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. 4 o' V* a; p3 K. I3 Q- \: Y8 J* J6 }/ z3 o8 K
May be distributed freely as long as none of the files are 9 H$ f) T9 [8 n: b
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Send suggestions to bkbirge@yahoo.com & |( C6 ~& N6 } R
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Contributed Files website (www.mathworks.com) under the ; y b( _+ ]0 S, z' @! f3 X
Optimization category. j7 P0 r z5 ^/ V+ c& M , D: R. ~4 D8 v" E9 r8 t- p0 FTo install: ( S* E! l: f0 K X. UExtract into any directory you want but make sure the matlab ! u4 y- A/ ^( r0 h8 c# Y' Ipath points to that directory and the subdirectories 6 M" m8 G# P. s9 n' b q
'hiddenutils' and 'testfunctions'. ' `, O5 x9 r. [$ o8 N5 s, Q
6 @7 Y! _+ i/ O6 L3 a# d4 |2 D; p; rEnjoy! - Brian Birge 1 v" M2 `- i+ r7 r, {/ l2 F) M* W' m' S( i( V, P/ `4 l; c
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INFO 4 M# ]$ t6 y9 r$ ~- s2 `" V4 u- S) QQuick start: just type ... out = pso_Trelea_vectorized('f6',2) ! L8 ?4 O, f; O& A1 tand watch it work! 8 s3 H0 X. }, Q L. v$ | ?! P. U+ J( KThis is a PSO toolbox implementing Common, Clerc 1", and " F- ?2 ~5 H& A- HTrelea types along with an alpha version of tracking changing 2 f. |7 ?. x+ C1 H" K# eenvironments. It can search for min, max, or 'distance' of " `3 S5 y" N% W! euser developed cost function. Very easy to use and hack with 9 D* S( R5 ]# ?% k1 z
reasonably good documentation (type help for any function and4 G x- w# p% }/ N
it should tell you what you need) and will take advantage of : H: j# F( S0 w) N& @- ^) ovectorized cost functions. It uses similar syntax to Matlab's 8 `6 i1 l0 _# B1 x( Z$ _optimization toolbox. Includes a suite of static and dynamic : o2 x. E) I8 H9 w3 H0 }- xtest functions. It also includes a dedicated PSO based neural * T- ?" R8 @+ r0 nnetwork trainer for use with Mathwork's neural network toolbox.$ j, q; C4 h' a9 Q# \) B0 z1 s" ]! k
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Run 'DemoPSOBehavior' to explore the various functions, options, ! |8 i* W+ H3 dand visualizations. 5 _5 e) \- u- r % i, @! _+ U) j# F8 XRun 'demoPSOnet' to see a neural net trained with PSO 2 T, t; E% t3 L0 d9 A) U! P
(requires neural net toolbox).0 \' x$ e" _! T/ C- k
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; \: @# P, n% j4 V8 N0 ^1 P- p6 S/ eThis toolbox is in constant development and I welcome 1 J7 i3 _! f5 n" E( P+ j. wsuggestions. The main program 'pso_Trelea_vectorized.m' lists : i4 O% k* `& @# F. }+ I& x: }: jvarious papers you can look at in the comments.2 N, Z0 O. K' ?- r
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Usage ideas: to find a global min/max, to optimize training of % \! i% Z1 f0 f# m7 }neural nets, error topology change tracking, teaching PSO, 3 J/ D; w, Z' U1 @
investigate Emergence, tune control systems/filters, paradigm 5 B, m- M; Y* B! L$ b; t9 Z
for multi-agent interaction, etc.9 t. A* i' W. ~( s% a( X
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------------------------------------------------------------- ! M9 \0 }& m: @6 |: b1 E) k8 x6 B 4 |6 I) t2 l: u: k - j9 I: {" {7 y2 mFiles included: 3 [( j! m- ^2 B" m/ c % x# A% \/ e2 Z# {6 P, B0 z- d0 W: D) s* s
