Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 11
3358-3364

Improved particle swarm optimization algorithm with multi-strategy fusion

Wu Dafei
Yang Guangyong
Fan Kangsheng
Xu Tianqi
School of Electrical & Information Technology, Yunnan Minzu University, Kunming 650500, China
DOI: 10.19734/j.issn.1001-3695.2022.04.0167

Abstract

To solve the problems of low convergence accuracy, slow convergence speed and easy to fall into local optimum of traditional particle swarm algorithm, this paper proposed an improved particle swarm algorithm with multi-strategy fusion. Firstly, in order to accelerate the convergence speed of free particles, the improved algorithm used a method of updating the position of free particles based on the midperpendicular algorithm. Secondly, the improved algorithm designed a strategy of generating exploding particles near the optimal particles to enhance the optimization-seeking accuracy and optimization-seeking speed of the algorithm, and the improved algorithm also designed a particle velocity updating strategy relying only on the global optimal particle position to accommodate the first two strategies. Finally, the algorithm also used the inertia weights and particle position update methods of the simplified particle swarm optimization algorithm based on probabilistic hierarchy. This paper designed a few comparison experiments with other five improved particle swarm algorithms, and the results show that the improved algorithm has a greater advantage and better performance whether dealing with low-dimensional problems or high-dimensional problems.

Foundation Support

国家自然科学基金资助项目(61761049,61261022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.04.0167
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 11
Section: Algorithm Research & Explore
Pages: 3358-3364
Serial Number: 1001-3695(2022)11-025-3358-07

Publish History

[2022-06-20] Accepted Paper
[2022-11-05] Printed Article

Cite This Article

吴大飞, 杨光永, 樊康生, 等. 多策略融合的改进粒子群优化算法 [J]. 计算机应用研究, 2022, 39 (11): 3358-3364. (Wu Dafei, Yang Guangyong, Fan Kangsheng, et al. Improved particle swarm optimization algorithm with multi-strategy fusion [J]. Application Research of Computers, 2022, 39 (11): 3358-3364. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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