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.* l0 v' \7 w6 E4 G9 w$ p
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May be distributed freely as long as none of the files are 1 ^! z8 R* k. ~2 C& M
modified. 4 \$ F) y+ O% x G5 N ( j! K6 m8 w( F1 n$ `Send suggestions to bkbirge@yahoo.com # ^6 _+ O' F2 W/ [" |( F: K# a$ k9 `: X5 G' G8 { J
Updates will be posted periodically at the Mathworks User . @" U9 ~% L! jContributed Files website (www.mathworks.com) under the " Y& V9 C+ \7 X7 `6 WOptimization category. 0 @. c) I3 k' K- h0 @" O* [- ?' g9 x+ x% j( D/ g
To install: , v7 y' x7 p( M9 [% s# wExtract into any directory you want but make sure the matlab # n' i v/ }9 f/ o% I% p% Gpath points to that directory and the subdirectories - n& L$ M, H: E0 `6 ~4 n'hiddenutils' and 'testfunctions'. X5 i* [0 u! x# L# o7 W
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Enjoy! - Brian Birge9 q& e* H3 t; R: I
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INFO % d: l' w* Q9 U3 A8 |Quick start: just type ... out = pso_Trelea_vectorized('f6',2) & l' i+ `% H8 H; n" x. _% land watch it work!& j3 T" m: f# f2 P l
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This is a PSO toolbox implementing Common, Clerc 1", and 4 G- L7 f) f C' t: N$ l6 [
Trelea types along with an alpha version of tracking changing% J* @% t5 r; ^1 d, X
environments. It can search for min, max, or 'distance' of / j3 S& Q4 W* b I6 V! P5 I
user developed cost function. Very easy to use and hack with " Y2 Q1 D& f: O0 |reasonably good documentation (type help for any function and- d1 x* l9 \5 y; g+ k0 n' a* r
it should tell you what you need) and will take advantage of ! Z9 L9 M; n: y0 k1 X
vectorized cost functions. It uses similar syntax to Matlab's2 \( P7 R1 h" r% e
optimization toolbox. Includes a suite of static and dynamic & l5 Z" j, ^. c( [* J4 ]8 u
test functions. It also includes a dedicated PSO based neural . R" U4 v( a4 m; M1 I, u; J u9 v8 I. Cnetwork trainer for use with Mathwork's neural network toolbox. ; h# `$ P2 p3 @5 | 9 N' f$ A( @+ l% t3 KRun 'DemoPSOBehavior' to explore the various functions, options, 7 W" I3 H5 ~' l4 ^7 I' ?
and visualizations. 5 Q5 F2 Q/ K; i s1 {9 v7 p$ Y, F7 k6 ~. f
Run 'demoPSOnet' to see a neural net trained with PSO 5 c* m* _( t: t1 [# [. E4 q! M# m
(requires neural net toolbox). 5 c% M8 P* @' Q1 ]- W/ m% N o+ m* K- a; H; f& ?& W4 R" \
0 l/ [! n5 t/ y4 {. uThis toolbox is in constant development and I welcome 8 p/ u- d5 F/ u+ isuggestions. The main program 'pso_Trelea_vectorized.m' lists 5 j& O; p( \7 F4 |, B# ~. r
various papers you can look at in the comments.) M6 M# _% n5 `) P
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Usage ideas: to find a global min/max, to optimize training of % E/ X- B. V! L6 eneural nets, error topology change tracking, teaching PSO, # a' B3 [0 s+ {* O' v5 u8 {investigate Emergence, tune control systems/filters, paradigm U; S3 T# G3 Y$ B1 D$ A# o" S2 o
for multi-agent interaction, etc. ) I" C% M( M2 ^6 y. w# `8 R3 `3 e ) N* f, U+ k3 t* O------------------------------------------------------------- / v+ w) b3 b# z2 \-------------------------------------------------------------, ?& R# _( Z. o" z% W& @+ @
