PSOtoolbox
所属分类：超算/并行计算
开发工具：matlab
文件大小：863KB
下载次数：186
上传日期：2009-02-20 09:21:45
上 传 者：
TYUST
说明： 微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。
(Particle Swarm Optimization [PSO] are equal by Kennedy and Eberhart in 1995 developed an evolutionary computing technology, from preying on the birds of the simulation process. PSO with genetic algorithm is similar to an iterative optimization-based tool, but the use of genetic algorithms and genetic manipulation of different sub-optimize the use of groups between the various entities within the " collaboration" and " competitive" relationship, according to themselves and their competition the flying experience, adjust their behavior. Comparison with genetic algorithms, PSO has the advantage of being simple and easy and did not realize the need to adjust the parameters much. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications.)
文件列表:
微粒群优化算法(PSO)工具箱\PSOt\A Particle Swarm Optimization (PSO) Primer.pdf (741121, 2003-05-22)
微粒群优化算法(PSO)工具箱\PSOt\DemoPSOBehavior.m (4862, 2006-03-06)
微粒群优化算法(PSO)工具箱\PSOt\goplotpso.m (5788, 2006-03-14)
微粒群优化算法(PSO)工具箱\PSOt\goplotpso4demo.m (4899, 2006-03-06)
微粒群优化算法(PSO)工具箱\PSOt\hiddenutils\forcecol.m (172, 2004-04-27)
微粒群优化算法(PSO)工具箱\PSOt\hiddenutils\forcerow.m (181, 2004-04-27)
微粒群优化算法(PSO)工具箱\PSOt\hiddenutils\linear_dyn.m (749, 2004-08-23)
微粒群优化算法(PSO)工具箱\PSOt\hiddenutils\normmat.m (4588, 2006-03-17)
微粒群优化算法(PSO)工具箱\PSOt\hiddenutils\spiral_dyn.m (841, 2004-08-27)
微粒群优化算法(PSO)工具箱\PSOt\nnet\demoPSOnet.m (1857, 2006-03-17)
微粒群优化算法(PSO)工具箱\PSOt\nnet\goplotpso4net.m (7808, 2006-03-14)
微粒群优化算法(PSO)工具箱\PSOt\nnet\pso_neteval.m (836, 2006-03-10)
微粒群优化算法(PSO)工具箱\PSOt\nnet\trainpso.m (11664, 2006-03-17)
微粒群优化算法(PSO)工具箱\PSOt\pso_Trelea_vectorized.m (22223, 2006-03-17)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\ackley.m (871, 2004-08-23)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\alpine.m (639, 2004-08-19)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\DeJong_f2.m (732, 2004-08-13)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\DeJong_f3.m (506, 2004-08-19)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\DeJong_f4.m (1004, 2004-08-13)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\f6.m (314, 2005-06-27)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\f6mod.m (700, 2004-08-23)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\f6_bubbles_dyn.m (1510, 2004-08-26)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\f6_linear_dyn.m (617, 2006-02-27)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\f6_spiral_dyn.m (872, 2006-02-27)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\Foxhole.m (1278, 2004-08-13)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\Griewank.m (1214, 2004-08-13)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\NDparabola.m (663, 2004-08-19)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\Rastrigin.m (521, 2004-08-19)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\Rosenbrock.m (723, 2004-08-19)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions\tripod.m (895, 2004-08-19)
微粒群优化算法(PSO)工具箱\PSO工具箱使用简介\PSO工具箱使用简介.doc (101376, 2007-06-01)
微粒群优化算法(PSO)工具箱\PSO工具箱使用简介\test_func.m (569, 2007-06-01)
微粒群优化算法(PSO)工具箱\PSO工具箱使用简介\test_main.m (339, 2007-06-01)
微粒群优化算法(PSO)工具箱\PSO工具箱使用简介.rar (57941, 2008-07-04)
微粒群优化算法(PSO)工具箱\rand_list.txt (10006, 2001-08-25)
微粒群优化算法(PSO)工具箱\shuoming.txt (426, 2008-07-04)
微粒群优化算法(PSO)工具箱\PSOt\hiddenutils (0, 2008-11-24)
微粒群优化算法(PSO)工具箱\PSOt\nnet (0, 2008-11-24)
微粒群优化算法(PSO)工具箱\PSOt\testfunctions (0, 2008-11-24)
... ...
