Move all AbstractEAComparators to actually use that as base object
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@ -19,7 +19,7 @@ import java.util.Comparator;
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* @see AbstractEAIndividual#isDominatingFitness(double[], double[])
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*/
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@eva2.util.annotation.Description(value = "A comparator class for general EA individuals. Compares individuals based on their fitness in context of minimization.")
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public class EAIndividualComparator implements Comparator<Object>, Serializable {
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public class EAIndividualComparator implements Comparator<AbstractEAIndividual>, Serializable {
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// flag whether a data field should be used.
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private String indyDataKey = "";
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private int fitCriterion = -1;
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@ -122,7 +122,7 @@ public class EAIndividualComparator implements Comparator<Object>, Serializable
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* @return -1 if the first is dominant, 1 if the second is dominant, otherwise 0
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*/
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@Override
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public int compare(Object o1, Object o2) {
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public int compare(AbstractEAIndividual o1, AbstractEAIndividual o2) {
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boolean o1domO2, o2domO1;
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if (preferFeasible) { // check constraint violation first?
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@ -9,7 +9,7 @@ import java.util.Comparator;
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* Compare two AbstractEAIndividuals by their distance to a reference individual.
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* Usable to sort by a distance.
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*/
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public class IndividualDistanceComparator implements Comparator<Object>, Serializable {
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public class IndividualDistanceComparator implements Comparator<AbstractEAIndividual>, Serializable {
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private AbstractEAIndividual refIndy = null;
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private InterfaceDistanceMetric distMetric = null;
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@ -30,9 +30,9 @@ public class IndividualDistanceComparator implements Comparator<Object>, Seriali
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}
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@Override
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public int compare(Object o1, Object o2) {
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double d1 = distMetric.distance((AbstractEAIndividual) o1, refIndy);
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double d2 = distMetric.distance((AbstractEAIndividual) o2, refIndy);
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public int compare(AbstractEAIndividual o1, AbstractEAIndividual o2) {
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double d1 = distMetric.distance(o1, refIndy);
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double d2 = distMetric.distance(o2, refIndy);
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if (d1 == d2) {
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return 0;
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@ -10,7 +10,7 @@ import java.util.Comparator;
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*
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* @author mkron
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*/
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public class IndividualWeightedFitnessComparator implements Comparator<Object>, Serializable {
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public class IndividualWeightedFitnessComparator implements Comparator<AbstractEAIndividual>, Serializable {
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/**
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* Generated serial version identifier
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*/
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@ -75,9 +75,9 @@ public class IndividualWeightedFitnessComparator implements Comparator<Object>,
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* @see java.util.Comparator#compare(java.lang.Object, java.lang.Object)
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*/
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@Override
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public int compare(Object o1, Object o2) {
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double[] f1 = ((AbstractEAIndividual) o1).getFitness();
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double[] f2 = ((AbstractEAIndividual) o2).getFitness();
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public int compare(AbstractEAIndividual o1, AbstractEAIndividual o2) {
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double[] f1 = o1.getFitness();
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double[] f2 = o2.getFitness();
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double score1 = calcScore(f1);
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double score2 = calcScore(f2);
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@ -52,7 +52,7 @@ public class ArchivingNSGAIISMeasure extends ArchivingNSGAII {
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public void calculateCrowdingDistance(Population front) {
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Object[] frontArray = front.toArray();
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AbstractEAIndividual[] frontArray = front.toArray(new AbstractEAIndividual[front.size()]);
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boolean[] assigned = new boolean[frontArray.length];
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double[] v = new double[frontArray.length];
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@ -68,8 +68,8 @@ public class ArchivingNSGAIISMeasure extends ArchivingNSGAII {
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Arrays.sort(frontArray, new EAIndividualComparator(0));
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((AbstractEAIndividual) frontArray[0]).putData("HyperCube", Double.MAX_VALUE); //die beiden aussen bekommen maximal wert als measure
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((AbstractEAIndividual) frontArray[frontArray.length - 1]).putData("HyperCube", Double.MAX_VALUE);
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frontArray[0].putData("HyperCube", Double.MAX_VALUE); //die beiden aussen bekommen maximal wert als measure
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frontArray[frontArray.length - 1].putData("HyperCube", Double.MAX_VALUE);
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v[0] = Double.MAX_VALUE;
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v[frontArray.length - 1] = Double.MAX_VALUE;
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@ -1038,7 +1038,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
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* @param comparator indicate whether constraints should be regarded
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* @return The index of the best (worst) individual.
