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package net.adaptivebox.sco;
/**
* Description: The description of social cognitive agent.
*
* @Information source: a) external library (L); b) the own memory: a point that
* generated in the last learning cycle
*
* @Coefficients: TaoB and TaoW
*
* @ Author Create/Modi Note
* Xiaofeng Xie Mar 11, 2003
* Xiaofeng Xie May 11, 2004
* Xiaofeng Xie May 20, 2004
*
* 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] Xie X F, Zhang W J. Solving engineering design problems by social cognitive
* optimization. Genetic and Evolutionary Computation Conference, 2004: 261-262
*/
import net.adaptivebox.global.*;
import net.adaptivebox.space.*;
import net.adaptivebox.goodness.*;
import net.adaptivebox.problem.*;
import net.adaptivebox.knowledge.*;
public class SCAgent {
//Describes the problem to be solved (encode the point into intermediate information)
private ProblemEncoder problemEncoder;
//Forms the goodness landscape
private IGoodnessCompareEngine specComparator;
//the coefficients of SCAgent
protected int TaoB = 2;
//The early version set TaoW as the size of external library (NL), but 4 is often enough
protected int TaoW = 4;
//The referred external library
protected Library externalLib;
//store the point that generated in current learning cycle
protected SearchPoint trailPoint;
//the own memory: store the point that generated in last learning cycle
protected SearchPoint pcurrent_t;
public void setExternalLib(Library lib) {
externalLib = lib;
}
public void setProblemEncoder(ProblemEncoder encoder) {
problemEncoder = encoder;
trailPoint = problemEncoder.getFreshSearchPoint();
pcurrent_t = problemEncoder.getEncodedSearchPoint();
}
public void setSpecComparator(IGoodnessCompareEngine comparer) {
specComparator = comparer;
}
public SearchPoint generatePoint() {
//generate a new point
generatePoint(trailPoint);
//evalute the generated point
problemEncoder.evaluate(trailPoint);
return trailPoint;
}
protected void generatePoint(ILocationEngine tempPoint) {
SearchPoint Xmodel, Xrefer, libBPoint;
// choose Selects a better point (libBPoint) from externalLib (L) based
// on tournament selection
int xb = externalLib.tournamentSelection(specComparator, TaoB, true);
libBPoint = externalLib.getSelectedPoint(xb);
// Compares pcurrent_t with libBPoint
// The better one becomes model point (Xmodel)
// The worse one becomes refer point (Xrefer)
if(specComparator.compare(pcurrent_t.getEncodeInfo(), libBPoint.getEncodeInfo())==IGoodnessCompareEngine.LARGER_THAN) {
Xmodel = libBPoint;
Xrefer = pcurrent_t;
} else {
Xmodel = pcurrent_t;
Xrefer = libBPoint;
}
// observational learning: generates a new point near the model point, which
// the variation range is decided by the difference of Xmodel and Xrefer
inferPoint(tempPoint, Xmodel, Xrefer, problemEncoder.getDesignSpace());
}
//1. Update the current point into the external library
//2. Replace the current point by the generated point
public void updateInfo() {
//Selects a bad point kw from TaoW points in Library
int xw = externalLib.tournamentSelection(specComparator, TaoW, false);
//Repaces kw with pcurrent_t
externalLib.getSelectedPoint(xw).importPoint(pcurrent_t);
//Repaces pcurrent_t (x(t)) with trailPoint (x(t+1))
pcurrent_t.importPoint(trailPoint);
}
// 1---model point, 2---refer point
public boolean inferPoint(ILocationEngine newPoint, ILocationEngine point1,ILocationEngine point2, DesignSpace space){
double[] newLoc = newPoint.getLocation();
double[] real1 = point1.getLocation();
double[] real2 = point2.getLocation();
for (int i=0; i<newLoc.length; i++) {
newLoc[i] = real1[i]*2-real2[i];
//boundary handling
newLoc[i] = space.boundAdjustAt(newLoc[i], i);
newLoc[i] = RandomGenerator.doubleRangeRandom(newLoc[i], real2[i]);
}
return true;
}
}
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