deagent.java

用java实现的改进粒子群算法

/**
 * Description: The description of differential evolution agent.

  #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
 *
 * 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] 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 initial idea
 * [2] Zhang W J, Xie X F. DEPSO: hybrid particle swarm with differential
 * evolution operator. IEEE Int. Conf. on Systems, Man & Cybernetics,
 * Washington D C, USA, 2003: 3816-3821
 * @ The experimental-based explanation on the differential vector
 */

package agent;

import knowledge.*;

import Global.*;
import problem.*;
import space.*;

public class DEAgent extends AbstractAgent implements ILibEngine {
  public int DVNum = 2;  //Number of differential vectors
  public double FACTOR = 0.5; //scale constant: (0, 1.2]
  public double CR = 0.9;     //crossover constant: [0, 1]

  //The referred external library
  protected Library socialLib;
  //the own memory: store the point that generated in last learning cycle
  protected SearchPoint pbest_t;
  //the best point in the social sharing information
  protected SearchPoint gbest_t;

  public void setLibrary(Library lib) {
    socialLib = lib;
  }

  public void setProblemEncoder(ProblemEncoder encoder) {
    super.setProblemEncoder(encoder);
  }

  public void setGbest(SearchPoint gbest) {
    gbest_t = gbest;
  }

  public void setPbest(SearchPoint pbest) {
    pbest_t = pbest;
  }

  protected void generatePoint(ILocationEngine tempPoint, int t) {
    BasicPoint[] referPoints = getReferPoints();
    int DIMENSION = problemEncoder.getDesignSpace().getDimension();
    int rj = RandomGenerator.intRangeRandom(0, DIMENSION-1);
    for (int k=0; k<DIMENSION; k++) {
      if (Math.random()<CR || k == DIMENSION-1) {
        double Dabcd = 0;
        for(int i=0; i<referPoints.length; i++) {
          Dabcd += Math.pow(-1, i%2)*referPoints[i].getLocation()[rj];
        }
        tempPoint.getLocation()[rj] = gbest_t.getLocation()[rj]+FACTOR*Dabcd;
      } else {
        tempPoint.getLocation()[rj] = pbest_t.getLocation()[rj];
      }
      rj = (rj+1)%DIMENSION;
    }
  }

  public void updateInfo() {
    Library.replace(specComparator, trailPoint, pbest_t);
  }

  protected SearchPoint[] getReferPoints() {
    SearchPoint[] referPoints = new SearchPoint[DVNum*2];
    for(int i=0; i<referPoints.length; i++) {
      referPoints[i] = socialLib.getSelectedPoint(RandomGenerator.intRangeRandom(0, socialLib.getPopSize()-1));
    }
    return referPoints;
  }
}