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/**
* Description: provide the information for the search space (S)
*
* @ Author Create/Modi Note
* Xiaofeng Xie Mar 2, 2003
* Xiaofeng Xie May 11, 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.
*
* @References:
* [1] Zhang W J, Xie X F, Bi D C. Handling boundary constraints for numerical
* optimization by particle swarm flying in periodic search space. Congress
* on Evolutionary Computation, Oregon, USA, 2004
* @ especially for particle swarm agent
*/
package net.adaptivebox.space;
import net.adaptivebox.global.*;
public class DesignSpace {
//The information of all the dimension
private DesignDim[] dimProps;
public DesignSpace(int dim) {
dimProps = new DesignDim[dim];
}
public DesignDim getDimAt(int index) {
return dimProps[index];
}
public void setElemAt(DesignDim elem, int index) {
dimProps[index] = elem;
}
public int getDimension() {
if (dimProps==null) {
return -1;
}
return dimProps.length;
}
public double boundAdjustAt(double val, int dim){
return dimProps[dim].paramBound.boundAdjust(val);
}
public void annulusAdjust (double[] location){
for (int i=0; i<getDimension(); i++) {
location[i] = dimProps[i].paramBound.annulusAdjust(location[i]);
}
}
public void randomAdjust (double[] location){
for (int i=0; i<getDimension(); i++) {
location[i] = dimProps[i].paramBound.randomAdjust(location[i]);
}
}
public boolean satisfyCondition(double[] location){
for (int i=0; i<getDimension(); i++) {
if (!dimProps[i].paramBound.isSatisfyCondition(location[i])) {
return false;
}
}
/*If the limits are not violated, return TRUE*/
return(true);
}
public void mutationAt(double[] location, int i){
location[i] = dimProps[i].paramBound.getRandomValue();
}
public double mutationUniformAtPointAsCenter (double pointX, int i){
double length = this.getMagnitudeIn(i)/2;
pointX += RandomGenerator.doubleRangeRandom(-1*length, length);
return pointX;
}
public double getUpValueAt(int dimensionIndex) {
return dimProps[dimensionIndex].paramBound.maxValue;
}
public double getLowValueAt(int dimensionIndex) {
return dimProps[dimensionIndex].paramBound.minValue;
}
public double getMagnitudeIn(int dimensionIndex) {
return dimProps[dimensionIndex].paramBound.getLength();
}
public boolean initilizeGeneAtPointAsCenter(double[] tempX){
if (tempX.length!=this.getDimension()) {
return false;
}
for(int i=0;i<tempX.length;i++) {
double length = this.getMagnitudeIn(i)/2;
tempX[i]+=RandomGenerator.doubleRangeRandom(-1*length, length);
}
return true;
}
public void initializeGene(double[] tempX){
for(int i=0;i<tempX.length;i++) tempX[i] = dimProps[i].paramBound.getRandomValue(); //Global.RandomGenerator.doubleRangeRandom(9.8, 10);
}
public double[] getFreshGene() {
double[] tempX = new double[this.getDimension()];
initializeGene(tempX);
return tempX;
}
public void getMappingPoint(double[] point) {
for(int i=0; i<getDimension(); i++) {
point[i] = dimProps[i].paramBound.annulusAdjust(point[i]);
if(dimProps[i].isDiscrete()) {
point[i] = dimProps[i].getGrainedValue(point[i]);
}
}
}
public double[] getRealLoc(double[] imageLoc) {
double[] realLoc = new double[imageLoc.length];
System.arraycopy(imageLoc, 0, realLoc, 0, imageLoc.length);
annulusAdjust(realLoc);
return realLoc;
}
}
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