Particle Swarm Optimization (PSO) is an intelligent optimization algorithm based on the Swarm Intelligence. It is based on a simple mathematical model, developed by Kennedy and Eberhart in 1995, to describe the social behavior of birds and fish. The model relies mostly on the basic principles of self-organization which is used to describe the dynamics of complex systems.
Swarm intelligence is ability of such systems, to achieve a higher level of intelligence, which is absolutely unreachable for any of system units. For example, a flock of birds as a society, has very complex behavior patterns, which is beyond the intelligence level of any of birds in the flock, of course. However, this complex patterns are created via simple and repetitive tasks, performed by any of members in the flock.
PSO utilizes a very simplified model of social behavior to solve the optimization problems, in a cooperative and intelligent framework. PSO is one of the most useful and famous metaheuristics and it is successfully applied to various optimization problems. If you would like to read more about PSO, you can see the related article on Wikipedia (here).
In this post, we are going to share with you a complete implementation of Particle Swarm Optimization (PSO) in MATLAB. The algorithm is implemented in a structured manner and if you are familiar with MATLAB programming language, you will find it easy, to use the codes in your research projects.
The download link of this project follows.
Implementation of Particle Swarm Optimization (PSO) in MATLABDownload