Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1124-1131

Multi-strategy coyote optimization algorithm

Zhang Xinming1,2
Yang Fangyuan1
Liu Guoqi1,2
1. College of Computer & Information Engineering, Henan Normal University, Xinxiang Henan 453007, China
2. Engineering Laboratory of Intelligence Business & Internet of Things of Henan Province, Xinxiang Henan 453007, China
DOI: 10.19734/j.issn.1001-3695.2021.08.0338

Abstract

Coyote optimization algorithm(COA) is a swarm intelligent algorithm proposed recently, which has a unique search structure and good optimization performance. In order to improve COA further, this paper proposed a multi-strategy COA(MSCOA). Firstly, the best coyote in a group used a global best coyote-guided growth strategy to improve its social adaptation, the worst coyote in the group used the best coyote guidance reinforcement strategy to strengthen the worst coyote's search ability. Secondly, the other coyotes in the group adopted a group growth strategy that dynamically adjusted information interchange to improve information sharing degree among the coyotes in the group, and MSCOA crossed this growth strategy with an improved migration strategy to further improve search ability. Finally, MSCOA adopted a dynamic grouping strategy to reduce the manual setting of parameters and improve operability. Hybridizing the above strategies can keep better balance exploration and exploitation to maximize the performance. A large number of experimental results on complex functions from the CEC2014 test set show that MSCOA has stronger search ability, faster running speed, and higher search efficiency than COA, and has more obvious advantages over quite a few other excellent algorithms.

Foundation Support

国家自然科学基金项目(61901160)
河南省高等学校重点科研项目(19A520026)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0338
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: Algorithm Research & Explore
Pages: 1124-1131
Serial Number: 1001-3695(2022)04-028-1124-08

Publish History

[2021-11-12] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

张新明, 杨方圆, 刘国奇. 多策略的郊狼优化算法 [J]. 计算机应用研究, 2022, 39 (4): 1124-1131. (Zhang Xinming, Yang Fangyuan, Liu Guoqi. Multi-strategy coyote optimization algorithm [J]. Application Research of Computers, 2022, 39 (4): 1124-1131. )

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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