if (i % 3 == 0) { //particles_ mutated with a non-uniform mutation %3
// nonUniformMutation_.execute(particles_.get(i));
// } else if (i % 3 == 1) { //particles_ mutated with a uniform mutation operator
// uniformMutation_.execute(particles_.get(i));
// } else //particles_ without mutation
// ;
}
} // mopsoMutation
/**
* Update the speed of each particle
*
* @throws JMException
*/
private void computeSpeed(int iter, int miter) throws JMException, IOException {
double r1, r2, W, C1, C2;
double wmax, wmin, deltaMax, deltaMin;
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 = PseudoRandom.randInt(0, leaders_.size() - 1);
int pos2 = PseudoRandom.randInt(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
}
// Juanjo, si usamos esto en lugar de lo de antes va de pena
// bestGlobal = new XReal(leaders_.get(0)) ;
r1 = PseudoRandom.randDouble(r1Min_, r1Max_);
r2 = PseudoRandom.randDouble(r2Min_, r2Max_);
C1 = PseudoRandom.randDouble(C1Min_, C1Max_);
C2 = PseudoRandom.randDouble(C2Min_, C2Max_);
W = PseudoRandom.randDouble(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_, // [var],
deltaMin_, // [var],
var,
i);
}
}
} // computeSpeed
/**
* Returns the minimum distance from a <code>Solution</code> to a <code>
* SolutionSet according to the variable values</code>.
*
* @param solution The <code>Solution</code>.
* @param solutionSet The <code>SolutionSet</code>.
* @return The minimum distance between solution and the set.
* @throws JMException
*/
public double distanceToSolutionSetInSolutionSpace(Solution solution, SolutionSet solutionSet)
throws JMException {
// At start point the distance is the max
double distance = Double.MAX_VALUE;
// found the min distance respect to population
for (int i = 0; i < solutionSet.size(); i++) {
double aux = this.distanceBetweenSolutions(solution, solutionSet.get(i));
if (aux < distance) distance = aux;
} // for
// ->Return the best distance
return distance;
} // distanceToSolutionSetInSolutionSpace
/**
* Update the position of each particle
*
* @throws jmetal.util.JMException
*/
private void computeNewPositions() throws JMException {
for (int i = 0; i < swarmSize_; i++) {
XReal particle = new XReal(particles_.get(i));
for (int var = 0; var < particle.getNumberOfDecisionVariables(); var++) {
particle.setValue(var, particle.getValue(var) + speed_[i][var]);
if (particle.getValue(var) < problem_.getLowerLimit(var)) {
particle.setValue(var, problem_.getLowerLimit(var));
speed_[i][var] = speed_[i][var] * ChVel1_; //
}
if (particle.getValue(var) > problem_.getUpperLimit(var)) {
particle.setValue(var, problem_.getUpperLimit(var));
speed_[i][var] = speed_[i][var] * ChVel2_; //
}
}
}
} // computeNewPositions
private void computeNewPositions_parallel(int i) throws JMException {
//Variable[] particle = particles_.get(i).getDecisionVariables();
XReal particle = new XReal(particles_.get(i)) ;
//particle.move(speed_[i]);
for (int var = 0; var < particle.size(); var++) {
particle.setValue(var, particle.getValue(var) + speed_[i][var]) ;
if (particle.getValue(var) < problem_.getLowerLimit(var)) {
particle.setValue(var, problem_.getLowerLimit(var));
speed_[i][var] = speed_[i][var] * ChVel1_; //
}
if (particle.getValue(var) > problem_.getUpperLimit(var)) {
particle.setValue(var, problem_.getUpperLimit(var));
speed_[i][var] = speed_[i][var] * ChVel2_; //
}
}
}
/**
* Runs of the PESA2 algorithm.
