Algorithm Research & Explore
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394-398,444

Planar-mirror reflection imaging learning based marine predators algorithm and feature selection

Xu Minga
Long Wena,b
Yang Yanga
a. School of Mathematics & Statistics, b. Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang 550025, China

Abstract

The basic marine predators algorithm(MPA) has some drawbacks such as slow convergence speed, low precision, and easy to fall into local optima when solving complex optimization problems. In order to overcome these shortcomings, this paper proposed a planar-mirror reflection imaging learning-based MPA(PRIL-MPA). In the early stage of the iteration, the PRIL-MPA introduced the strategy that the current global optimal individual guides other individuals in the group to search, so as to accelerate the convergence speed of the algorithm. In the later stage of the iteration, the PRIL-MPA introduced the planar mirror reflection imaging strategy to escape from the local optimization and improve the accuracy of the solution. On the one hand, a comparison of PRIL-MPA with four other algorithms on 12 benchmark test functions shows that the proposed algorithm achieves significant improvement in terms of the solution precision and convergence speed. On the other hand, this paper applied PRIL-MPA to solve 21 feature selection problems, which shows that the proposed algorithm can effectively remove redundant features and improve the accuracy of data classification. Compared with other algorithms, the performance of the proposed algorithm is more competitive.

Foundation Support

国家自然科学基金资助项目(61463009)
贵州省自然科学基金资助项目(黔科合基础[2020]1Y012)
贵州省教育厅创新群体重大研究项目(黔教合KY字[2021]015)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.07.0342
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Algorithm Research & Explore
Pages: 394-398,444
Serial Number: 1001-3695(2023)02-013-0394-05

Publish History

[2022-09-22] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

徐明, 龙文, 羊洋. 基于平面镜反射成像学习的海洋捕食者算法及特征选择 [J]. 计算机应用研究, 2023, 40 (2): 394-398,444. (Xu Ming, Long Wen, Yang Yang. Planar-mirror reflection imaging learning based marine predators algorithm and feature selection [J]. Application Research of Computers, 2023, 40 (2): 394-398,444. )

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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