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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 11
3254-3261,3268

Particle swarm optimization algorithm with dual population cross-learning

Li Wei
Ding Shuhui
Chen Xunjun
School of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0133

Abstract

Particle swarm optimization(PSO) algorithm is widely used because of its few dominant parameters, fast convergence speed and easy implementation. However, the algorithm has low precision and is easy to fall into the problem of local optimization. This paper proposed a particle swarm optimization algorithm with dual population cross-learning(DPCPSO). In this algorithm, it divided the whole population into ordinary sub-population and elite sub-population. The ordinary sub-population adopted a comprehensive mutation mechanism, which made the ordinary sub-population randomly choose the direction of the excellent particles or maintain its own direction to mutate by setting the probability parameter, so as to focus on finding the possible solution area. The elite sub-population adopted the cross-learning mechanism to cross-generate the historical optimal and global optimal individuals of the particles, so as to guide the particles to locally search the possible solution area. To balance the global exploration and local exploitation capabilities of particles, this paper proposed a nonlinear inertia weight. To verify the effectiveness of the algorithm, the proposed algorithm was tested on 16 benchmark problems and compared with other seven variants of particle swarm optimization algorithm. The experimental results show that the proposed algorithm ranks the first in solving accuracy and convergence speed, and it verifies that the algorithm performance is better than other variants of particle swarm optimization algorithm in solving.

Foundation Support

国家自然科学基金资助项目(62066019)
江西省自然科学基金面上项目(20202BABL202020)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0133
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 11
Section: Algorithm Research & Explore
Pages: 3254-3261,3268
Serial Number: 1001-3695(2023)11-008-3254-08

Publish History

[2023-06-07] Accepted Paper
[2023-11-05] Printed Article

Cite This Article

李伟, 丁书慧, 陈勋俊. 基于双种群交叉学习的粒子群优化算法 [J]. 计算机应用研究, 2023, 40 (11): 3254-3261,3268. (Li Wei, Ding Shuhui, Chen Xunjun. Particle swarm optimization algorithm with dual population cross-learning [J]. Application Research of Computers, 2023, 40 (11): 3254-3261,3268. )

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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