** in main directory: 1 j3 U% U: N L a$ e1 ~+ X4 ]) W7 t3 Y+ L
0) ReadMe.txt - this file, duh ' `' A3 ~/ }6 R# U" h1 T1) A Particle Swarm Optimization (PSO) Primer.pdf - powerpoint converted to pdf presentation explaining the very basics of PSO * c9 l$ B( I! U2) DemoPSOBehavior.m - demo script, useful to see how the pso main function is called1 R" L1 y* {5 B( _5 M @# ]
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 lot9 I. k3 s9 ]5 v1 w
4) goplotpso.m - default plotting routine used by pso algorithm 1 }. z+ u' C9 r5) pso_Trelea_vectorized.m - main PSO algorithm function, implements Common, Trelea 1&2, Clerc 1", and an alpha version of tracking environmental changes.+ _/ I! _% r7 a5 H S) L2 B
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** in 'hiddenutils' # f0 a) H4 [' ], H) L- ^3 w $ B+ t; u5 ~, A0 R+ \/ m7 l1) 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 ) Q$ w8 ?9 M2 N0 K2 b+ p3 T! u2) normmat.m - takes a matrix and reformats the data to fit between a new range, very flexible ' g( `0 h1 a R% X3) linear_dyn, spiral_dyn.m - helpers for the dynamic test functions listed in the 'testfunctions' directory5 J7 ], U+ Q7 C
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** in 'testfunctions'. o& l; q- _' ]5 E; c
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A bunch of useful functions (mostly 2D) for testing. See help for each one for specifics. Here's a list of the names: * b0 b. s9 }4 N% m. L8 ~& Z! X 9 x# t0 S; ^" e3 ]Static test functions, minima don't change w.r.t. time/iteration: % X5 n, S4 A: u; G$ y 1) Ackley) j" T8 g1 P3 M6 H; Y' h6 G4 w0 ^4 G7 D
2) Alpine2 c% w& e) P5 V/ _& _' w% P5 ^
3) DeJong_f2% S* b! h; H) }6 N
4) DeJong_f3 ) Q" D f4 N- x8 g1 O8 r 5) DeJong_f40 A1 d0 s* m3 m" y% M+ l6 G
6) Foxhole 9 H! z2 E8 B9 N9 p 7) Griewank) _. s; Y* R2 \: G$ G
8) NDparabola# c; p5 A. K: g3 N
9) Rastrigin * x' w' v0 Q$ J; `2 C- r7 X, j10) Rosenbrock- N- F. B6 T' h& n: C
11) Schaffer f6 2 ]5 `; ?' c5 e$ L" _12) Schaffer f6 modified (5 f6 functions translated from each other)" b; U; E4 S4 w; M( T$ l
13) Tripod ) {' u$ H4 Z0 |3 I# e* V/ d- M, X: T
Dynamic test functions, minima/environment evolves over time (NOT iteration, though easily modifed to do so):. s' d3 B( O: u2 u/ @
14) f6_bubbles_dyn; P: u3 R' H F" ?1 S0 ]
15) f6_linear_dyn5 Y5 U% O, y, I3 ~6 ~
16) f6_spiral_dyn 3 P" Y' O0 V, a" y4 D 3 r7 T3 x# @ ]1 k5 O3 { ! s! B' f# _, q7 `; r5 v5 Y/ @, h 1 v0 V$ p5 u9 H8 T** in 'nnet' (all these require Matlab's Neural Net toolbox) 8 J! E! w7 n9 B ) p* N9 _* [6 N8 m' T0 k3 z' u 1) demoPSOnet - standalone demo to show neural net training 0 J: x G8 n/ i9 ?; E9 \/ p 2) trainpso - the neural net toolbox plugin, set net.trainFcn to this. @! n, I+ d0 B; \& z8 P* w
3) pso_neteval - wrapper used by trainpso to call the main PSO optimizer, this is the cost function that PSO will optimize# i2 z w- z2 R" U/ V
4) goplotpso4net - default graphing plugin for trainpso, shows net architecture, relative weight indications, error, and PSO details on run" d4 m( b5 P6 A% F$ z( h& z' r