*
* @return a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the
* algorithm execution
* @throws JMException
*/
public SolutionSet execute() throws JMException, ClassNotFoundException {
int archiveSize, bisections, maxEvaluations, evaluations, populationSize;
AdaptiveGridArchive archive;
SolutionSet solutionSet;
Operator crossover, mutation, selection;
// Read parameters
populationSize = ((Integer) (inputParameters_.get("populationSize"))).intValue();
archiveSize = ((Integer) (inputParameters_.get("archiveSize"))).intValue();
bisections = ((Integer) (inputParameters_.get("bisections"))).intValue();
maxEvaluations = ((Integer) (inputParameters_.get("maxEvaluations"))).intValue();
// Get the operators
crossover = operators_.get("crossover");
mutation = operators_.get("mutation");
// Initialize the variables
evaluations = 0;
archive = new AdaptiveGridArchive(archiveSize, bisections, problem_.getNumberOfObjectives());
solutionSet = new SolutionSet(populationSize);
HashMap parameters = null;
selection = new PESA2Selection(parameters);
// -> Create the initial individual and evaluate it and his constraints
for (int i = 0; i < populationSize; i++) {
Solution solution = new Solution(problem_);
problem_.evaluate(solution);
problem_.evaluateConstraints(solution);
solutionSet.add(solution);
}
// Incorporate non-dominated solution to the archive
for (int i = 0; i < solutionSet.size(); i++) {
archive.add(solutionSet.get(i)); // Only non dominated are accepted by
// the archive
}
// Clear the init solutionSet
solutionSet.clear();
// Iterations....
Solution[] parents = new Solution[2];
do {
// -> Create the offSpring solutionSet
while (solutionSet.size() < populationSize) {
parents[0] = (Solution) selection.execute(archive);
parents[1] = (Solution) selection.execute(archive);
Solution[] offSpring = (Solution[]) crossover.execute(parents);
mutation.execute(offSpring[0]);
problem_.evaluate(offSpring[0]);
problem_.evaluateConstraints(offSpring[0]);
evaluations++;
solutionSet.add(offSpring[0]);
}
for (int i = 0; i < solutionSet.size(); i++) archive.add(solutionSet.get(i));
// Clear the solutionSet
solutionSet.clear();
} while (evaluations < maxEvaluations);
// Return the solutionSet of non-dominated individual
return archive;
} // execute
/**
* Runs of the SMPSO algorithm.
*
* @return a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the
* algorithm execution
* @throws JMException
*/
@Override
public SolutionSet execute() throws JMException, ClassNotFoundException {
initParams();
success_ = false;
// ->Step 1 (and 3) Create the initial population and evaluate
for (int i = 0; i < swarmSize_; i++) {
Solution particle = new Solution(problem_);
problem_.evaluate(particle);
problem_.evaluateConstraints(particle);
particles_.add(particle);
}
// -> Step2. Initialize the speed_ of each particle to 0
for (int i = 0; i < swarmSize_; i++) {
for (int j = 0; j < problem_.getNumberOfVariables(); j++) {
speed_[i][j] = 0.0;
}
}
// Step4 and 5
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
leaders_.add(particle);
}
// -> Step 6. Initialize the memory of each particle
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
best_[i] = particle;
}
// Crowding the leaders_
// distance_.crowdingDistanceAssignment(leaders_, problem_.getNumberOfObjectives());
leaders_.computeHVContribution();
// -> Step 7. Iterations ..
while (iteration_ < maxIterations_) {
System.out.println("Iteration: " + iteration_ + " times");
try {
// Compute the speed_
computeSpeed(iteration_, maxIterations_);
} catch (IOException ex) {
Logger.getLogger(SMPSOhv.class.getName()).log(Level.SEVERE, null, ex);
}
// Compute the new positions for the particles_
computeNewPositions();
// Mutate the particles_
mopsoMutation(iteration_, maxIterations_);
// Evaluate the new particles_ in new positions
for (int i = 0; i < particles_.size(); i++) {
Solution particle = particles_.get(i);
problem_.evaluate(particle);
problem_.evaluateConstraints(particle);
}
// Actualize the archive
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
leaders_.add(particle);
}
if (leaders_.size() < archiveSize_) leaders_.computeHVContribution();
// Actualize the memory of this particle
for (int i = 0; i < particles_.size(); i++) {
int flag = dominance_.compare(particles_.get(i), best_[i]);
if (flag != 1) { // the new particle is best_ than the older remeber
Solution particle = new Solution(particles_.get(i));
best_[i] = particle;
}
}
iteration_++;
}
return this.leaders_;
} // execute
/**
* Assigns crowding distances to all solutions in a <code>SolutionSet</code>.
