1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
|
/**
* Description: The description of particle swarm (PS) Generate-and-test Behavior.
*
#Supported parameters:
NAME VALUE_type Range DefaultV Description
c1 real [0, 2] 1.494 PSAgent: learning factor for pbest
c2 real [0, 2] 1.494 PSAgent: learning factor for gbest
w real [0, 1] 0.729 PSAgent: inertia weight
CL real [0, 0.1] 0 PSAgent: chaos factor
//Other choices for c1, c2, w, and CL: (2, 2, 0.4, 0.001)
* Author Create/Modi Note
* Xiaofeng Xie May 11, 2004
* Xiaofeng Xie Jul 01, 2008
*
* 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] Kennedy J, Eberhart R C. Particle swarm optimization. IEEE Int. Conf. on
* Neural Networks, Perth, Australia, 1995: 1942-1948
* For original particle swarm idea
* [2] Shi Y H, Eberhart R C. A Modified Particle Swarm Optimizer. IEEE Inter. Conf.
* on Evolutionary Computation, Anchorage, Alaska, 1998: 69-73
* For the inertia weight: adjust the trade-off between exploitation & exploration
* [3] Clerc M, Kennedy J. The particle swarm - explosion, stability, and
* convergence in a multidimensional complex space. IEEE Trans. on Evolutionary
* Computation. 2002, 6 (1): 58-73
* Constriction factor: ensures the convergence
* [4] Xie X F, Zhang W J, Yang Z L. A dissipative particle swarm optimization.
* Congress on Evolutionary Computation, Hawaii, USA, 2002: 1456-1461
* The CL parameter
* [5] Xie X F, Zhang W J, Bi D C. Optimizing semiconductor devices by self-
* organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA,
* 2004: 2017-2022
* Further experimental analysis on the convergence of PSO
* [6] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical
* optimization. Genetic and Evolutionary Computation Conference (GECCO),
* Seattle, WA, USA, 2004: 238-250
* -> a generate-and-test behavior
*
*/
package net.adaptivebox.deps.behavior;
import net.adaptivebox.global.RandomGenerator;
import net.adaptivebox.goodness.IGoodnessCompareEngine;
import net.adaptivebox.knowledge.Library;
import net.adaptivebox.knowledge.SearchPoint;
import net.adaptivebox.problem.ProblemEncoder;
import net.adaptivebox.space.BasicPoint;
import net.adaptivebox.space.DesignSpace;
public class PSGTBehavior extends AbsGTBehavior {
// Two normally choices for (c1, c2, weight), i.e., (2, 2, 0.4), or (1.494,
// 1.494, 0.729) The first is used in dissipative PSO (cf. [4]) as CL>0, and
// the second is achieved by using constriction factors (cf. [3])
public double c1 = 2;
public double c2 = 2;
//inertia weight
public double weight = 0.4;
//See ref[4], normally be 0.001~0.005
public double CL = 0;
// the own memory: store the point that generated in old learning cycle
private BasicPoint pold_t;
// the own memory: store the point that generated in last learning cycle
private BasicPoint pcurrent_t;
@Override
public void setMemPoints(SearchPoint pbest, BasicPoint pcurrent, BasicPoint pold) {
pcurrent_t = pcurrent;
pbest_t = pbest;
pold_t = pold;
}
@Override
public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) {
DesignSpace designSpace = problemEncoder.getDesignSpace();
double[] pold_t_location = pold_t.getLocation();
double[] pbest_t_location = pbest_t.getLocation();
double[] pcurrent_t_location = pcurrent_t.getLocation();
double[] gbest_t_location = socialLib.getGbest().getLocation();
double[] trailPointLocation = trailPoint.getLocation();
int DIMENSION = designSpace.getDimension();
for (int b = 0; b < DIMENSION; b++) {
if (RandomGenerator.doubleZeroOneRandom() < CL) {
designSpace.mutationAt(trailPointLocation, b);
continue;
}
double deltaxb = weight * (pcurrent_t_location[b] - pold_t_location[b])
+ c1 * RandomGenerator.doubleZeroOneRandom() * (pbest_t_location[b] - pcurrent_t_location[b])
+ c2 * RandomGenerator.doubleZeroOneRandom() * (gbest_t_location[b] - pcurrent_t_location[b]);
// limitation for delta_x
double deltaxbm = 0.5 * designSpace.getMagnitudeIn(b);
if (deltaxb < -deltaxbm) {
deltaxb = -deltaxbm;
} else if (deltaxb > deltaxbm) {
deltaxb = deltaxbm;
}
trailPointLocation[b] = pcurrent_t_location[b] + deltaxb;
}
}
@Override
public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) {
Library.replace(qualityComparator, trailPoint, pbest_t);
pold_t.importLocation(pcurrent_t);
pcurrent_t.importLocation(trailPoint);
}
}
|
