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Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

Jiarui Zhou, Junshan Yang, Ling Lin, Zexuan Zhu, Zhen Ji
Copyright: © 2018 |Pages: 26
ISBN13: 9781522551348|ISBN10: 1522551344|EISBN13: 9781522551355
DOI: 10.4018/978-1-5225-5134-8.ch002
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MLA

Zhou, Jiarui, et al. "Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy." Critical Developments and Applications of Swarm Intelligence, edited by Yuhui Shi, IGI Global, 2018, pp. 27-52. https://doi.org/10.4018/978-1-5225-5134-8.ch002

APA

Zhou, J., Yang, J., Lin, L., Zhu, Z., & Ji, Z. (2018). Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy. In Y. Shi (Ed.), Critical Developments and Applications of Swarm Intelligence (pp. 27-52). IGI Global. https://doi.org/10.4018/978-1-5225-5134-8.ch002

Chicago

Zhou, Jiarui, et al. "Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy." In Critical Developments and Applications of Swarm Intelligence, edited by Yuhui Shi, 27-52. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5134-8.ch002

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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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