The original PSO algorithm is discovered through simplified social model simulation (Shi, 2004). PSO is a simple concept adapted from nature decentralized and self-organized systems such as choreography of a bird flock and fishing schooling. PSO is a population-based algorithm in which individual particles work together to solve a given problem. In PSO, physical position is not an important factor. The Population (or swarm) and the member called particle is initialized by assigning random positions and velocities and potential solutions are then flown through the hyperspace. The particles learn over time in response to their own experience and the experience of the other particles in their group (Ferguson, 2004). As mentioned before, PSO was introduced by Kennedy and Eberhart.
However in 1995, nowadays this concept has been explored by many other researchers around the globe and has been applied in many applications. Below are some application examples using PSO for optimization:
Application of Particle Swarm Optimization to design the electromagnetic absorbers by Suomin Cui* and Daniel S. Weile. (2005) Dept. of Electrical & Computer Engineering, University of Delaware. The synchronous PSO was applied to optimize multilayer coatings and polygonal absorbers for wide band frequency and/or wide incident range.
Human Tremor Analysis Using Particle Swarm Optimization by Russell C., Eberhart, and Xiaohui Hu (1999) where they present methods for the analysis of human tremor using particle swarm optimization. Two forms of human tremor are addressed which are essential tremor and Parkinson's disease.
Particle Swarm Optimization methods for pattern recognition and image processing by Mahamed G. H. Omran (2004) where PSO has been used to classify objects into different categories.