A Particle Swarm Optimization Using Local Stochastic Search for Continuous Optimization
The particle swarm optimizer (PSO) is a swarm intelligence based heuristic optimization technique that can be applied to a wide range of problems. After analyzing the dynamics of tranditioal PSO, this paper presents a new PSO variant based on local stochastic search strategy (LSSPSO) for performance enhancement. This is inspired by a social phenomenon that everyone wants to first exceed the nearest superior and then all superior. Specifically, LSSPSO adopts 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 tranditioal 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.
Keywordsparticle swarm optimization continuous optimization local stochastic search
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