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Implements ABC algorithm

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fixes #24
This commit is contained in:
Fabian Becker 2014-10-26 17:08:37 +01:00
parent 5bd11e4624
commit a8320cad76
1 changed files with 170 additions and 13 deletions
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183
src/eva2/optimization/strategies/ArtificialBeeColony.java
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@ -1,23 +1,32 @@
package eva2.optimization.strategies;
import eva2.optimization.go.InterfacePopulationChangedEventListener;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.individuals.InterfaceDataTypeDouble;
import eva2.optimization.population.InterfaceSolutionSet;
import eva2.optimization.population.Population;
import eva2.problems.AbstractOptimizationProblem;
import eva2.problems.F1Problem;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.optimization.population.SolutionSet;
import eva2.tools.math.Mathematics;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import eva2.util.annotation.Parameter;
import java.util.ArrayList;
import java.io.Serializable;
/**
* Artificial Bee Colony
*
* This optimizer implements the original ABC algorithm proposed by Karaboga et.al.
*/
@Description(value = "Artificial Bee Colony Optimizer")
public class ArtificialBeeColony extends AbstractOptimizer {
@Description("Artificial Bee Colony")
public class ArtificialBeeColony extends AbstractOptimizer implements Serializable {
protected AbstractOptimizationProblem optimizationProblem = new F1Problem();
protected Population population;
/**
* A food source which could not be improved through "maxTrials" trials is abandoned by its employed bee.
*/
@Parameter(name = "trials", description = "Maximum number of trials until bee abandons the food source")
protected int maxTrials = 100;
protected AbstractEAIndividual bestIndividual;
public ArtificialBeeColony() {
@ -25,7 +34,8 @@ public class ArtificialBeeColony extends AbstractOptimizer {
}
public ArtificialBeeColony(ArtificialBeeColony copy) {
this.population = (Population) copy.population.clone();
this.maxTrials = copy.maxTrials;
}
@Override
@ -40,7 +50,9 @@ public class ArtificialBeeColony extends AbstractOptimizer {
@Override
public void initialize() {
this.optimizationProblem.initializePopulation(this.population);
this.evaluatePopulation(this.population);
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
/**
@ -55,6 +67,7 @@ public class ArtificialBeeColony extends AbstractOptimizer {
if (reset) {
this.population.init();
this.evaluatePopulation(this.population);
this.bestIndividual = this.population.getBestEAIndividual();
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
}
@ -72,18 +85,162 @@ public class ArtificialBeeColony extends AbstractOptimizer {
@Override
public void optimize() {
/**
*
* bee.SendEmployedBees();
* bee.CalculateProbabilities();
* bee.SendOnlookerBees();
* bee.MemorizeBestSource();
* bee.SendScoutBees();
*/
/**
* Sending employed bees
*/
for(int i = 0; i < this.population.size(); i++) {
// 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.randomInt(0, this.population.size() - 1);
double[] randomIndy = ((InterfaceDataTypeDouble) this.population.get(neighbour)).getDoubleData();
double r = RNG.randomDouble();
indyDoubleData[randomParam] = indyDoubleData[randomParam] + (indyDoubleData[randomParam] - randomIndy[randomParam]) * (r - 0.5) * 2.0;
// Make sure new indy is in range
Mathematics.projectToRange(indyDoubleData, ((InterfaceDataTypeDouble) newIndividual).getDoubleRange());
((InterfaceDataTypeDouble) newIndividual).setDoubleGenotype(indyDoubleData);
this.optimizationProblem.evaluate(newIndividual);
if (newIndividual.getFitness(0) < indy.getFitness(0)) {
newIndividual.setAge(0);
this.population.replaceIndividualAt(i, newIndividual);
} else {
// Keep individual but increase the age
indy.incrAge();
}
}
/**
* Send onlooker bees to food sources based on fitness proportional probablity
*/
int t = 0, i = 0;
double maxFitness = this.population.getBestFitness()[0];
while (t < this.population.size()) {
double r = RNG.randomDouble();
/**
* Choose a food source depending on its probability to be chosen
*/
if (r < ((0.9 * (this.population.getEAIndividual(i).getFitness(0) / maxFitness)) + 0.1)) {
t++;
// 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.randomInt(0, this.population.size() - 1);
double[] randomIndy = ((InterfaceDataTypeDouble) this.population.get(neighbour)).getDoubleData();
r = RNG.randomDouble();
indyDoubleData[randomParam] = indyDoubleData[randomParam] + (indyDoubleData[randomParam] - randomIndy[randomParam]) * (r - 0.5) * 2.0;
// Make sure new indy is in range
Mathematics.projectToRange(indyDoubleData, ((InterfaceDataTypeDouble) newIndividual).getDoubleRange());
((InterfaceDataTypeDouble) newIndividual).setDoubleGenotype(indyDoubleData);
this.optimizationProblem.evaluate(newIndividual);
if (newIndividual.getFitness(0) < indy.getFitness(0)) {
newIndividual.setAge(1);
this.population.replaceIndividualAt(i, newIndividual);
} else {
// Keep individual but increase the age
indy.incrAge();
}
}
i++;
if (i == this.population.size()) {
i = 0;
}
}
/**
* Remember best Individual
*/
if (bestIndividual != null && bestIndividual.getFitness(0) < this.population.getBestEAIndividual().getFitness(0)) {
bestIndividual = this.population.getBestEAIndividual();
} else {
bestIndividual = this.population.getBestEAIndividual();
}
/**
* Send scout bee
*/
AbstractEAIndividual oldestIndy = getOldestIndividual();
if (oldestIndy.getAge() > this.maxTrials) {
oldestIndy.init(this.optimizationProblem);
this.optimizationProblem.evaluate(oldestIndy);
this.population.incrFunctionCalls();
}
this.population.incrFunctionCallsBy(this.population.size());
/**
* ToDo: This is ugly.
*
* incrGeneration increments the age of all indies. Age management however happens
* 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);
}
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
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()) {
oldestIndy = this.population.getEAIndividual(i);
}
}
return oldestIndy;
}
@Override
public InterfaceSolutionSet getAllSolutions() {
return null;
/**
* ToDo: This should somehow preserve the best found individual.
*/
return new SolutionSet(this.population);
}
@Override
public String getStringRepresentation() {
return this.toString();
}
@Override
public void setPopulation(Population pop) {
this.population = pop;
}
public void setMaxTrials(int maxTrials) {
this.maxTrials = maxTrials;
}
public int getMaxTrials() {
return maxTrials;
}
}