Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 2
418-423

Double strategies co-evolutionary quantum-behaved particle swarm optimization algorithm and its application

He Guanga,b
Lu Xiaolic
Li Gaoxia,b
a. Chongqing Key Laboratory of Social Economic & Applied Statistics, b. School of Mathematics & Statistics, c. Research Center for Economy of Upper Reaches of the Yangtze River, Chongqing Technology & Business University, Chongqing 400067, China
DOI: 10.19734/j.issn.1001-3695.2022.07.0353

Abstract

To improve the local mining and global search ability of quantum-behaved particle swarm optimization algorithm(QPSO), this paper proposed an improved QPSO algorithm(DSQPSO). DSQPSO algorithm introduced the double strategies co-evolution to adjust the particle position update formula. Firstly, in order to fully reflect the advantage of individual exploration and the characteristic of collective guidance, this paper put forward two kinds of ideas of attraction points to achieve better integration of individuals and the swarm as well as information exchange. Secondly, it redefined the search scope of the particle through considering the relationship between the optimal average position and the global optimum and individual's historical optimum respectively. Moreover, in the iterative process, DSQPSO used the random perturbation mechanism to adjust the global optimal position in order to help the diversity of the swarm to be preserved. Based on 18 test functions, this paper compared DSQPSO with PSO, QPSO, RQPSO and LQPSO in convergence accuracy and robustness. Furthermore, in terms of the optimization results, it compared the improved algorithm with eight intelligent algorithms on two practical engineering optimization problems. Experiments indicate that whether in benchmarking or in engineering application, DSQPSO has obvious advantages in calculation precision and convergence effect.

Foundation Support

国家自然科学基金资助项目(11901068)
重庆市科委资助项目(cstc2016jcyjA0564)
重庆市教委资助项目(KJQN202100815,18SKJD034)
重庆工商大学科研平台开放课题(KFJJ2016008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.07.0353
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Algorithm Research & Explore
Pages: 418-423
Serial Number: 1001-3695(2023)02-017-0418-06

Publish History

[2022-10-10] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

何光, 卢小丽, 李高西. 基于双策略协同进化的QPSO算法及其应用 [J]. 计算机应用研究, 2023, 40 (2): 418-423. (He Guang, Lu Xiaoli, Li Gaoxi. Double strategies co-evolutionary quantum-behaved particle swarm optimization algorithm and its application [J]. Application Research of Computers, 2023, 40 (2): 418-423. )

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.

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