Cleanup & Logging
This commit is contained in:
Fabian Becker
parent
61ed76577e
commit
917c79089b
@ -210,7 +210,6 @@ public class MainFrame extends JFrame implements OptimizationStateListener {
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long wait = System.currentTimeMillis() - startTime;
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long wait = System.currentTimeMillis() - startTime;
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LOGGER.info("Loaded EvA2 in " + wait + "ms.");
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LOGGER.info("Loaded EvA2 in " + wait + "ms.");
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System.out.print(wait);
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if (!autorun) {
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if (!autorun) {
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if (!noSplash) {
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if (!noSplash) {
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try {
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try {
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@ -267,8 +267,7 @@ public class GenericObjectEditor implements PropertyEditor {
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return;
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return;
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}
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}
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Vector<String> v = null;
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Vector<String> v = new Vector<>(getClassesFromProperties(classType.getName(), null));
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v = new Vector<>(getClassesFromProperties(classType.getName(), null));
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try {
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try {
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if (v.size() > 0) {
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if (v.size() > 0) {
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@ -1092,7 +1092,7 @@ public class FunctionArea extends DArea implements Serializable {
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public void toggleLog() {
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public void toggleLog() {
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boolean setMinPos = false;
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boolean setMinPos = false;
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if (!log && !checkLoggable()) {
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if (!log && !checkLoggable()) {
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System.err.println("Warning: toggling logarithmics scale with values <= 0! Some points will not be displayed.");
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LOGGER.warning("Warning: toggling logarithmic scale with values <= 0! Some points will not be displayed.");
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setMinPos = true;
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setMinPos = true;
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}
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}
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if (log == false) {
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if (log == false) {
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@ -1,293 +0,0 @@
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package eva2.optimization.strategies;
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import eva2.optimization.individuals.AbstractEAIndividual;
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import eva2.optimization.individuals.InterfaceDataTypeDouble;
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import eva2.optimization.population.PopulationInterface;
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import eva2.optimization.population.InterfaceSolutionSet;
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import eva2.optimization.population.Population;
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import eva2.optimization.population.SolutionSet;
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import eva2.problems.InterfaceAdditionalPopulationInformer;
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import eva2.tools.math.Mathematics;
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import eva2.tools.math.RNG;
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import eva2.util.annotation.Description;
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import eva2.util.annotation.Parameter;
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import java.io.Serializable;
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import java.util.logging.Logger;
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/**
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* Artificial Bee Colony
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*
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* This optimizer implements the original ABC algorithm proposed by Karaboga et.al.
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*/
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@Description("Artificial Bee Colony")
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public class ArtificialBeeColony extends AbstractOptimizer implements InterfaceAdditionalPopulationInformer, Serializable {
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private static final Logger LOGGER = Logger.getLogger(ArtificialBeeColony.class.getName());
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/**
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* A food source which could not be improved through "maxTrials" trials is abandoned by its employed bee.
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*/
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protected int maxTrials = 100;
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protected int scoutCount = 0;
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protected AbstractEAIndividual bestIndividual;
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public ArtificialBeeColony() {
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}
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public ArtificialBeeColony(ArtificialBeeColony copy) {
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this.population = (Population) copy.population.clone();
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this.maxTrials = copy.maxTrials;
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}
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@Override
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public Object clone() {
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return new ArtificialBeeColony(this);
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}
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@Override
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public String getName() {
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return "Artificial Bee Colony";
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}
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@Override
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public void initialize() {
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this.scoutCount = 0;
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this.population.setAutoAging(false);
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this.optimizationProblem.initializePopulation(this.population);
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this.evaluatePopulation(this.population);
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this.bestIndividual = this.population.getBestEAIndividual();
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this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
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}
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/**
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* This method will initialize the optimizer with a given population
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*
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* @param pop The initial population
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* @param reset If true the population is reset.
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*/
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@Override
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public void initializeByPopulation(Population pop, boolean reset) {
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this.population = (Population) pop.clone();
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// We handle aging ourselves
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this.population.setAutoAging(false);
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if (reset) {
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this.population.initialize();
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this.evaluatePopulation(this.population);
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this.bestIndividual = this.population.getBestEAIndividual();
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this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
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}
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}
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/**
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* This method will evaluate the current population using the given problem.
