Particle Swarm Optimization Performance for Unconstrained Optimization Problems
Snehal Kamalapur, Varsha Patil, Shirish Sane
Journal of Networking Technology, Vol. 1, No. 2, 2010.
DOI: 
Link: http://www.dline.info/jnt/fulltext/v1n2/4.pdf
Abstract:
Particle swarm Optimization (PSO) is mainly inspired by social behavior patterns of organisms that live and interact within large groups. The term PSO refers to a relatively new family of algorithms that is used to find optimal or near to optimal solutions to numerical and qualitative problems. It is an optimization paradigm that simulates the ability of human to process knowledge. The capability of PSO method to address the maximization and minimization unconstrained problems is investigated through numerous experiments on different test problems. Results obtained are reported. The two variants PSO-IW (Inertia Weight) and PSO-IC (Inertia weight and Constriction factor) are used for the experiments. Conclusions are derived. These variants exhibit different performance for different test problems.