nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java

   1 package net.adaptivebox.deps;
   2
   3 /**
   4  * Description: The description of agent with hybrid differential evolution and particle swarm.
   5  *
   6  * @ Author        Create/Modi     Note
   7  * Xiaofeng Xie    Jun 10, 2004
   8  * Xiaofeng Xie    Jul 01, 2008
   9  *
  10  * This library is free software; you can redistribute it and/or
  11  * modify it under the terms of the GNU Lesser General Public
  12  * License as published by the Free Software Foundation; either
  13  * version 2.1 of the License, or (at your option) any later version.
  14  *
  15  * This library is distributed in the hope that it will be useful,
  16  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  17  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  18  * Lesser General Public License for more details.
  19  *
  20  * Please acknowledge the author(s) if you use this code in any way.
  21  *
  22  * @version 1.0
  23  * @Since MAOS1.0
  24  *
  25  * @References:
  26  * [1] Zhang W J, Xie X F. DEPSO: hybrid particle swarm with differential
  27  * evolution operator. IEEE International Conference on Systems, Man & Cybernetics,
  28  * Washington D C, USA, 2003: 3816-3821
  29  * [2] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical
  30  *     optimization. Genetic and Evolutionary Computation Conference (GECCO),
  31  *     Seattle, WA, USA, 2004: 238-250
  32  *     -> an agent perspective
  33  */
  34
  35 import net.adaptivebox.deps.behavior.*;
  36 import net.adaptivebox.goodness.IGoodnessCompareEngine;
  37 import net.adaptivebox.knowledge.*;
  38 import net.adaptivebox.problem.*;
  39 import net.adaptivebox.space.*;
  40
  41 public class DEPSAgent implements ILibEngine {
  42
  43   //Describes the problem to be solved
  44   protected ProblemEncoder problemEncoder;
  45   //Forms the goodness landscape
  46   protected IGoodnessCompareEngine qualityComparator;
  47
  48   //store the point that generated in current learning cycle
  49   protected SearchPoint trailPoint;
  50
  51   //temp variable
  52   private AbsGTBehavior selectGTBehavior;
  53
  54   //The referred library
  55   protected Library socialLib;
  56   //the own memory: store the point that generated in old learning cycle
  57   protected BasicPoint pold_t;
  58   //the own memory: store the point that generated in last learning cycle
  59   protected BasicPoint pcurrent_t;
  60   //the own memory: store the personal best point
  61   protected SearchPoint pbest_t;
  62
  63   //Generate-and-test Behaviors
  64   protected DEGTBehavior deGTBehavior;
  65   protected PSGTBehavior psGTBehavior;
  66   public double switchP = 0.5;
  67
  68   public void setLibrary(Library lib) {
  69     socialLib = lib;
  70     deGTBehavior.setLibrary(socialLib);
  71     psGTBehavior.setLibrary(socialLib);
  72   }
  73
  74   public void setProblemEncoder(ProblemEncoder encoder) {
  75     problemEncoder = encoder;
  76     trailPoint = problemEncoder.getFreshSearchPoint();
  77     pold_t = problemEncoder.getFreshSearchPoint();
  78     pcurrent_t = problemEncoder.getFreshSearchPoint();
  79   }
  80
  81   public void setSpecComparator(IGoodnessCompareEngine comparer) {
  82     qualityComparator = comparer;
  83   }
  84
  85   public void setPbest(SearchPoint pbest) {
  86     pbest_t = pbest;
  87   }
  88
  89   protected AbsGTBehavior getGTBehavior() {
  90     if (Math.random()<switchP) {
  91       return deGTBehavior;
  92     } else {
  93       return psGTBehavior;
  94     }
  95   }
  96
  97   public void setGTBehavior(AbsGTBehavior gtBehavior) {
  98     if (gtBehavior instanceof DEGTBehavior) {
  99       deGTBehavior = ((DEGTBehavior)gtBehavior);
 100       deGTBehavior.setPbest(pbest_t);
 101       return;
 102     }
 103     if (gtBehavior instanceof PSGTBehavior) {
 104       psGTBehavior = ((PSGTBehavior)gtBehavior);
 105       psGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
 106       return;
 107     }
 108   }
 109
 110   public void generatePoint() {
 111     // generates a new point in the search space (S) based on
 112     // its memory and the library
 113     selectGTBehavior = this.getGTBehavior();
 114     selectGTBehavior.generateBehavior(trailPoint, problemEncoder);
 115     //evaluate into goodness information
 116     problemEncoder.evaluate(trailPoint);
 117   }
 118
 119   public void learn() {
 120     selectGTBehavior.testBehavior(trailPoint, qualityComparator);
 121   }
 122
 123   public SearchPoint getMGState() {
 124     return trailPoint;
 125   }
 126 }
 127