nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java

   1 /**
   2  * Description: The description of particle swarm (PS) Generate-and-test Behavior.
   3  *
   4   #Supported parameters:
   5   NAME    VALUE_type   Range      DefaultV        Description
   6   c1       real        [0, 2]     1.494           PSAgent: learning factor for pbest
   7   c2       real        [0, 2]     1.494           PSAgent: learning factor for gbest
   8   w        real        [0, 1]     0.729           PSAgent: inertia weight
   9   CL       real        [0, 0.1]   0               PSAgent: chaos factor
  10   //Other choices for c1, c2, w, and CL: (2, 2, 0.4, 0.001)
  11
  12  * @ Author        Create/Modi     Note
  13  * Xiaofeng Xie    May 11, 2004
  14  * Xiaofeng Xie    Jul 01, 2008
  15  *
  16  * This library is free software; you can redistribute it and/or
  17  * modify it under the terms of the GNU Lesser General Public
  18  * License as published by the Free Software Foundation; either
  19  * version 2.1 of the License, or (at your option) any later version.
  20  *
  21  * This library is distributed in the hope that it will be useful,
  22  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  23  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  24  * Lesser General Public License for more details.
  25  *
  26  * Please acknowledge the author(s) if you use this code in any way.
  27  *
  28  * @version 1.0
  29  * @Since MAOS1.0
  30  *
  31  * @References:
  32  * [1] Kennedy J, Eberhart R C. Particle swarm optimization. IEEE Int. Conf. on
  33  * Neural Networks, Perth, Australia, 1995: 1942-1948
  34  * @ For original particle swarm idea
  35  * [2] Shi Y H, Eberhart R C. A Modified Particle Swarm Optimizer. IEEE Inter. Conf.
  36  * on Evolutionary Computation, Anchorage, Alaska, 1998: 69-73
  37  * @ For the inertia weight: adjust the trade-off between exploitation & exploration
  38  * [3] Clerc M, Kennedy J. The particle swarm - explosion, stability, and
  39  * convergence in a multidimensional complex space. IEEE Trans. on Evolutionary
  40  * Computation. 2002, 6 (1): 58-73
  41  * @ Constriction factor: ensures the convergence
  42  * [4] Xie X F, Zhang W J, Yang Z L. A dissipative particle swarm optimization.
  43  * Congress on Evolutionary Computation, Hawaii, USA, 2002: 1456-1461
  44  * @ The CL parameter
  45  * [5] Xie X F, Zhang W J, Bi D C. Optimizing semiconductor devices by self-
  46  * organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA,
  47  * 2004: 2017-2022
  48  * @ Further experimental analysis on the convergence of PSO
  49  * [6] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical
  50  *     optimization. Genetic and Evolutionary Computation Conference (GECCO),
  51  *     Seattle, WA, USA, 2004: 238-250
  52  *     -> a generate-and-test behavior
  53  *
  54  */
  55
  56 package net.adaptivebox.deps.behavior;
  57
  58 import net.adaptivebox.goodness.*;
  59 import net.adaptivebox.knowledge.*;
  60 import net.adaptivebox.problem.*;
  61 import net.adaptivebox.space.*;
  62
  63 public class PSGTBehavior extends AbsGTBehavior {
  64   // Two normally choices for (c1, c2, weight), i.e., (2, 2, 0.4), or (1.494, 1.494, 0.729)
  65   // The first is used in dissipative PSO (cf. [4]) as CL>0, and the second is achieved by using
  66   // constriction factors (cf. [3])
  67   public double c1=2;
  68   public double c2=2;
  69   public double weight = 0.4; //inertia weight
  70
  71   public double CL=0;  //See ref[4], normally be 0.001~0.005
  72
  73   //the own memory: store the point that generated in old learning cycle
  74   protected BasicPoint pold_t;
  75   //the own memory: store the point that generated in last learning cycle
  76   protected BasicPoint pcurrent_t;
  77   //the own memory: store the personal best point
  78   protected SearchPoint pbest_t;
  79
  80   public void setMemPoints(SearchPoint pbest, BasicPoint pcurrent, BasicPoint pold) {
  81     pcurrent_t = pcurrent;
  82     pbest_t = pbest;
  83     pold_t = pold;
  84   }
  85
  86   public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) {
  87     SearchPoint gbest_t = socialLib.getGbest();
  88     DesignSpace designSpace = problemEncoder.getDesignSpace();
  89     int DIMENSION = designSpace.getDimension();
  90     double deltaxb, deltaxbm;
  91     for (int b=0;b<DIMENSION;b++) {
  92       if (Math.random()<CL) {
  93         designSpace.mutationAt(trailPoint.getLocation(), b);
  94       } else {
  95         deltaxb = weight*(pcurrent_t.getLocation()[b]-pold_t.getLocation()[b])
  96              + c1*Math.random()*(pbest_t.getLocation()[b]-pcurrent_t.getLocation()[b])
  97              + c2*Math.random()*(gbest_t.getLocation()[b]-pcurrent_t.getLocation()[b]);
  98         //limitation for delta_x
  99         deltaxbm = 0.5*designSpace.getMagnitudeIn(b);
 100         if(deltaxb<-deltaxbm) {
 101           deltaxb = -deltaxbm;
 102         } else if (deltaxb>deltaxbm) {
 103           deltaxb = deltaxbm;
 104         }
 105         trailPoint.getLocation()[b] = pcurrent_t.getLocation()[b]+deltaxb;
 106       }
 107     }
 108   }
 109
 110   public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) {
 111     Library.replace(qualityComparator, trailPoint, pbest_t);
 112     pold_t.importLocation(pcurrent_t);
 113     pcurrent_t.importLocation(trailPoint);
 114    }
 115
 116 }
 117