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Refactoring of ABC.

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This commit is contained in:
Fabian Becker 2014-11-23 19:18:05 +01:00
parent 510170be50
commit 801c3513e7
2 changed files with 49 additions and 43 deletions
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4
src/eva2/optimization/population/Population.java
View File

@ -711,8 +711,8 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
}
this.historyList.add((AbstractEAIndividual) this.getBestEAIndividual().clone());
}
for (Object individual : this) {
((AbstractEAIndividual) individual).incrAge();
for (AbstractEAIndividual individual : this) {
individual.incrAge();
}
this.generationCount++;
firePropertyChangedEvent(NEXT_GENERATION_PERFORMED);

88
src/eva2/optimization/strategies/ArtificialBeeColony.java
View File

@ -11,6 +11,7 @@ import eva2.util.annotation.Description;
import eva2.util.annotation.Parameter;
import java.io.Serializable;
import java.util.logging.Logger;
/**
* Artificial Bee Colony
@ -19,6 +20,7 @@ import java.io.Serializable;
*/
@Description("Artificial Bee Colony")
public class ArtificialBeeColony extends AbstractOptimizer implements Serializable {
private static final Logger LOGGER = Logger.getLogger(ArtificialBeeColony.class.getName());
/**
* A food source which could not be improved through "maxTrials" trials is abandoned by its employed bee.
@ -104,6 +106,7 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
* Send onlooker bees to food sources based on fitness proportional probability
*/
sendOnlookerBees();
this.population.incrFunctionCallsBy(this.population.size());
/**
* Remember best Individual
@ -126,8 +129,8 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
* in the algorithm itself (for ABC) so we have to -1 all ages.
*/
this.population.incrGeneration();
for (Object individual : this.population) {
((AbstractEAIndividual) individual).setAge(((AbstractEAIndividual) individual).getAge() - 1);
for (AbstractEAIndividual individual : this.population) {
individual.setAge(individual.getAge() - 1);
}
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
@ -136,6 +139,7 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
protected void sendScoutBees() {
AbstractEAIndividual oldestIndy = getOldestIndividual();
if (oldestIndy.getAge() > this.maxTrials) {
LOGGER.finer("Scout bee generated.");
oldestIndy.initialize(this.optimizationProblem);
this.optimizationProblem.evaluate(oldestIndy);
this.population.incrFunctionCalls();
@ -153,7 +157,7 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
/**
* Choose a food source depending on its probability to be chosen. The probability
* is proportional to the
* is proportional to the inverse fitness
*/
double pI = getFitnessProportion(this.population.getEAIndividual(i))/sumFitness;
if (r < pI) {
@ -162,22 +166,7 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
// The current individual to compare to
AbstractEAIndividual indy = this.population.getEAIndividual(i);
// The new individual which we are generating
AbstractEAIndividual newIndividual = (AbstractEAIndividual) indy.getClone();
double[] indyDoubleData = ((InterfaceDataTypeDouble) newIndividual).getDoubleData();
int randomParam = RNG.randomInt(0, indyDoubleData.length - 1);
int neighbour = RNG.randomIntWithout(i, 0, this.population.size() - 1);
double[] randomIndy = ((InterfaceDataTypeDouble) this.population.get(neighbour)).getDoubleData();
double phi = RNG.randomDouble(-1.0, 1.0);
indyDoubleData[randomParam] += (indyDoubleData[randomParam] - randomIndy[randomParam]) * phi;
// Make sure new indy is in range
Mathematics.projectToRange(indyDoubleData, ((InterfaceDataTypeDouble) newIndividual).getDoubleRange());
((InterfaceDataTypeDouble) newIndividual).setDoubleGenotype(indyDoubleData);
this.optimizationProblem.evaluate(newIndividual);
this.population.incrFunctionCalls();
AbstractEAIndividual newIndividual = moveOnlookerBee(indy, i);
if (newIndividual.getFitness(0) < indy.getFitness(0)) {
newIndividual.setAge(1);
@ -200,21 +189,7 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
// The current individual to compare to
AbstractEAIndividual indy = this.population.getEAIndividual(i);
// The new individual which we are generating
AbstractEAIndividual newIndividual = (AbstractEAIndividual) indy.getClone();
double[] indyDoubleData = ((InterfaceDataTypeDouble) newIndividual).getDoubleData();
int neighbour = RNG.randomIntWithout(i, 0, this.population.size() - 1);
double[] randomIndy = ((InterfaceDataTypeDouble) this.population.get(neighbour)).getDoubleData();
int randomParam = RNG.randomInt(0, indyDoubleData.length - 1);
double r = RNG.randomDouble(-1.0, 1.0);
indyDoubleData[randomParam] += (indyDoubleData[randomParam] - randomIndy[randomParam]) * r;
// Make sure new indy is in range
Mathematics.projectToRange(indyDoubleData, ((InterfaceDataTypeDouble) newIndividual).getDoubleRange());
((InterfaceDataTypeDouble) newIndividual).setDoubleGenotype(indyDoubleData);
this.optimizationProblem.evaluate(newIndividual);
AbstractEAIndividual newIndividual = moveEmployedBee(indy, i);
if (newIndividual.getFitness(0) < indy.getFitness(0)) {
newIndividual.setAge(0);
@ -226,10 +201,47 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
}
}
/**
*
* @param baseIndividual
* @param index
* @return
*/
protected AbstractEAIndividual moveEmployedBee(AbstractEAIndividual baseIndividual, int index) {
// The new individual which we are generating
AbstractEAIndividual newIndividual = (AbstractEAIndividual) baseIndividual.getClone();
double[] indyDoubleData = ((InterfaceDataTypeDouble) newIndividual).getDoubleData();
int randomParam = RNG.randomInt(0, indyDoubleData.length - 1);
int neighbour = RNG.randomIntWithout(index, 0, this.population.size() - 1);
double[] randomIndy = ((InterfaceDataTypeDouble) this.population.get(neighbour)).getDoubleData();
double phi = RNG.randomDouble(-1.0, 1.0);
indyDoubleData[randomParam] += phi * (indyDoubleData[randomParam] - randomIndy[randomParam]);
// Make sure new indy is in range
Mathematics.projectToRange(indyDoubleData, ((InterfaceDataTypeDouble) newIndividual).getDoubleRange());
((InterfaceDataTypeDouble) newIndividual).setDoubleGenotype(indyDoubleData);
this.optimizationProblem.evaluate(newIndividual);
return newIndividual;
}
/**
* In the standard ABC the onlookers behave exaclty like the employed bees.
*
* @param baseIndividual
* @param index
* @return A new individual
*/
protected AbstractEAIndividual moveOnlookerBee(AbstractEAIndividual baseIndividual, int index) {
return moveEmployedBee(baseIndividual, index);
}
private AbstractEAIndividual getOldestIndividual() {
AbstractEAIndividual oldestIndy = this.population.getEAIndividual(0);
for(int i = 1; i < this.population.size(); i++) {
if (oldestIndy.getAge() > this.population.getEAIndividual(i).getAge()) {
if (oldestIndy.getAge() < this.population.getEAIndividual(i).getAge()) {
oldestIndy = this.population.getEAIndividual(i);
}
}
@ -249,12 +261,6 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
return this.toString();
}
@Override
public void setPopulation(Population pop) {
this.population = pop;
}
@Parameter(name = "trials", description = "Maximum number of trials until bee abandons the food source")
public void setMaxTrials(int maxTrials) {
this.maxTrials = maxTrials;