Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

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Author(s): Jiarui Zhou (Harbin Institute of Technology, China), Junshan Yang (Shenzhen University, China), Ling Lin (Shenzhen University, China), Zexuan Zhu (Shenzhen University, China)and Zhen Ji (Shenzhen University, China)
Copyright: 2018
Pages: 26
Source title: Critical Developments and Applications of Swarm Intelligence
Source Author(s)/Editor(s): Yuhui Shi (Southern University of Science and Technology, China)
DOI: 10.4018/978-1-5225-5134-8.ch002

Keywords: Artificial Intelligence / Biologically-Inspired Computing / Information Science Reference / Medicine & Healthcare

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Abstract

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. It is easy to get trapped in local optima. For this reason, improvements are made to detect stagnation during the optimization and reactivate the swarm to search towards the global optimum. This chapter imposes the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel crown jewel defense (CJD) strategy is introduced to restart the swarm when it is trapped in a local optimum region. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting. Experimental results suggest that the LCJDPSO-rfl outperforms state-of-the-art PSO variants on most of the functions.

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Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

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