*
* @param solutionSet The <code>SolutionSet</code>.
* @param nObjs Number of objectives.
*/
public void crowdingDistanceAssignment(SolutionSet solutionSet, int nObjs) {
int size = solutionSet.size();
if (size == 0) return;
if (size == 1) {
solutionSet.get(0).setCrowdingDistance(Double.POSITIVE_INFINITY);
return;
} // if
if (size == 2) {
solutionSet.get(0).setCrowdingDistance(Double.POSITIVE_INFINITY);
solutionSet.get(1).setCrowdingDistance(Double.POSITIVE_INFINITY);
return;
} // if
// Use a new SolutionSet to evite alter original solutionSet
SolutionSet front = new SolutionSet(size);
for (int i = 0; i < size; i++) {
front.add(solutionSet.get(i));
}
for (int i = 0; i < size; i++) front.get(i).setCrowdingDistance(0.0);
double objetiveMaxn;
double objetiveMinn;
double distance;
for (int i = 0; i < nObjs; i++) {
// Sort the population by Obj n
front.sort(new ObjectiveComparator(i));
objetiveMinn = front.get(0).getObjective(i);
objetiveMaxn = front.get(front.size() - 1).getObjective(i);
// Set de crowding distance
front.get(0).setCrowdingDistance(Double.POSITIVE_INFINITY);
front.get(size - 1).setCrowdingDistance(Double.POSITIVE_INFINITY);
for (int j = 1; j < size - 1; j++) {
distance = front.get(j + 1).getObjective(i) - front.get(j - 1).getObjective(i);
distance = distance / (objetiveMaxn - objetiveMinn);
distance += front.get(j).getCrowdingDistance();
front.get(j).setCrowdingDistance(distance);
} // for
} // for
} // crowdingDistanceAssing
/**
* Execute the ElitistES algorithm
* @throws JMException
*/
public SolutionSet execute() throws JMException, ClassNotFoundException {
int maxEvaluations ;
int evaluations ;
SolutionSet population ;
SolutionSet offspringPopulation ;
Operator mutationOperator ;
Comparator comparator ;
comparator = new ObjectiveComparator(0) ; // Single objective comparator
// Read the params
maxEvaluations = ((Integer)this.getInputParameter("maxEvaluations")).intValue();
// Initialize the variables
population = new SolutionSet(mu_) ;
offspringPopulation = new SolutionSet(mu_ + lambda_) ;
evaluations = 0;
// Read the operators
mutationOperator = this.operators_.get("mutation");
System.out.println("(" + mu_ + " + " + lambda_+")ES") ;
// Create the parent population of mu solutions
Solution newIndividual;
for (int i = 0; i < mu_; i++) {
newIndividual = new Solution(problem_);
evaluations++;
population.add(newIndividual);
} //for
finish {
//
async {
for (int i = 0; i < mu_; i++) {
evaluate_internal(population, i, null);
}
}
}
// Main loop
int offsprings ;
offsprings = lambda_ / mu_ ;
while (evaluations < maxEvaluations) {
// STEP 1. Generate the mu+lambda population
for (int i = 0; i < mu_; i++) {
for (int j = 0; j < offsprings; j++) {
Solution offspring = new Solution(population.get(i)) ;
offspringPopulation.add(offspring) ;
evaluations ++ ;
} // for
} // for
finish {
//
async {
for (int i = 0; i < mu_*offsprings; i++) {
evaluate_internal(offspringPopulation, i, mutationOperator);
}
}
}
// STEP 2. Add the mu individuals to the offspring population
for (int i = 0 ; i < mu_; i++) {
offspringPopulation.add(population.get(i)) ;
} // for
population.clear() ;
// STEP 3. Sort the mu+lambda population
offspringPopulation.sort(comparator) ;
// STEP 4. Create the new mu population
for (int i = 0; i < mu_; i++)
population.add(offspringPopulation.get(i)) ;
System.out.println("Evaluation: " + evaluations + " Fitness: " +
population.get(0).getObjective(0)) ;
// STEP 6. Delete the mu+lambda population
offspringPopulation.clear() ;
} // while
// Return a population with the best individual
SolutionSet resultPopulation = new SolutionSet(1) ;
resultPopulation.add(population.get(0)) ;
} // execute
private void evaluate_internal(SolutionSet s, int i, Operator mutationOperator) throws JMException, ClassNotFoundException {
Solution sol = s.get(i);
if(mutationOperator != null) mutationOperator.execute(sol);
/**
* Runs of the SMPSO algorithm.
