A particle swarm optimization using local stochastic search and enhancing diversity for continuous optimization

The particle swarm optimizer (PSO) is a swarm intelligence based on heuristic optimization technique that can be applied to a wide range of problems. After analyzing the dynamics of traditional PSO, this paper presents a new PSO variant on the basis of local stochastic search strategy (LSSPSO) for performance improvement. This is encouraged by a social phenomenon that everyone wants to first exceed the nearest superior and then all superior. Specifically, LSSPSO employs a local stochastic search to adjust inertia weight in terms of keeping a balance between the diversity and the convergence speed, aiming to improve the performance of traditional PSO. Experiments conducted on unimodal and multimodal test functions demonstrate the effectiveness of LSSPSO in solving multiple benchmark problems as compared to several other PSO variants.