标题: 粒子群算法Matlab工具箱！ [打印本页] 作者: madio 时间: 2020-2-19 08:35 标题: 粒子群算法Matlab工具箱！ PSOt, particle swarm optimization toolbox for matlab. # S4 ?% v4 a F) |" D) W. V' S
May be distributed freely as long as none of the files are 7 X7 a( k8 o- H. @/ S/ ymodified. % ^" T- Y7 \4 P8 n @" U% s ' Y6 f% v% c3 s( SSend suggestions to bkbirge@yahoo.com ' r: }0 v* U% I- y
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Updates will be posted periodically at the Mathworks User 1 M( P& [7 C6 N5 ^8 o; Y2 H+ @; i0 X
Contributed Files website (www.mathworks.com) under the # {1 f+ u/ y E7 AOptimization category. & D3 D( `8 d9 X( r6 M9 X% W" y! A' t 0 j7 u w$ w0 iTo install: 8 a4 ]( @& f# S4 T6 c9 p! _1 RExtract into any directory you want but make sure the matlab ; G, f" Z, |: ?: J/ _# U
path points to that directory and the subdirectories 5 L. E, a" v( u7 ]8 d- f'hiddenutils' and 'testfunctions'. ( K! w. q. J3 F: y
) }# [3 i# H5 A4 FEnjoy! - Brian Birge 7 L* f* _% @ n% W6 q ) A U$ K+ A! `/ k-------------------------------------------------------------! O' s5 P6 @( z. `
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INFO + }, M" B$ O% _7 O' {# KQuick start: just type ... out = pso_Trelea_vectorized('f6',2) 0 \0 w" c; L8 S \0 K1 wand watch it work! r* |2 _: N- m# I# V9 B
9 C; w# ~0 O" yThis is a PSO toolbox implementing Common, Clerc 1", and 0 c8 s8 i, u. S+ ]
Trelea types along with an alpha version of tracking changing; G- X9 T4 M% |$ L
environments. It can search for min, max, or 'distance' of 1 I" A v+ q. T$ d" {
user developed cost function. Very easy to use and hack with , @, b% z& A' i+ j; f# w7 Q4 Ureasonably good documentation (type help for any function and2 o& V# \- z y& f. w
it should tell you what you need) and will take advantage of " E" J0 ]/ Q `. f# J* r L" R( y
vectorized cost functions. It uses similar syntax to Matlab's , {" l) a( t" P- u, H" [optimization toolbox. Includes a suite of static and dynamic 3 }7 O* O; a, P. O1 Qtest functions. It also includes a dedicated PSO based neural 6 U# F. h$ \+ l1 ]+ q* v
network trainer for use with Mathwork's neural network toolbox. ' L' O) M K% O* S& P : b: }+ C7 O! L+ dRun 'DemoPSOBehavior' to explore the various functions, options, , ]& M, _6 E: v2 R
and visualizations. + m/ o3 J+ S K" w
% R7 _. b6 U. J% }; z+ m3 qRun 'demoPSOnet' to see a neural net trained with PSO 6 ]# f) O, J( B' n(requires neural net toolbox). ; @# f* ]1 O4 V # D# s7 Q9 }1 B% E0 l. M+ o+ P7 p) F8 x$ |
This toolbox is in constant development and I welcome : _4 _9 ]& S7 `! t
suggestions. The main program 'pso_Trelea_vectorized.m' lists ; q# T* K1 A. W9 a' qvarious papers you can look at in the comments.8 _3 S2 V! r% x* Y! Z3 p, ]( ]0 q }
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Usage ideas: to find a global min/max, to optimize training of 0 n7 U' N* h$ T* e. J: i% Vneural nets, error topology change tracking, teaching PSO, ! X x- l0 Z6 y einvestigate Emergence, tune control systems/filters, paradigm 9 l8 n1 }. J: F9 h0 `
for multi-agent interaction, etc. 8 X" n4 N% z E, z6 W" ]& S5 O7 z# F" W8 \
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) z9 f+ w/ E% }+ q' J' G# d** in main directory:$ H c1 X# T. y, f: X, _
