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package net.adaptivebox.deps;
/**
* Description: The description of agent with hybrid differential evolution and particle swarm.
*
* @ Author Create/Modi Note
* Xiaofeng Xie Jun 10, 2004
* Xiaofeng Xie Jul 01, 2008
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* Please acknowledge the author(s) if you use this code in any way.
*
* @version 1.0
* @Since MAOS1.0
*
* @References:
* [1] Zhang W J, Xie X F. DEPSO: hybrid particle swarm with differential
* evolution operator. IEEE International Conference on Systems, Man & Cybernetics,
* Washington D C, USA, 2003: 3816-3821
* [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
* -> an agent perspective
*/
import net.adaptivebox.deps.behavior.AbsGTBehavior;
import net.adaptivebox.deps.behavior.DEGTBehavior;
import net.adaptivebox.deps.behavior.PSGTBehavior;
import net.adaptivebox.global.RandomGenerator;
import net.adaptivebox.goodness.IGoodnessCompareEngine;
import net.adaptivebox.knowledge.ILibEngine;
import net.adaptivebox.knowledge.Library;
import net.adaptivebox.knowledge.SearchPoint;
import net.adaptivebox.problem.ProblemEncoder;
import net.adaptivebox.space.BasicPoint;
public class DEPSAgent {
// Describes the problem to be solved
private ProblemEncoder problemEncoder;
// Forms the goodness landscape
private IGoodnessCompareEngine qualityComparator;
// store the point that generated in current learning cycle
private SearchPoint trailPoint;
// temp variable
private AbsGTBehavior selectGTBehavior;
// the own memory: store the point that generated in old learning cycle
private BasicPoint pold_t;
// the own memory: store the point that generated in last learning cycle
private BasicPoint pcurrent_t;
// the own memory: store the personal best point
private SearchPoint pbest_t;
// Generate-and-test behaviors.
private DEGTBehavior deGTBehavior;
private PSGTBehavior psGTBehavior;
private double switchP = 0.5;
public DEPSAgent(ProblemEncoder encoder, DEGTBehavior deGTBehavior, PSGTBehavior psGTBehavior,
double switchP, IGoodnessCompareEngine comparer, SearchPoint pbest) {
this.switchP = switchP;
problemEncoder = encoder;
qualityComparator = comparer;
trailPoint = problemEncoder.getFreshSearchPoint();
pold_t = problemEncoder.getFreshSearchPoint();
pcurrent_t = problemEncoder.getFreshSearchPoint();
pbest_t = pbest;
this.deGTBehavior = deGTBehavior;
this.deGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
this.psGTBehavior = psGTBehavior;
this.psGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
}
public void setSpecComparator(IGoodnessCompareEngine comparer) {
qualityComparator = comparer;
}
private AbsGTBehavior getGTBehavior() {
if (RandomGenerator.doubleZeroOneRandom() < switchP) {
return deGTBehavior;
} else {
return psGTBehavior;
}
}
public void setGTBehavior(AbsGTBehavior gtBehavior) {
gtBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
}
public void generatePoint() {
// generates a new point in the search space (S) based on
// its memory and the library
selectGTBehavior = getGTBehavior();
selectGTBehavior.generateBehavior(trailPoint, problemEncoder);
// evaluate into goodness information
problemEncoder.evaluate(trailPoint);
}
public void learn() {
selectGTBehavior.testBehavior(trailPoint, qualityComparator);
}
public SearchPoint getMGState() {
return trailPoint;
}
}
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