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. 9 o0 X& D, s, z# ?5 q) K6 C+ i6 ?+ H$ a 4 m `! e+ Z# @$ \$ M- @May be distributed freely as long as none of the files are # X6 a% z$ }# l0 wmodified. 3 t4 ~" v. C/ }, z c$ }& w4 B2 t! w( N; L8 qSend suggestions to bkbirge@yahoo.com , h/ m7 o$ ?3 w' B8 \4 S% u
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Updates will be posted periodically at the Mathworks User 0 j5 G: Y! R: `5 \" ~Contributed Files website (www.mathworks.com) under the ) Y6 [& j( v8 k/ M- Z% Q/ X
Optimization category.' w' m2 O" E. R) l: g; k4 P
0 q" r2 f$ E5 z$ p* D1 MTo install:5 p' W. C$ }+ i |: R2 x j8 j4 \/ H2 R
Extract into any directory you want but make sure the matlab 0 u7 ?0 R: Z" j8 x
path points to that directory and the subdirectories ; ], s* H. I ^' d'hiddenutils' and 'testfunctions'. $ M+ q/ {; J! o# O
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Enjoy! - Brian Birge $ w( ^2 A3 p7 J. p( N5 Y- T7 Z7 @ 1 N+ G, U5 l8 s! g2 z) F, ?! Z0 ]' j9 p-------------------------------------------------------------% o" l3 w \) Y# `2 A1 g
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INFO ) x3 ]- d k$ H8 HQuick start: just type ... out = pso_Trelea_vectorized('f6',2) 8 `3 N, G" |8 C0 S6 ?5 Wand watch it work!6 `( t& F: v" ?
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This is a PSO toolbox implementing Common, Clerc 1", and 3 i/ Y. R6 b9 a0 W% n6 `3 bTrelea types along with an alpha version of tracking changing # B0 Z, v7 p. O: Wenvironments. It can search for min, max, or 'distance' of . X& q9 K$ W/ x. K' m# ?# v5 J
user developed cost function. Very easy to use and hack with 3 L: a1 Q% d! h: Yreasonably good documentation (type help for any function and. V% s; } ^0 D8 r
it should tell you what you need) and will take advantage of & A. w3 E* Y( i
vectorized cost functions. It uses similar syntax to Matlab's- }! W9 c2 d7 e
optimization toolbox. Includes a suite of static and dynamic 3 l, [. Z: x6 _9 W5 z7 vtest functions. It also includes a dedicated PSO based neural 8 W. m9 ]0 X9 L4 l2 c2 z: l8 ^+ ^* d
network trainer for use with Mathwork's neural network toolbox.% U5 d, t* K! R" e; h1 l' R5 R
6 L* U! }: r" i& o" A3 pRun 'DemoPSOBehavior' to explore the various functions, options, 1 D( d2 r2 L# j6 t. }and visualizations. 0 a! y- {4 ?& A# s5 _
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Run 'demoPSOnet' to see a neural net trained with PSO 6 B6 N( B( ]8 s6 v( ~: Y* s
(requires neural net toolbox). 1 e% K. d% N: T8 z- I- c9 l7 W2 C: r* t2 u: X& ~% a3 [+ W0 w' @) j
& a4 p! N8 u, I! H/ R, sThis toolbox is in constant development and I welcome # y/ C, N l6 ]$ s
suggestions. The main program 'pso_Trelea_vectorized.m' lists & A2 K# {& a) a7 s7 |# Bvarious papers you can look at in the comments. + M: I* `: U0 Q# {% B / |" I2 \, F; m" [Usage ideas: to find a global min/max, to optimize training of & u# T$ y) K4 I$ V* q* ~neural nets, error topology change tracking, teaching PSO, 7 n* |7 X6 H9 ~& e
investigate Emergence, tune control systems/filters, paradigm ( b) T- A' X+ ]$ j/ v
for multi-agent interaction, etc.8 |% G2 O: h4 x1 f
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Files included: D9 v9 t1 g8 S1 d1 q0 g) i: l6 X