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** in main directory:8 ]- P; S; W1 d0 d. s9 q$ b' H$ {
% n9 i+ q& r5 H7 D' A* C; r0) ReadMe.txt - this file, duh + B! }% R, H9 y. d: [1) A Particle Swarm Optimization (PSO) Primer.pdf - powerpoint converted to pdf presentation explaining the very basics of PSO8 a6 Z3 b& t1 G! O& [
2) DemoPSOBehavior.m - demo script, useful to see how the pso main function is called6 `% }6 D F3 V6 U/ l( F( Q5 w
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 # o+ c: Z: }6 u, @, n7 F' ]4) goplotpso.m - default plotting routine used by pso algorithm 8 l f; Z" w5 c5) pso_Trelea_vectorized.m - main PSO algorithm function, implements Common, Trelea 1&2, Clerc 1", and an alpha version of tracking environmental changes.; C3 ~9 w3 A+ }8 K) X' d
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* H7 S+ `0 R# ^# | m . ^7 J* I* H( m! a& e2 ^! b** in 'hiddenutils', ^+ C. P. R3 D+ A) S/ R7 i
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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 algo7 r8 ?. x2 r( \) g* h% q# f) P
2) normmat.m - takes a matrix and reformats the data to fit between a new range, very flexible8 }6 d$ r: a" {% d( q
3) linear_dyn, spiral_dyn.m - helpers for the dynamic test functions listed in the 'testfunctions' directory& E+ q4 A! b: F, ^' w0 Y
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** in 'testfunctions' 0 j) d* M1 D6 U1 [+ ]8 M1 f- W' x' c( u , o% [3 ?, ^. UA bunch of useful functions (mostly 2D) for testing. See help for each one for specifics. Here's a list of the names:* c! ]8 k; r, C# Z% [5 F
' d- K4 v/ d* K* zStatic test functions, minima don't change w.r.t. time/iteration:9 e: J5 Z+ O+ J" X- b: @/ _9 S4 y9 l2 T
1) Ackley I7 J9 O6 _% a B) b, y! }
2) Alpine + l1 K5 F3 o5 e7 e 3) DeJong_f2 * U) t- g2 `+ q% J2 A 4) DeJong_f3 1 X/ A) N3 S, | 5) DeJong_f40 R9 _' ?4 y4 H; v
6) Foxhole . O/ C! N4 @& ]5 t$ n6 C$ K( d, J1 V% I 7) Griewank 3 K7 {8 ?* p$ K! }6 }* t7 Q 8) NDparabola& f" T- k/ E+ l! q4 `! K
9) Rastrigin & f/ B, S" N6 G10) Rosenbrock 7 p9 P! I7 g* r11) Schaffer f6! r: Y5 i+ |. E% v: p) P
12) Schaffer f6 modified (5 f6 functions translated from each other) - T2 D3 D& a [ k! S, M13) Tripod4 I1 N/ P8 z# n! Y( _8 T
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Dynamic test functions, minima/environment evolves over time (NOT iteration, though easily modifed to do so):* x# d$ |7 L2 G( m: P
14) f6_bubbles_dyn0 z& T5 u$ L4 F5 {9 b" H; g
15) f6_linear_dyn" L- B1 Q4 O- J4 m8 W f: [; j% Z
16) f6_spiral_dyn - A1 O0 O8 T. \; s; |0 Y$ @0 `. h: Y# l0 Q2 P. e
3 Y3 @/ K/ M8 G: r- y+ S ; c, c; ?( O& c% y- A4 u** in 'nnet' (all these require Matlab's Neural Net toolbox)) L" f3 s, T/ E& G
! j; T$ o# ~' z/ t9 \ 1) demoPSOnet - standalone demo to show neural net training0 l& q5 F$ z6 z
2) trainpso - the neural net toolbox plugin, set net.trainFcn to this Z" q4 [9 S: i; I) M' W, j 3) pso_neteval - wrapper used by trainpso to call the main PSO optimizer, this is the cost function that PSO will optimize, L( y; i! d. @7 m9 w0 t
4) goplotpso4net - default graphing plugin for trainpso, shows net architecture, relative weight indications, error, and PSO details on run( t1 S: P6 O+ [8 m$ E2 M
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