Sam版Matlab粒子群PSO工具已经更新

显著亮点

感兴趣的同学最好先去 Mathworks文件交换站原始链接 阅读一下作者的说明和以往的一些尤其是作者有答复的讨论。——特别说明的是，我通常没有免费的时间（free time）解答跟工具箱使用类似的问题；如有问题经自己反复努力之后仍解决不掉，建议直接注册ID之后到该文件原始网站留言讨论。

这次最大的更新是支持除了bound上下边界约束之外的其它类型的线性和非线性约束条件。名字也从Another Particle Swarm Toolbox改为Constrained Particle Swarm Optimization

注意它的别名：

Implementation of a PSO algorithm with the same syntax as the Genetic Algorithm Toolbox.

也就是，它的语法或使用方法跟Matlab的基因算法（遗传算法）工具GA非常相似。所以，如果熟悉了matlab中的GA，则学习这个工具就非常驾轻就熟。——我除了写写博客，通常不提供免费的辅导；如果有特别的需要，在预付费的情况下，可以酌情辅导。请勿私信关于该工具如何使用方面的问题。

英文版介绍

下载地址（需要注册了现在）

Description

Previously titled “Another Particle Swarm Toolbox”
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.

（很多人老跑来问我，这个工具箱怎么用；其实，它使用方面跟Matlab自带的GA toolbox极其相似,但似乎还更简单方便一些；如果要学习,建议先学习matlab的GA工具箱(基因算法,遗传算法),中英文的材料和文档很多; 而这个工具箱, 不但有详细的描述解释，而且代码注释也很详细；代码本身可读性完全不亚于mathworks官方的代码；……所以，如果这样前提下您这个还要做伸手党，请先付费）

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.
Acknowledgements

Particle Swarm Optimization Toolbox and Test Functions For Global Optimization Algorithms inspired this file.

This file inspired Co Blade: Software For Analysis And Design Of Composite Blades.
Required Products Optimization Toolbox
MATLAB release MATLAB 8.6 (R2015b)
Other requirements Familiarity with the Genetic Algorithm and Direct Search Toolbox would help in using this package. Tested on Mac OSX 10.11.3; will probably work for other platforms.