Particle Swarm Optimization with Enhanced Global Search and Local Search

Abstract

In order to mitigate the problems of premature convergence and low search accuracy that exist in traditional particle swarm optimization (PSO), this paper presents PSO with enhanced global search and local search (EGLPSO). In EGLPSO, most of the particles would be concentrated in global search at the beginning. Along with the iteration, the particles would slowly focus on local search. A new updating strategy would be used for global search, and a partial mutation strategy is applied to the leader particle of local search for a better position. During each iteration, the best particle of global search would exchange information with some particles of local search. EGLPSO is tested on a set of 12 benchmark functions, and it is also compared with other four PSO variants and another six well-known PSO variants. The experimental results showed that EGLPSO can greatly improve the performance of traditional PSO in terms of search accuracy, search efficiency, and global optimality.

Keywords

• particle swarm optimization
• optimization
• pso
• metaheuristics