Parallelization of Particle Swarm Optimization and Its Implementation on Scalable Multi-core Architecture

The Particle Swarm Optimization (PSO) is a stochastic, population-based algorithm for search and optimization from a multidimensional space. Many engineering design problems in real life have complicated optimization functions, which require massive computational power, to solve in reasonable time when implemented sequentially. Thus, scalable parallel implementations are required to speed up these algorithms, and reduce the over-all design process time. In this paper, we present a model for parallelization of PSO algorithm, and its implementation on Cell Broadband Engine architecture.