#Introduction
Particle swarm optimization (PSO) is a derivative-free global optimum solver. It is inspired by the surprisingly organized behaviour of large groups of simple animals, such as flocks of birds, schools of fish, or swarms of locusts. The individual creatures, or "particles", in this algorithm are primitive, knowing only four simple things: 1 & 2) their own current location in the search space and fitness value, 3) their previous personal best location, and 4) the overall best location found by all the particles in the "swarm". There are no gradients or Hessians to calculate. Each particle continually adjusts its speed and trajectory in the search space based on this information, moving closer towards the global optimum with each iteration. As seen in nature, this computational swarm displays a remarkable level of coherence and coordination despite the simplicity of its individual particles.
#Ease of Use
If you are already using the Genetic Algorithm (GA) included with MATLAB's Global Optimization Toolbox, then this PSO toolbox will save you a great deal of time. It can be called from the MATLAB command line using the same syntax as the GA, with some additional options specific to PSO. This will allow a high degree of code re-usability between the PSO toolbox and the GA toolbox. Certain GA-specific parameters such as cross-over and mutation functions will obviously not be applicable to the PSO algorithm. However, many of the commonly used options for the Genetic Algorithm Toolbox may be used interchangeably with PSO since they are both iterative population-based solvers. See >> help pso (from the ./psopt directory) for more details.
#Features
* NEW: support for distributed computing using MATLAB's parallel computing toolbox.
* Full support for bounded, linear, and nonlinear constraints.
* Modular and customizable.
* Binary optimization. See PSOBINARY function for details.
* Vectorized fitness functions.
* Solver parameters controlled using 'options' structure similar to existing MATLAB optimization solvers.
* User-defined custom plots may be written using same template as GA plotting functions.
* Another optimization solver may be called as a "hybrid function" to refine PSO results.
A demo function is included, with a small library of test functions. To run the demo, from the psopt directory, call >> psodemo with no inputs or outputs.
New features and bug fixes will continue to be released until this is made redundant by the release of an official MATLAB PSO toolbox. Bug reports and feature requests are welcome.
Special thanks to the following people for contributing code and bug fixes:
* Ben Xin Kang of the University of Hong Kong
* Christian Hansen of the University of Hannover
* Erik Schreurs from the MATLAB Central community
* J. Oliver of Brigham Young University
* Michael Johnston of the IRIS toolbox
* Ziqiang (Kevin) Chen
#Bibliography
* J Kennedy, RC Eberhart, YH Shi. Swarm Intelligence. Academic Press, 2001.
* Particle Swarm Optimization. http://en.wikipedia.org/wiki/Particle_swarm_optimization
* RE Perez, K Behdinan. Particle swarm approach for structural design optimization. Computers and Structures 85 (2007) 1579–1588.
* SM Mikki, AA Kishk. Particle Swarm Optimization: A Physics-Based Approach. Morgan & Claypool, 2008.
#Addendum A
Nonlinear inequality constraints in the form c(x) ≤ 0 and nonlinear equality constraints of the form ceq(x) = 0 have now been fully implemented. The 'penalize' constraint boundary enforcement method is now default. It has been redesigned and tested extensively, and should work with all types of constraints.
See the following document for the proper syntax for defining nonlinear constraint functions: http://www.mathworks.com/help/optim/ug/writing-constraints.html#brhkghv-16.
To see a demonstration of nonlinear inequality constraints using a quadrifolium overlaid on Rosenbrock's function, run PSODEMO and choose 'nonlinearconstrdemo' as the test function.
#Addendum B
See the following guide in the GA toolbox documentation to get started on using the parallel computing toolbox.
http://www.mathworks.com/help/gads/genetic-algorithm-options.html#f17234
#Addendum C
If you are a beginner hoping to learn to use this toolbox for work or school, here are some essential readings:
* MATLAB's Optimization Toolbox: http://www.mathworks.com/help/optim/index.html
* MATLAB's Global Optimization Toolbox: http://www.mathworks.com/help/gads/index.html
* MATLAB's Genetic Algorithm: http://www.mathworks.com/help/gads/genetic-algorithm.html
#Addendum D
There is now a particle swarm optimizer included with the Global Optimization Toolbox. It does not seem to handle constraints at this time. If you have a recent version of the Global Optimization Toolbox installed, you will need to set the path appropriately in your code to use this toolbox.
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数学建模中常用算法的MATLAB实现.zip
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数学建模中常用算法的MATLAB实现.zip (390个子文件)
SAPSO.asv 1KB
YSPSO.asv 1KB
Thumbs.db 14KB
PSO工具箱使用简介.doc 96KB
利用主成分确定权重.docx 347KB
加权函数确定.docx 258KB
featool-multiphysics-setup.exe 48.92MB
heattransfer.fea 58KB
pso_Trelea_vectorized.m 22KB
pso.m 19KB
heattransfer.m 18KB
pso.m 14KB
pso.m 13KB
trainpso.m 11KB
psooptimset.m 9KB
findRules.m 8KB
findRules.m 8KB
findRules.m 8KB
goplotpso4net.m 7KB
peaksPlotIterates.m 7KB
main.m 6KB
psooptimset.m 6KB
goplotpso.m 5KB
psocheckinitialpopulation.m 5KB
goplotpso4demo.m 5KB
DemoPSOBehavior.m 5KB
psocheckbounds.m 4KB
normmat.m 4KB
normmat.m 4KB
y3_5.m 4KB
tsp_ga.m 4KB
id3.m 4KB
id3.m 4KB
id3.m 4KB
psoplotswarm.m 4KB
one_dimensional_qualified_condition.m 4KB
main.m 4KB
y20_2.m 4KB
psoplotswarm.m 3KB
psoboundspenalize.m 3KB
GA.m 3KB
main1.m 3KB
psorunhybridfcn.m 3KB
psocheckbounds.m 3KB
y20_1.m 3KB
main.m 3KB
psocheckinitialpopulation.m 2KB
maxflow.m 2KB
Bmixmax.m 2KB
psoboundssoft.m 2KB
gaPeaksExample.m 2KB
psodemo.m 2KB
psoiterate.m 2KB
y29_4.m 2KB
one_dimensional_qualified_condition.m 2KB
main.m 2KB
ployinterp_column.m 2KB
psorunhybridfcn.m 2KB
ahp.m 2KB
overlaysurface.m 2KB
psoplotswarmsurf.m 2KB
overlaysurface.m 2KB
psoplotswarmsurf.m 2KB
demoPSOnet.m 2KB
psogenerateoutputmessage.m 2KB
mintreek.m 2KB
psoboundsabsorb.m 2KB
SimuAPSO.m 2KB
y2_4.m 2KB
y9_2.m 2KB
bestselect.m 2KB
unqualified_condition.m 2KB
print_tree.m 2KB
print_tree.m 2KB
print_tree.m 2KB
psoplotbestf.m 2KB
psodemo.m 2KB
y3_4.m 1KB
fpdfprinter.m 1KB
fpdfprinter.m 1KB
y16.m 1KB
f6_bubbles_dyn.m 1KB
psoplotbestf.m 1KB
psobinary.m 1KB
minRoute.m 1KB
crossover.m 1KB
SAPSO.m 1KB
Cross.m 1KB
trans2matrix.m 1KB
trans2matrix.m 1KB
trans2matrix.m 1KB
nonlinearconstrdemo.m 1KB
Mutation.m 1KB
YSPSO.m 1KB
psogenerateoutputmessage.m 1KB
Cross.m 1KB
SAPSO.m 1KB
Foxhole.m 1KB
main.m 1KB
RandWPSO.m 1KB
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