*
* @return a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the
* algorithm execution
* @throws jmetal.util.JMException
*/
public SolutionSet execute() throws JMException, ClassNotFoundException {
initParams();
success_ = false;
// ->Step 1 (and 3) Create the initial population and evaluate
for (int i = 0; i < swarmSize_; i++) {
Solution particle = new Solution(problem_);
problem_.evaluate(particle);
problem_.evaluateConstraints(particle);
particles_.add(particle);
}
// -> Step2. Initialize the speed_ of each particle to 0
for (int i = 0; i < swarmSize_; i++) {
for (int j = 0; j < problem_.getNumberOfVariables(); j++) {
speed_[i][j] = 0.0;
}
}
// Step4 and 5
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
leaders_.add(particle);
}
// -> Step 6. Initialize the memory of each particle
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
best_[i] = particle;
}
// Crowding the leaders_
distance_.crowdingDistanceAssignment(leaders_, problem_.getNumberOfObjectives());
// -> Step 7. Iterations ..
while (iteration_ < maxIterations_) {
try {
// Compute the speed_
computeSpeed(iteration_, maxIterations_);
} catch (IOException ex) {
Logger.getLogger(SMPSO.class.getName()).log(Level.SEVERE, null, ex);
}
// Compute the new positions for the particles_
computeNewPositions();
// Mutate the particles_
mopsoMutation(iteration_, maxIterations_);
// Evaluate the new particles_ in new positions
for (int i = 0; i < particles_.size(); i++) {
Solution particle = particles_.get(i);
problem_.evaluate(particle);
problem_.evaluateConstraints(particle);
}
// Actualize the archive
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
leaders_.add(particle);
}
// Actualize the memory of this particle
for (int i = 0; i < particles_.size(); i++) {
int flag = dominance_.compare(particles_.get(i), best_[i]);
if (flag != 1) { // the new particle is best_ than the older remeber
Solution particle = new Solution(particles_.get(i));
best_[i] = particle;
}
}
// Assign crowding distance to the leaders_
distance_.crowdingDistanceAssignment(leaders_, problem_.getNumberOfObjectives());
iteration_++;
/*
if ((iteration_ % 1) == 0) {
leaders_.printObjectivesOfValidSolutionsToFile("FUNV"+iteration_) ;
leaders_.printObjectivesToFile("FUN"+iteration_) ;
leaders_.printVariablesToFile("VAR"+iteration_) ;
}
*/
}
// leaders_.printObjectivesOfValidSolutionsToFile("FUNV.SMPSO") ;
leaders_.printFeasibleFUN("FUN_SMPSO");
return this.leaders_;
} // execute
/**
* Update the speed of each particle
* @throws JMException
*/
private void computeSpeed(int iter, int miter) throws JMException, IOException {
double r1, r2 ;
//double W ;
double C1, C2;
double wmax, wmin, deltaMax, deltaMin;
XReal bestGlobal;
bestGlobal = new XReal(globalBest_) ;
for (int i = 0; i < particlesSize_; i++) {
XReal particle = new XReal(particles_.get(i)) ;
XReal bestParticle = new XReal(localBest_[i]) ;
//int bestIndividual = (Integer)findBestSolution_.execute(particles_) ;
C1Max_ = 2.5;
C1Min_ = 1.5;
C2Max_ = 2.5;
C2Min_ = 1.5;
r1 = PseudoRandom.randDouble(r1Min_, r1Max_);
r2 = PseudoRandom.randDouble(r2Min_, r2Max_);
C1 = PseudoRandom.randDouble(C1Min_, C1Max_);
C2 = PseudoRandom.randDouble(C2Min_, C2Max_);
//W = PseudoRandom.randDouble(WMin_, WMax_);
