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Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

Dongping Tian1,*, Bingchun Li1, Jing Liu1, Chen Liu1, Ling Yuan1, Zhongzhi Shi2

1 School of Computer Science and Technology, Kashi University, Kashi, 844006, China
2 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

* Corresponding Author: Dongping Tian. Email: email

Intelligent Automation & Soft Computing 2023, 37(3), 2783-2807. https://doi.org/10.32604/iasc.2023.039531

Abstract

Particle swarm optimization (PSO) is a stochastic computation technique that has become an increasingly important branch of swarm intelligence optimization. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal problems. Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization (abbreviated as AMS-PSO). To start with, the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the AMS-PSO. Subsequently, according to the current iteration, different update schemes are used to regulate the particle search process at different evolution stages. To be specific, two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution stage. Followed by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm diversity. In addition, an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO method. Finally, extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

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APA Style
Tian, D., Li, B., Liu, J., Liu, C., Yuan, L. et al. (2023). Adaptive multi-updating strategy based particle swarm optimization. Intelligent Automation & Soft Computing, 37(3), 2783-2807. https://doi.org/10.32604/iasc.2023.039531
Vancouver Style
Tian D, Li B, Liu J, Liu C, Yuan L, Shi Z. Adaptive multi-updating strategy based particle swarm optimization. Intell Automat Soft Comput . 2023;37(3):2783-2807 https://doi.org/10.32604/iasc.2023.039531
IEEE Style
D. Tian, B. Li, J. Liu, C. Liu, L. Yuan, and Z. Shi "Adaptive Multi-Updating Strategy Based Particle Swarm Optimization," Intell. Automat. Soft Comput. , vol. 37, no. 3, pp. 2783-2807. 2023. https://doi.org/10.32604/iasc.2023.039531



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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