/** * After every {@link #interval} iteration, pick {@link #numberOfSentries a number of} random * entities from the given {@link Algorithm algorithm's} topology and compare their previous * fitness values with their current fitness values. An environment change is detected when the * difference between the previous and current fitness values are >= the specified {@link * #epsilon} value. * * @param algorithm used to get hold of topology of entities and number of iterations * @return true if a change has been detected, false otherwise */ @Override public <A extends HasTopology & Algorithm & HasNeighbourhood> boolean detect(A algorithm) { if ((AbstractAlgorithm.get().getIterations() % interval == 0) && (AbstractAlgorithm.get().getIterations() != 0)) { List all = Java.List_ArrayList().f(algorithm.getTopology()); for (int i = 0; i < numberOfSentries.getParameter(); i++) { // select random sentry entity int random = Rand.nextInt(all.size()); StandardParticle sentry = (StandardParticle) all.get(random); // check for change // double previousFitness = sentry.getFitness().getValue(); boolean detectedChange = false; if (sentry.getFitness().getClass().getName().matches("MinimisationFitness")) { Fitness previousFitness = sentry.getFitness(); sentry.calculateFitness(); Fitness currentFitness = sentry.getFitness(); if (Math.abs(previousFitness.getValue() - currentFitness.getValue()) >= epsilon) { detectedChange = true; } } else if (sentry.getFitness().getClass().getName().matches("StandardMOFitness")) { MOFitness previousFitness = (MOFitness) sentry.getFitness(); sentry.calculateFitness(); MOFitness currentFitness = (MOFitness) sentry.getFitness(); for (int k = 0; k < previousFitness.getDimension(); k++) if (Math.abs( previousFitness.getFitness(k).getValue() - currentFitness.getFitness(k).getValue()) >= epsilon) { detectedChange = true; break; } } if (detectedChange) { System.out.println("Detected a change"); return true; } // remove the selected element from the all list preventing it from being selected again all.remove(random); } } return false; }
/** {@inheritDoc} */ @Override public int compare(E o1, E o2) { SinglePopulationBasedAlgorithm populationBasedAlgorithm = (SinglePopulationBasedAlgorithm) AbstractAlgorithm.getAlgorithmList().index(0); MOOptimisationProblem problem = ((MOOptimisationProblem) populationBasedAlgorithm.getOptimisationProblem()); Particle p1 = (Particle) o1; Particle p2 = (Particle) o2; MOFitness fitness1 = ((MOFitness) problem.getFitness(p1.getBestPosition())); MOFitness fitness2 = ((MOFitness) problem.getFitness(p2.getBestPosition())); int value = fitness1.compareTo(fitness2); if (fitness1.compareTo(fitness2) == 0) { int random = Rand.nextInt(20); if (random > 10) value *= -1; } return value; }
/** * This is an Synchronous strategy: * * <ol> * <li>For all particles: * <ol> * <li>Update the particle velocity * <li>Update the particle position * </ol> * <li>For all particles: * <ol> * <li>Calculate the particle fitness * <li>For all particles in the current particle's neighbourhood: * <ol> * <li>Update the neighbourhood best * </ol> * </ol> * </ol> * * @see * net.sourceforge.cilib.PSO.IterationStrategy#performIteration(net.sourceforge.cilib.PSO.PSO) * @param pso The {@link PSO} to have an iteration applied. */ @Override public void performIteration(PSO pso) { Topology<Particle> topology = pso.getTopology(); for (Particle current : topology) { current.updateVelocity(); current.updatePosition(); // TODO: replace with visitor (will simplify particle interface) boundaryConstraint.enforce(current); } Problem problem = AbstractAlgorithm.getAlgorithmList().get(0).getOptimisationProblem(); for (Particle current : topology) { current.calculateFitness(); for (Particle other : topology.neighbourhood(current)) { Particle p1 = current.getNeighbourhoodBest().getClone(); Particle p2 = other.getNeighbourhoodBest().getClone(); OptimisationSolution s1 = new OptimisationSolution( p1.getCandidateSolution().getClone(), problem.getFitness(p1.getCandidateSolution().getClone())); OptimisationSolution s2 = new OptimisationSolution( p2.getCandidateSolution().getClone(), problem.getFitness(p2.getCandidateSolution().getClone())); MOFitness fitness1 = (MOFitness) s1.getFitness(); MOFitness fitness2 = (MOFitness) s2.getFitness(); // System.out.println("fitness1 = "); // for (int i=0; i < fitness1.getDimension(); i++) // System.out.println(fitness1.getFitness(i).getValue()); // // System.out.println("fitness2 = "); // for (int i=0; i < fitness2.getDimension(); i++) // System.out.println(fitness2.getFitness(i).getValue()); if (fitness1.compareTo(fitness2) > 0) { other.setNeighbourhoodBest(current); } } } }