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/**
* Description: The description of differential evolution Generate-and-Test Behavior.
#Supported parameters:
NAME VALUE_type Range DefaultV Description
FACTOR real (0, 1.2] 0.5 DEAgent: scale constant
CR real [0, 1] 0.9 DEAgent: crossover constant
//Other choices for FACTOR and CR: (0.5, 0.1)
*
* Author Create/Modi Note
* Xiaofeng Xie May 11, 2004
* Xiaofeng Xie Jul 01, 2008
*
* @version 1.0
* @Since MAOS1.0
*
* @References:
* [1] Storn R, Price K. Differential evolution - a simple and efficient
* heuristic for global optimization over continuous spaces. Journal of
* Global Optimization, 1997, 11: 341-359
* The original differential evolution idea
* [2] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical
* optimization. Genetic and Evolutionary Computation Conference (GECCO),
* Seattle, WA, USA, 2004: 238-250
* -> a generate-and-test behavior
*/
package net.adaptivebox.deps.behavior;
import net.adaptivebox.global.RandomGenerator;
import net.adaptivebox.goodness.IGoodnessCompareEngine;
import net.adaptivebox.knowledge.Library;
import net.adaptivebox.knowledge.SearchPoint;
import net.adaptivebox.problem.ProblemEncoder;
import net.adaptivebox.space.BasicPoint;
public class DEGTBehavior extends AbsGTBehavior {
//Number of differential vectors, normally be 1 or 2
private static final int DVNum = 2;
//scale constant: (0, 1.2], normally be 0.5
public double MIN_FACTOR = 0.5;
//scale constant: (0, 1.2], normally be 0.5
public double MAX_FACTOR = 0.5;
//crossover constant: [0, 1], normally be 0.1 or 0.9
public double CR = 0.9;
@Override
public void setMemPoints(SearchPoint pbest, BasicPoint pcurrent, BasicPoint pold) {
pbest_t = pbest;
}
/**
* Crossover and mutation for a single vector element done in a single step.
*
* @param index Index of the trial vector element to be changed.
* @param trialVector Trial vector reference.
* @param globalVector Global best found vector reference.
* @param differenceVectors List of vectors used for difference delta
* calculation.
*/
private void crossoverAndMutation(int index, double trialVector[], double globalVector[],
BasicPoint differenceVectors[]) {
double delta = 0D;
for (int i = 0; i < differenceVectors.length; i++) {
delta += (i % 2 == 0 ? +1D : -1D) * differenceVectors[i].getLocation()[index];
}
trialVector[index] = globalVector[index] + RandomGenerator.doubleRangeRandom(MIN_FACTOR, MAX_FACTOR) * delta;
}
@Override
public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) {
BasicPoint[] referPoints = getReferPoints();
int DIMENSION = problemEncoder.getDesignSpace().getDimension();
int guaranteeIndex = RandomGenerator.intRangeRandom(0, DIMENSION - 1);
double[] trailVector = trailPoint.getLocation();
double[] locaclVector = pbest_t.getLocation();
double[] globalVector = socialLib.getGbest().getLocation();
/* Handle first part of the trial vector. */
for (int index = 0; index < guaranteeIndex; index++) {
if (CR <= RandomGenerator.doubleZeroOneRandom()) {
trailVector[index] = locaclVector[index];
continue;
}
crossoverAndMutation(index, trailVector, globalVector, referPoints);
}
/* Guarantee for at least one change in the trial vector. */
crossoverAndMutation(guaranteeIndex, trailVector, globalVector, referPoints);
/* Handle second part of the trial vector. */
for (int index = guaranteeIndex + 1; index < DIMENSION; index++) {
if (CR <= RandomGenerator.doubleZeroOneRandom()) {
trailVector[index] = locaclVector[index];
continue;
}
crossoverAndMutation(index, trailVector, globalVector, referPoints);
}
}
@Override
public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) {
Library.replace(qualityComparator, trailPoint, pbest_t);
}
private SearchPoint[] getReferPoints() {
SearchPoint[] referPoints = new SearchPoint[DVNum * 2];
for (int i = 0; i < referPoints.length; i++) {
referPoints[i] = socialLib.getRandomPoint();
}
return referPoints;
}
}
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