Adaptive particle swarm optimization with feedback control of diversity

Authors:
Jing Jie;Jianchao Zeng;Chongzhao Han
Affiliations:
,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an City, China;Division of System Simulation & Computer Application, Taiyuan University of Science & Technology, Taiyuan City, China;School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an City, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Year:
2006

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Abstract

Swarm-diversity is an important factor influencing the global convergence of particle swarm optimization (PSO). In order to overcome the premature convergence, the paper introduced a negative feedback mechanism into particle swarm optimization and developed an adaptive PSO. The improved method takes advantage of the swarm-diversity to control the tuning of the inertia weight (PSO-DCIW), which in turn can adjust the swarm-diversity adaptively and contribute to a successful global search. The proposed PSO-DCIW was applied to some well-known benchmarks and compared with the other notable improved methods for PSO. The relative experimental results show PSO-DCIW is a robust global optimization method for the complex multimodal functions, which can improve the performance of the standard PSO and alleviate the premature convergence validly.