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** in main directory:4 `2 D5 ?) n) D5 D, b- ~8 p6 W1 R
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0) ReadMe.txt - this file, duh ; ^# r6 H+ V, g, G, k: U1) A Particle Swarm Optimization (PSO) Primer.pdf - powerpoint converted to pdf presentation explaining the very basics of PSO + F- D- \( o) G/ a& E) @5 {2) DemoPSOBehavior.m - demo script, useful to see how the pso main function is called - H0 F, J$ L$ o6 g5 D+ h% E3) 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 y5 |4 k# K+ Q8 l0 r. z
4) goplotpso.m - default plotting routine used by pso algorithm 2 Q; l9 Q& b5 e9 r/ v5) pso_Trelea_vectorized.m - main PSO algorithm function, implements Common, Trelea 1&2, Clerc 1", and an alpha version of tracking environmental changes. : ]/ N9 l* ?) P+ y! x1 i/ A+ O' E/ j8 q+ M; k4 _; t
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** in 'hiddenutils': f& `8 |/ Q {/ |2 t2 b+ O- \
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1) 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 9 w& d0 o! V( P* G* l2 o2) normmat.m - takes a matrix and reformats the data to fit between a new range, very flexible/ C6 `9 Z2 K7 B6 \0 i
3) linear_dyn, spiral_dyn.m - helpers for the dynamic test functions listed in the 'testfunctions' directory $ V! G e+ G' u ' r0 [& H: P" f* Q6 t + j) t) h" ]' m/ L2 @2 b6 D! c/ r: z, y3 u5 N( M0 ?( y
** in 'testfunctions' 5 x* N+ X0 u8 m, C 3 J8 K9 r. r* i4 g4 M9 j, W% vA bunch of useful functions (mostly 2D) for testing. See help for each one for specifics. Here's a list of the names: & y$ U9 P2 t7 b+ j) p) G7 a' r7 S: ~/ e+ _/ E
Static test functions, minima don't change w.r.t. time/iteration: 7 W# X o v- p/ H 1) Ackley : L6 w# |7 U) p 2) Alpine t) g5 j0 @$ V& f
3) DeJong_f2. c! v" H. M/ R+ a( e) x
4) DeJong_f3 ; b" Z( M5 w2 Z" W# \5 N 5) DeJong_f4 6 x# A; ^( X2 H8 n i 6) Foxhole 3 ^0 S& z9 F# K3 W/ M 7) Griewank 0 V0 j' q. {4 Z 8) NDparabola : i( q: n; n7 Y! C4 t 9) Rastrigin, i; A3 X0 M7 b p3 p0 b( M
10) Rosenbrock* P3 W6 a, d' ]/ D6 y
11) Schaffer f6' ~/ P9 m. k. i
12) Schaffer f6 modified (5 f6 functions translated from each other)% M' {0 @! F% D8 d
13) Tripod, {, X9 q9 j' q. U+ K; g. }
: a. @- @5 G2 q! VDynamic test functions, minima/environment evolves over time (NOT iteration, though easily modifed to do so):; s m4 W! O8 Z# X
14) f6_bubbles_dyn 7 |. J, }( Z1 k; T! |15) f6_linear_dyn {( x; a8 A1 B V' d0 @) p) U! G16) f6_spiral_dyn7 }3 Y" W9 A6 V8 t; H3 m( c
: t1 k: t* _ P+ J& C) I6 X ; ~2 j1 f0 n2 d # x' ] V5 F9 E# c5 F** in 'nnet' (all these require Matlab's Neural Net toolbox) 5 y ]" o# U/ E! f/ x) l0 s; j- {# @& z( ?9 p7 k. ^
1) demoPSOnet - standalone demo to show neural net training& j/ P8 c! t! z6 O; w" r
2) trainpso - the neural net toolbox plugin, set net.trainFcn to this9 G! Y, m9 C# I1 a8 S8 n6 c w2 n" [
3) pso_neteval - wrapper used by trainpso to call the main PSO optimizer, this is the cost function that PSO will optimize. b( P) i i1 h) W
4) goplotpso4net - default graphing plugin for trainpso, shows net architecture, relative weight indications, error, and PSO details on run ) S4 z+ `# Z4 ^; }8 f; M5 E 1 l. D3 ^# X7 l+ k+ a0 i T- k6 `) e5 W
