Group search optimizer with interactive dynamic neighborhood

Authors:
Guohua He;Zhihua Cui;Jianchao Zeng
Affiliations:
Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

Citing 3
Cited 1

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Abstract

Group search optimizer(GSO) is a new novel optimization algorithm by simulating animal behaviour. It uses the Gbest topology structure, which leads to rapid exchange of information among particles. So,it is easily trapped into a local optima when dealing with multimodal optimization problems. In this paper,inspiration from the Newman and Watts model,a improved group search optimizer with interactive dynamic neighborhood (IGSO) is proposed.Adopting uniform design and the linear regression method on the parameter selection, four benchmark functions demonstrate the effectiveness of the algorithm.