Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 4
1015-1019

Dynamically adaptive cuckoo search algorithm based on dimension by opposition-based learning

Huang Minming1a,1b
He Qing1a,1b
Wen Xi2
1. a. College of Big Data & Information Engineering, b. Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
2. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300000, China
DOI: 10.19734/j.issn.1001-3695.2018.10.0725

Abstract

There are still some shortcomings in cuckoo search algorithm, such as low convergence precision, slow convergence speed, weak search vitality and interference phenomena among dimensions when dealing with high-dimensional optimization problems. This paper proposed dynamically adaptive cuckoo search algorithm based on dimension by opposition-based learning. Firstly, the selected solution updated for dimension-by-dimension by opposition-based learning, which could reduce interdimensional interference and expand population diversity. Then it used the method of elite retention to evaluate the result and improve the search ability of the algorithm. Finally, it fully utilized the information of the current solution to dynamically adaptive the scaling factor control to guide the solution to converge quickly and enhance the search vitality of the algorithm. The experimental results show that compared with the standard cuckoo search algorithm, the proposed algorithm has improved convergence precision, convergence speed and search vitality. Compared with other improved algorithms, it has certain competitive advantage.

Foundation Support

贵州省科技计划项目重大专项项目(黔科合重大专项字［2018］3002
黔科合重大专项字［2016］3022)
贵州省公共大数据重点实验室开放课题(2017BDKFJJ004)
贵州省教育厅青年科技人才成长项目(黔科合KY字［2016］124)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.10.0725
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 4
Section: Algorithm Research & Explore
Pages: 1015-1019
Serial Number: 1001-3695(2020)04-012-1015-05

Publish History

[2020-04-05] Printed Article

Cite This Article

黄闽茗, 何庆, 文熙. 基于逐维反向学习的动态适应布谷鸟算法 [J]. 计算机应用研究, 2020, 37 (4): 1015-1019. (Huang Minming, He Qing, Wen Xi. Dynamically adaptive cuckoo search algorithm based on dimension by opposition-based learning [J]. Application Research of Computers, 2020, 37 (4): 1015-1019. )

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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