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.AbsGTBehavior;
  36 import net.adaptivebox.deps.behavior.DEGTBehavior;
  37 import net.adaptivebox.deps.behavior.PSGTBehavior;
  38 import net.adaptivebox.global.RandomGenerator;
  39 import net.adaptivebox.goodness.IGoodnessCompareEngine;
  40 import net.adaptivebox.knowledge.ILibEngine;
  41 import net.adaptivebox.knowledge.Library;
  42 import net.adaptivebox.knowledge.SearchPoint;
  43 import net.adaptivebox.problem.ProblemEncoder;
  44 import net.adaptivebox.space.BasicPoint;
  45
  46 public class DEPSAgent {
  47
  48   // Describes the problem to be solved
  49   private ProblemEncoder problemEncoder;
  50
  51   // Forms the goodness landscape
  52   private IGoodnessCompareEngine qualityComparator;
  53
  54   // store the point that generated in current learning cycle
  55   private SearchPoint trailPoint;
  56
  57   // temp variable
  58   private AbsGTBehavior selectGTBehavior;
  59
  60   // the own memory: store the point that generated in old learning cycle
  61   private BasicPoint pold_t;
  62
  63   // the own memory: store the point that generated in last learning cycle
  64   private BasicPoint pcurrent_t;
  65
  66   // the own memory: store the personal best point
  67   private SearchPoint pbest_t;
  68
  69   // Generate-and-test behaviors.
  70   private DEGTBehavior deGTBehavior;
  71   private PSGTBehavior psGTBehavior;
  72
  73   private double switchP = 0.5;
  74
  75   public DEPSAgent(ProblemEncoder encoder, DEGTBehavior deGTBehavior, PSGTBehavior psGTBehavior,
  76                    double switchP, IGoodnessCompareEngine comparer, SearchPoint pbest) {
  77       this.switchP = switchP;
  78
  79       problemEncoder = encoder;
  80
  81       qualityComparator = comparer;
  82
  83       trailPoint = problemEncoder.getFreshSearchPoint();
  84       pold_t = problemEncoder.getFreshSearchPoint();
  85       pcurrent_t = problemEncoder.getFreshSearchPoint();
  86       pbest_t = pbest;
  87
  88       this.deGTBehavior = deGTBehavior;
  89       this.deGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
  90
  91       this.psGTBehavior = psGTBehavior;
  92       this.psGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
  93   }
  94
  95   public void setSpecComparator(IGoodnessCompareEngine comparer) {
  96     qualityComparator = comparer;
  97   }
  98
  99   private AbsGTBehavior getGTBehavior() {
 100     if (RandomGenerator.doubleZeroOneRandom() < switchP) {
 101       return deGTBehavior;
 102     } else {
 103       return psGTBehavior;
 104     }
 105   }
 106
 107   public void setGTBehavior(AbsGTBehavior gtBehavior) {
 108     gtBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
 109   }
 110
 111   public void generatePoint() {
 112     // generates a new point in the search space (S) based on
 113     // its memory and the library
 114     selectGTBehavior = getGTBehavior();
 115     selectGTBehavior.generateBehavior(trailPoint, problemEncoder);
 116
 117     // evaluate into goodness information
 118     problemEncoder.evaluate(trailPoint);
 119   }
 120
 121   public void learn() {
 122     selectGTBehavior.testBehavior(trailPoint, qualityComparator);
 123   }
 124
 125   public SearchPoint getMGState() {
 126     return trailPoint;
 127   }
 128 }