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/**
* Description: The description of particle swarm (PS) Generate-and-test Behavior.
*
#Supported parameters:
NAME VALUE_type Range DefaultV Description
c1 real [0, 2] 1.494 PSAgent: learning factor for pbest
c2 real [0, 2] 1.494 PSAgent: learning factor for gbest
w real [0, 1] 0.729 PSAgent: inertia weight
CL real [0, 0.1] 0 PSAgent: chaos factor
//Other choices for c1, c2, w, and CL: (2, 2, 0.4, 0.001)
* Author Create/Modi Note
* Xiaofeng Xie May 11, 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] Kennedy J, Eberhart R C. Particle swarm optimization. IEEE Int. Conf. on
* Neural Networks, Perth, Australia, 1995: 1942-1948
* For original particle swarm idea
* [2] Shi Y H, Eberhart R C. A Modified Particle Swarm Optimizer. IEEE Inter. Conf.
* on Evolutionary Computation, Anchorage, Alaska, 1998: 69-73
* For the inertia weight: adjust the trade-off between exploitation & exploration
* [3] Clerc M, Kennedy J. The particle swarm - explosion, stability, and
* convergence in a multidimensional complex space. IEEE Trans. on Evolutionary
* Computation. 2002, 6 (1): 58-73
* Constriction factor: ensures the convergence
* [4] Xie X F, Zhang W J, Yang Z L. A dissipative particle swarm optimization.
* Congress on Evolutionary Computation, Hawaii, USA, 2002: 1456-1461
* The CL parameter
* [5] Xie X F, Zhang W J, Bi D C. Optimizing semiconductor devices by self-
* organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA,
* 2004: 2017-2022
* Further experimental analysis on the convergence of PSO
* [6] 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.goodness.IGoodnessCompareEngine;
import net.adaptivebox.knowledge.Library;
import net.adaptivebox.knowledge.SearchPoint;
import net.adaptivebox.problem.ProblemEncoder;
import net.adaptivebox.space.BasicPoint;
import net.adaptivebox.space.DesignSpace;
public class PSGTBehavior extends AbsGTBehavior {
// Two normally choices for (c1, c2, weight), i.e., (2, 2, 0.4), or (1.494, 1.494, 0.729)
// The first is used in dissipative PSO (cf. [4]) as CL>0, and the second is achieved by using
// constriction factors (cf. [3])
public double c1=2;
public double c2=2;
public double weight = 0.4; //inertia weight
public double CL=0; //See ref[4], normally be 0.001~0.005
//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;
public void setMemPoints(SearchPoint pbest, BasicPoint pcurrent, BasicPoint pold) {
pcurrent_t = pcurrent;
pbest_t = pbest;
pold_t = pold;
}
@Override
public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) {
SearchPoint gbest_t = socialLib.getGbest();
DesignSpace designSpace = problemEncoder.getDesignSpace();
int DIMENSION = designSpace.getDimension();
double deltaxb, deltaxbm;
for (int b=0;b<DIMENSION;b++) {
if (Math.random()<CL) {
designSpace.mutationAt(trailPoint.getLocation(), b);
} else {
deltaxb = weight*(pcurrent_t.getLocation()[b]-pold_t.getLocation()[b])
+ c1*Math.random()*(pbest_t.getLocation()[b]-pcurrent_t.getLocation()[b])
+ c2*Math.random()*(gbest_t.getLocation()[b]-pcurrent_t.getLocation()[b]);
//limitation for delta_x
deltaxbm = 0.5*designSpace.getMagnitudeIn(b);
if(deltaxb<-deltaxbm) {
deltaxb = -deltaxbm;
} else if (deltaxb>deltaxbm) {
deltaxb = deltaxbm;
}
trailPoint.getLocation()[b] = pcurrent_t.getLocation()[b]+deltaxb;
}
}
}
@Override
public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) {
Library.replace(qualityComparator, trailPoint, pbest_t);
pold_t.importLocation(pcurrent_t);
pcurrent_t.importLocation(trailPoint);
}
}
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