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PSOt, particle swarm optimization toolbox for matlab.
May be distributed freely as long as none of the files are
modified.
Send suggestions to bkbirge@yahoo.com
Updates will be posted periodically at the Mathworks User
Contributed Files website (www.mathworks.com) under the
Optimization category.
To install:
Extract into any directory you want but make sure the matlab
path points to that directory and the subdirectories
'hiddenutils' and 'testfunctions'.
Enjoy! - Brian Birge
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INFO
Quick start: just type ... out = pso_Trelea_vectorized('f6',2)
and watch it work!
This is a PSO toolbox implementing Common, Clerc 1", and
Trelea types along with an alpha version of tracking changing
environments. It can search for min, max, or 'distance' of
user developed cost function. Very easy to use and hack with
reasonably good documentation (type help for any function and
it should tell you what you need) and will take advantage of
vectorized cost functions. It uses similar syntax to Matlab's
optimization toolbox. Includes a suite of static and dynamic
test functions. It also includes a dedicated PSO based neural
network trainer for use with Mathwork's neural network toolbox.
Run 'DemoPSOBehavior' to explore the various functions, options,
and visualizations.
Run 'demoPSOnet' to see a neural net trained with PSO
(requires neural net toolbox).
This toolbox is in constant development and I welcome
suggestions. The main program 'pso_Trelea_vectorized.m' lists
various papers you can look at in the comments.
Usage ideas: to find a global min/max, to optimize training of
neural nets, error topology change tracking, teaching PSO,
investigate Emergence, tune control systems/filters, paradigm
for multi-agent interaction, etc.
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Files included:
** in main directory:
0) ReadMe.txt - this file, duh
1) A Particle Swarm Optimization (PSO) Primer.pdf - powerpoint converted to pdf presentation explaining the very basics of PSO
2) DemoPSOBehavior.m - demo script, useful to see how the pso main function is called
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
4) goplotpso.m - default plotting routine used by pso algorithm
5) pso_Trelea_vectorized.m - main PSO algorithm function, implements Common, Trelea 1&2, Clerc 1", and an alpha version of tracking environmental changes.
** in 'hiddenutils'
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
2) normmat.m - takes a matrix and reformats the data to fit between a new range, very flexible
3) linear_dyn, spiral_dyn.m - helpers for the dynamic test functions listed in the 'testfunctions' directory
** in 'testfunctions'
A bunch of useful functions (mostly 2D) for testing. See help for each one for specifics. Here's a list of the names:
Static test functions, minima don't change w.r.t. time/iteration:
1) Ackley
2) Alpine
3) DeJong_f2
4) DeJong_f3
5) DeJong_f4
6) Foxhole
7) Griewank
8) NDparabola
9) Rastrigin
10) Rosenbrock
11) Schaffer f6
12) Schaffer f6 modified (5 f6 functions translated from each other)
13) Tripod
Dynamic test functions, minima/environment evolves over time (NOT iteration, though easily modifed to do so):
14) f6_bubbles_dyn
15) f6_linear_dyn
16) f6_spiral_dyn
** in 'nnet' (all these require Matlab's Neural Net toolbox)
1) demoPSOnet - standalone demo to show neural net training
2) trainpso - the neural net toolbox plugin, set net.trainFcn to this
3) pso_neteval - wrapper used by trainpso to call the main PSO optimizer, this is the cost function that PSO will optimize
4) goplotpso4net - default graphing plugin for trainpso, shows net architecture, relative weight indications, error, and PSO details on run
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