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*/
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public int getIndexOfBestOrWorstIndividual(boolean bBest, Comparator<Object> comparator) {
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public int getIndexOfBestOrWorstIndividual(boolean bBest, Comparator<AbstractEAIndividual> comparator) {
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ArrayList<?> sorted = getSorted(comparator);
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if (bBest) {
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return indexOf(sorted.get(0));
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@ -1051,7 +1051,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
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return getIndexOfBestOrWorstIndividual(true, comparator);
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}
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public AbstractEAIndividual getBestEAIndividual(Comparator<Object> comparator) {
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public AbstractEAIndividual getBestEAIndividual(Comparator<AbstractEAIndividual> comparator) {
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int index = getIndexOfBestOrWorstIndividual(true, comparator);
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return getEAIndividual(index);
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}
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@ -1199,7 +1199,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
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* fitness first
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* @see #getSortedNIndividuals(int, boolean, Population, Comparator)
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*/
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public Population getSortedBestFirst(Comparator<Object> comp) {
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public Population getSortedBestFirst(Comparator<AbstractEAIndividual> comp) {
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Population result = this.cloneWithoutInds();
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getSortedNIndividuals(size(), true, result, comp);
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result.synchSize();
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@ -1219,7 +1219,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
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* @param comp the Comparator to use with individuals
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* @param res The m sorted best or worst individuals, where m <= n (will be added to res)
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*/
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public void getSortedNIndividuals(int n, boolean bBestOrWorst, Population res, Comparator<Object> comp) {
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public void getSortedNIndividuals(int n, boolean bBestOrWorst, Population res, Comparator<AbstractEAIndividual> comp) {
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if ((n < 0) || (n > super.size())) {
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// this may happen, treat it gracefully
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//System.err.println("invalid request to getSortedNIndividuals: n="+n + ", size is " + super.size());
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@ -1291,7 +1291,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
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* on AbstractEAIndividual instances.
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* @return
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*/
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protected ArrayList<AbstractEAIndividual> sortBy(Comparator<Object> comp) {
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protected ArrayList<AbstractEAIndividual> sortBy(Comparator<AbstractEAIndividual> comp) {
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if (super.isEmpty()) {
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return new ArrayList<>();
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}
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@ -1320,7 +1320,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
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* @param comp The comparator
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* @return
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*/
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public ArrayList<AbstractEAIndividual> getSorted(Comparator<Object> comp) {
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public ArrayList<AbstractEAIndividual> getSorted(Comparator<AbstractEAIndividual> comp) {
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if (!comp.equals(lastSortingComparator) || (sortedArr == null) || (super.modCount != lastQModCount)) {
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ArrayList<AbstractEAIndividual> sArr = sortBy(comp);
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if (sortedArr == null) {
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@ -1340,7 +1340,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
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*
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* @see #getSorted(java.util.Comparator)
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*/
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public Population getSortedPop(Comparator<Object> comp) {
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public Population getSortedPop(Comparator<AbstractEAIndividual> comp) {
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Population pop = this.cloneWithoutInds();
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ArrayList<AbstractEAIndividual> sortedIndies = getSorted(comp);
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pop.addAll(sortedIndies);
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@ -44,7 +44,7 @@ import java.util.Vector;
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public class ParticleSwarmOptimization extends AbstractOptimizer implements java.io.Serializable, InterfaceAdditionalPopulationInformer {
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public enum PSOType { Inertness, Constriction }
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Object[] sortedPop = null;
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AbstractEAIndividual[] sortedPop = null;
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protected AbstractEAIndividual bestIndividual = null;
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protected boolean checkRange = true;
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protected boolean checkSpeedLimit = false;
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@ -1299,7 +1299,7 @@ public class ParticleSwarmOptimization extends AbstractOptimizer implements java
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}
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}
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if ((topology == PSOTopology.multiSwarm) || (topology == PSOTopology.tree)) {
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sortedPop = pop.toArray();
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sortedPop = pop.toArray(new AbstractEAIndividual[pop.size()]);
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if ((topology == PSOTopology.multiSwarm) || (treeStruct >= 2)) {
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Arrays.sort(sortedPop, new EAIndividualComparator());
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} else {
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