Abstract—Particle Swarm Optimization (PSO) algorithm has been widely used in various engineering problems because of its simplicity and efficiency. However, the PSO has a problem of premature convergence, due to the lack of diversity. The performance of the PSO algorithm can be further improved by hybrid techniques. There are numerous hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristic algorithms such as genetic algorithm (GA), ant colony optimization (ACO) just to name a few. In this paper, we present some of the commonly used hybrid PSO algorithms and study the performance of them through typical nonlinear optimization problems.

Index Terms—Particle swarm optimization, hybrid PSO,swarm intelligence

M. F. Ercan is with the School of Electrical and Electronic Engineering,Singapore Polytechnic, Singapore 139651 (e-mail: mfercan@sp.edu.sg).
X. Li is with the School of Information Science and Engineering,Central South University, Changsha, 410083, China (e-mail:xl_huse@126.com).

Cite: M. Fikret Ercan and Xiang Li, "Particle Swarm Optimization and Its Hybrids," International Journal of Computer and Communication Engineering vol. 2, no. 1, pp. 52-55 , 2013.