2 v* q) Q b& M; P0) ReadMe.txt - this file, duh # z! |; [* t: V! P5 e/ j' g( ~( ]1) A Particle Swarm Optimization (PSO) Primer.pdf - powerpoint converted to pdf presentation explaining the very basics of PSO# `( I. o; F3 K5 {' C5 B- q5 B$ c
2) DemoPSOBehavior.m - demo script, useful to see how the pso main function is called" U8 o6 g' q( S
3) goplotpso4demo.m - plotting routine called by the demo script, useful to see how custom plotting can be developed though this routine slows down the PSO a lot, ?& ~+ F" r9 g: T: }
4) goplotpso.m - default plotting routine used by pso algorithm; e S0 k, j, I6 P% t F
5) pso_Trelea_vectorized.m - main PSO algorithm function, implements Common, Trelea 1&2, Clerc 1", and an alpha version of tracking environmental changes. / D3 S7 `3 Z( e0 |4 t \9 V9 _6 T; d$ D8 I* U& L9 h
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** in 'hiddenutils' . J* R1 c k& y- @" @: ` ; g; X2 w$ ^2 m! ^' r8 ?, o1) forcerow, forcecol.m - utils to force a vector to be a row or column, superseded by Matlab 7 functions I believe but I think they are still called in the main algo $ @: `1 Q( @, G$ k2) normmat.m - takes a matrix and reformats the data to fit between a new range, very flexible3 e( D$ L V/ y
3) linear_dyn, spiral_dyn.m - helpers for the dynamic test functions listed in the 'testfunctions' directory % k, Z9 T% Z( n$ Y `6 Y1 V( ~% ^( U% q, r0 h. g, |9 {6 B
3 u3 l! C. ?2 U m% C/ J% A8 EA bunch of useful functions (mostly 2D) for testing. See help for each one for specifics. Here's a list of the names:# l0 |. d# y7 ]$ f* B( |! p
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Static test functions, minima don't change w.r.t. time/iteration: ; x5 d& h1 |3 z2 s/ d6 d6 c 1) Ackley 0 h7 h8 U" w* s; ^4 z$ }$ {8 g" o 2) Alpine" W: l9 H/ n1 H& k+ L
3) DeJong_f27 f( |* \2 r7 g e4 j! T/ g
4) DeJong_f3; y( F1 I: u. Q, ?: {' F. H
5) DeJong_f4* s+ i: |/ {; V+ }) B( g) N0 ]% k
6) Foxhole, @& p3 `3 n+ D* M1 {
7) Griewank / v: L% c8 V1 M4 t% H 8) NDparabola * w0 J8 m. @" _% O" f1 ^* p9 w5 k 9) Rastrigin , m; C* Q9 i9 O8 [% s+ w+ Q10) Rosenbrock + s# W: Y( L1 Q3 p11) Schaffer f6 & R$ C* X P9 |2 y12) Schaffer f6 modified (5 f6 functions translated from each other)) M- x1 H/ `$ P+ K
13) Tripod 5 f* z% ^0 X+ M" a9 f) s: O2 @ W$ Z; ]6 i! z' v5 } S. o
Dynamic test functions, minima/environment evolves over time (NOT iteration, though easily modifed to do so): ]( n7 ]5 K0 g& e
14) f6_bubbles_dyn " K3 ]+ b3 ?; c2 i& m9 G! ]15) f6_linear_dyn: f6 o8 @" Y+ d
16) f6_spiral_dyn- m% c, K3 P( L. h8 p; x5 f
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** in 'nnet' (all these require Matlab's Neural Net toolbox)$ }& l, z. Z9 Z9 l
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1) demoPSOnet - standalone demo to show neural net training W' m8 o* N. b
2) trainpso - the neural net toolbox plugin, set net.trainFcn to this! R; a4 \ h. e
3) pso_neteval - wrapper used by trainpso to call the main PSO optimizer, this is the cost function that PSO will optimize; p {& s: d8 [" t) p Y
4) goplotpso4net - default graphing plugin for trainpso, shows net architecture, relative weight indications, error, and PSO details on run' |# X& W/ P3 {) C Z& f
