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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2000-2006

Coyote optimization algorithm based on dual strategy learning and adaptive chaotic mutation

Zhao Jinjin1
Lu Haiyan1,2
Xu Jie1
Lu Mengdie1
Hou Xinyu1
1. School of Science, Jiangnan University, Wuxi Jiangsu 214122, China
2. Wuxi Engineering Technology Research Center for Biological Computing, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2021.12.0677

Abstract

Aiming at the shortcomings of COA, such as slow convergence speed, low solution accuracy and being easy to fall into local optimum, this paper proposed an improved coyote optimization algorithm based on dual strategy learning mechanism and adaptive chaotic mutation strategy(DCSCOA). Firstly, it adopted an oscillatory decline factor to generate diverse individuals for enhancing the global search ability. Secondly, it proposed a dual strategy learning mechanism to appropriately increase the influence of the group head wolf, so as to balance the local search ability and global search ability of the algorithm, and to improve the solution accuracy and convergence speed of the algorithm. Finally, it used an adaptive chaotic mutation mechanism to generate new individuals when the algorithm stagnates, so as to make the algorithm jump out of the local optimum. Through simulation experiments on 20 basic test functions and 11 CEC2017 test functions, the results show that the improved algorithm has higher solution accuracy, faster convergence speed and stronger stability.

Foundation Support

国家自然科学基金资助项目(61772013,61402201)
江苏省青年基金资助项目(BK20190578)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.12.0677
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Algorithm Research & Explore
Pages: 2000-2006
Serial Number: 1001-3695(2022)07-012-2000-07

Publish History

[2022-03-08] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

赵金金, 鲁海燕, 徐杰, 等. 双策略学习和自适应混沌变异的郊狼优化算法 [J]. 计算机应用研究, 2022, 39 (7): 2000-2006. (Zhao Jinjin, Lu Haiyan, Xu Jie, et al. Coyote optimization algorithm based on dual strategy learning and adaptive chaotic mutation [J]. Application Research of Computers, 2022, 39 (7): 2000-2006. )

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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