//
WMax_ = 0.9;
WMin_ = 0.9;
ChVel1_ = 1.0;
ChVel2_ = 1.0;
C1 = 2.5 ;
C2 = 1.5 ;
wmax = WMax_;
wmin = WMin_;
/*
for (int var = 0; var < particle.size(); 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],
var,
i);
}
*/
C1 = 1.5 ;
C2 = 1.5 ;
double W = 0.9 ;
for (int var = 0; var < particle.size(); var++) {
//Computing the velocity of this particle
speed_[i][var] = inertiaWeight(iter, miter, wmax, wmin) * speed_[i][var] +
C1 * r1 * (bestParticle.getValue(var) - particle.getValue(var)) +
C2 * r2 * (bestGlobal.getValue(var) - particle.getValue(var)) ;
}
}
} // computeSpeed
private void evaluate_parallel_internal(int i) throws JMException, ClassNotFoundException {
Solution particle = particles_.get(i);
problem_.evaluate(particle);
}
/**
* Runs of the SMPSO algorithm.
* @return a <code>SolutionSet</code> that is a set of non dominated solutions
* as a result of the algorithm execution
* @throws JMException
*/
public SolutionSet execute() throws JMException, ClassNotFoundException {
initParams();
success_ = false;
globalBest_ = null ;
//->Step 1 (and 3) Create the initial population and evaluate
for (int i = 0; i < particlesSize_; i++) {
Solution particle = new Solution(problem_);
problem_.evaluate(particle);
evaluations_ ++ ;
particles_.add(particle);
if ((globalBest_ == null) || (particle.getObjective(0) < globalBest_.getObjective(0)))
globalBest_ = new Solution(particle) ;
}
//-> Step2. Initialize the speed_ of each particle to 0
for (int i = 0; i < particlesSize_; i++) {
for (int j = 0; j < problem_.getNumberOfVariables(); j++) {
speed_[i][j] = 0.0;
}
}
//-> Step 6. Initialize the memory of each particle
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
localBest_[i] = particle;
}
//-> Step 7. Iterations ..
while (iteration_ < maxIterations_) {
int bestIndividual = (Integer)findBestSolution_.execute(particles_) ;
try {
//Compute the speed_
computeSpeed(iteration_, maxIterations_);
} catch (IOException ex) {
Logger.getLogger(PSO.class.getName()).log(Level.SEVERE, null, ex);
}
//Compute the new positions for the particles_
computeNewPositions();
//Mutate the particles_
//mopsoMutation(iteration_, maxIterations_);
//Evaluate the new particles_ in new positions
evaluations_ += particles_.size();
finish { //
async {
for (int i = 0; i < particles_.size(); i++) {
evaluate_parallel_internal(i);
}
}
}
//Actualize the memory of this particle
for (int i = 0; i < particles_.size(); i++) {
//int flag = comparator_.compare(particles_.get(i), localBest_[i]);
//if (flag < 0) { // the new particle is best_ than the older remember
if ((particles_.get(i).getObjective(0) < localBest_[i].getObjective(0))) {
Solution particle = new Solution(particles_.get(i));
localBest_[i] = particle;
} // if
if ((particles_.get(i).getObjective(0) < globalBest_.getObjective(0))) {
Solution particle = new Solution(particles_.get(i));
globalBest_ = particle;
} // if
}
iteration_++;
}
// Return a population with the best individual
SolutionSet resultPopulation = new SolutionSet(1) ;
resultPopulation.add(particles_.get((Integer)findBestSolution_.execute(particles_))) ;
return resultPopulation ;
} // execute
/** @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();
}
}
/**
* Runs the NSGA-II algorithm.