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*
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* @param population The population that is to be evaluated
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*/
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private void evaluatePopulation(Population population) {
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this.optimizationProblem.evaluate(population);
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population.incrGeneration();
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}
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protected double getFitnessProportion(AbstractEAIndividual indy) {
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double fitness = indy.getFitness(0);
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if (fitness >= 0) {
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fitness = 1.0 / (1.0 + fitness);
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} else {
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fitness = 1.0 + Math.abs(fitness);
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}
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return fitness;
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}
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@Override
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public void optimize() {
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/**
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* Sending employed bees
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*/
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sendEmployedBees();
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this.population.incrFunctionCallsBy(this.population.size());
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/**
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* Send onlooker bees to food sources based on fitness proportional probability
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*/
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sendOnlookerBees();
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this.population.incrFunctionCallsBy(this.population.size());
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/**
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* Remember best Individual
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*/
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memorizeBestSolution();
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/**
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* Send scout bee
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*/
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sendScoutBees();
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this.population.incrGeneration();
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this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
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}
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protected void memorizeBestSolution() {
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if (bestIndividual != null && bestIndividual.getFitness(0) < this.population.getBestEAIndividual().getFitness(0)) {
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bestIndividual = this.population.getBestEAIndividual();
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} else {
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bestIndividual = this.population.getBestEAIndividual();
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}
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}
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protected void sendScoutBees() {
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AbstractEAIndividual oldestIndy = getOldestIndividual();
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if (oldestIndy.getAge() > this.maxTrials) {
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LOGGER.finer("Scout bee generated.");
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this.scoutCount++;
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oldestIndy.initialize(this.optimizationProblem);
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this.optimizationProblem.evaluate(oldestIndy);
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this.population.incrFunctionCalls();
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}
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}
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protected void sendOnlookerBees() {
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int t = 0, i = 0;
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double sumFitness = 0.0;
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for (AbstractEAIndividual individual : this.population) {
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sumFitness += getFitnessProportion(individual);
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}
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while (t < this.population.size()) {
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double r = RNG.randomDouble();
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/**
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* Choose a food source depending on its probability to be chosen. The probability
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* is proportional to the inverse fitness
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*/
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double pI = getFitnessProportion(this.population.getEAIndividual(i))/sumFitness;
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if (r < pI) {
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t++;
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// The current individual to compare to
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AbstractEAIndividual indy = this.population.getEAIndividual(i);
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AbstractEAIndividual newIndividual = moveOnlookerBee(indy, i);
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if (newIndividual.getFitness(0) < indy.getFitness(0)) {
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newIndividual.setAge(1);
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this.population.replaceIndividualAt(i, newIndividual);
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} else {
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// Keep individual but increase the age
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indy.incrAge();
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}
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}
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i++;
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if (i == this.population.size()) {
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i = 0;
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}
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}
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}
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protected void sendEmployedBees() {
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for(int i = 0; i < this.population.size(); i++) {
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// The current individual to compare to
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AbstractEAIndividual indy = this.population.getEAIndividual(i);
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AbstractEAIndividual newIndividual = moveEmployedBee(indy, i);
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if (newIndividual.getFitness(0) < indy.getFitness(0)) {
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newIndividual.setAge(0);
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this.population.replaceIndividualAt(i, newIndividual);
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} else {
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// Keep individual but increase the age
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indy.incrAge();
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}
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}
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}
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/**
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*
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* @param baseIndividual
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* @param index
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* @return
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*/
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protected AbstractEAIndividual moveEmployedBee(AbstractEAIndividual baseIndividual, int index) {
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// The new individual which we are generating
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AbstractEAIndividual newIndividual = (AbstractEAIndividual) baseIndividual.getClone();
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double[] indyDoubleData = ((InterfaceDataTypeDouble) newIndividual).getDoubleData();
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int randomParam = RNG.randomInt(0, indyDoubleData.length - 1);
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int neighbour = RNG.randomIntWithout(index, 0, this.population.size() - 1);
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double[] randomIndy = ((InterfaceDataTypeDouble) this.population.get(neighbour)).getDoubleData();
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double phi = RNG.randomDouble(-1.0, 1.0);
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indyDoubleData[randomParam] += phi * (indyDoubleData[randomParam] - randomIndy[randomParam]);
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// Make sure new indy is in range
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Mathematics.projectToRange(indyDoubleData, ((InterfaceDataTypeDouble) newIndividual).getDoubleRange());
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((InterfaceDataTypeDouble) newIndividual).setDoubleGenotype(indyDoubleData);
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this.optimizationProblem.evaluate(newIndividual);
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return newIndividual;
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}
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/**
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* In the standard ABC the onlookers behave exactly like the employed bees.
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*
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* @param baseIndividual
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* @param index
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* @return A new individual
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*/
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protected AbstractEAIndividual moveOnlookerBee(AbstractEAIndividual baseIndividual, int index) {
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return moveEmployedBee(baseIndividual, index);
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}
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private AbstractEAIndividual getOldestIndividual() {
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AbstractEAIndividual oldestIndy = this.population.getEAIndividual(0);
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for(int i = 1; i < this.population.size(); i++) {
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if (oldestIndy.getAge() < this.population.getEAIndividual(i).getAge()) {
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oldestIndy = this.population.getEAIndividual(i);
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}
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}
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return oldestIndy;
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}
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@Override
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public InterfaceSolutionSet getAllSolutions() {
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/**
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* ToDo: This should somehow preserve the best found individual.
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*/
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Population solutions = (Population)this.population.clone();
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solutions.add(bestIndividual);
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return new SolutionSet(solutions);
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}
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@Override
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public String getStringRepresentation() {
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return this.toString();
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}
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@Parameter(name = "trials", description = "Maximum number of trials until bee abandons the food source")
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public void setMaxTrials(int maxTrials) {
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this.maxTrials = maxTrials;
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}
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public int getMaxTrials() {
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return maxTrials;
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}
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@Override
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public String[] getAdditionalDataHeader() {
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return new String[]{"scoutCount"};
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}
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@Override
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public String[] getAdditionalDataInfo() {
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return new String[]{"Number of re-initialized Bees (Scouts)"};
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}
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@Override
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public Object[] getAdditionalDataValue(PopulationInterface pop) {
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return new Object[]{scoutCount};
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}
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}
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