*
* @return a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the
* algorithm execution
* @throws JMException
*/
public SolutionSet execute() throws JMException, ClassNotFoundException {
int populationSize;
int maxEvaluations;
int evaluations;
QualityIndicator indicators; // QualityIndicator object
int requiredEvaluations; // Use in the example of use of the
// indicators object (see below)
SolutionSet population;
SolutionSet offspringPopulation;
SolutionSet union;
Distance distance = new Distance();
// Read the parameters
populationSize = ((Integer) getInputParameter("populationSize")).intValue();
maxEvaluations = ((Integer) getInputParameter("maxEvaluations")).intValue();
indicators = (QualityIndicator) getInputParameter("indicators");
// Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
// Read the operators
List<Operator> mutationOperators = new ArrayList<Operator>();
mutationOperators.add(operators_.get("oneNoteMutation"));
mutationOperators.add(operators_.get("harmonyNoteToPitch"));
mutationOperators.add(operators_.get("swapHarmonyNotes"));
mutationOperators.add(operators_.get("melodyNoteToHarmonyNote"));
mutationOperators.add(operators_.get("pitchSpaceMutation"));
Operator crossoverOperator = operators_.get("crossover");
Operator selectionOperator = operators_.get("selection");
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++) {
newSolution = new MusicSolution(problem_);
problem_.evaluate(newSolution);
problem_.evaluateConstraints(newSolution);
evaluations++;
population.add(newSolution);
}
int changeCount = 0;
// Generations ...
// while ((evaluations < maxEvaluations) && changeCount < 10 *
// populationSize) {
while ((evaluations < maxEvaluations)) {
List<Solution> copySolutions = copyList(population);
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 2); i++) {
if (evaluations < maxEvaluations) {
// obtain parents
parents[0] = (Solution) selectionOperator.execute(population);
parents[1] = (Solution) selectionOperator.execute(population);
Solution[] offSpring = (Solution[]) crossoverOperator.execute(parents);
for (Operator operator : mutationOperators) {
operator.execute(offSpring[0]);
operator.execute(offSpring[1]);
}
if (decorated) {
decorator.decorate(offSpring[0]);
decorator.decorate(offSpring[1]);
}
problem_.evaluate(offSpring[0]);
problem_.evaluateConstraints(offSpring[0]);
problem_.evaluate(offSpring[1]);
problem_.evaluateConstraints(offSpring[1]);
offspringPopulation.add(offSpring[0]);
offspringPopulation.add(offSpring[1]);
evaluations += 2;
}
}
// 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_.getNumberOfObjectives());
// 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_.getNumberOfObjectives());
front.sort(new jmetal.util.comparators.CrowdingComparator());
for (int k = 0; k < remain; k++) {
population.add(front.get(k));
}
remain = 0;
}
// This piece of code shows how to use the indicator object into the
// code
// of NSGA-II. In particular, it finds the number of evaluations
// required
// by the algorithm to obtain a Pareto front with a hypervolume
// higher
// than the hypervolume of the true Pareto front.
if ((indicators != null) && (requiredEvaluations == 0)) {
double HV = indicators.getHypervolume(population);
double p = indicators.getTrueParetoFrontHypervolume();
if (HV >= (0.98 * indicators.getTrueParetoFrontHypervolume())) {
requiredEvaluations = evaluations;
}
}
// check changes pareto front
// SolutionSet paretoFront = ranking.getSubfront(0);
List<Solution> frontSolutions = copyList(population);
boolean changed = hasPopulationChanged(copySolutions, frontSolutions);
LOGGER.fine(
"Population changed: "
+ changed
+ ", evaluations: "
+ evaluations
+ ", change count:"
+ changeCount);
if (changed) {
changeCount = 0;
} else {
changeCount++;
}
}
// Return as output parameter the required evaluations
setOutputParameter("evaluations", requiredEvaluations);
// Return the first non-dominated front
Ranking ranking = new Ranking(population);
return ranking.getSubfront(0);
}