Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 9
2701-2708,2751

Self-adaptive Gaussian keyhole imaging butterfly optimization algorithm based on Latin hypercube sampling

Xu Jiea
Lu Haiyana,b
Zhao Jinjina
Hou Xinyua
Lu Mengdiea
a. School of Science, b. Wuxi Engineering Technology Research Center for Biological Computing, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2022.02.0054

Abstract

Aiming at the shortcomings of Butterfly optimization algorithm, such as poor population diversity, low optimization accuracy and slow convergence speed, this paper proposed a self-adaptive Gaussian keyhole imaging butterfly optimization algorithm based on Latin hypercube sampling. Firstly, it used a Latin hypercube sampling population initialization strategy to enhance the population diversity and thereby improve the overall search ability of the algorithm. Then, it introduced the selfadaptive optimal guidance strategy, which could dynamically adjust the search range in different evolutionary periods, to balance the global and local search capabilities and hence improve the optimization accuracy of the algorithm. Finally, it adopted a Gaussian keyhole imaging strategy to disturb the optimal individuals, making the individuals of the population moving close to the optimal individuals, so as to further improve the solution accuracy and speed up the convergence of the algorithm. Through simulation experiments and Wilcoxon rank sum tests using 14 benchmark functions, the results show that the performance of the improved algorithm is greatly enhanced in terms of optimization accuracy, convergence speed, stability and scalability.

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.1001-3695.2022.02.0054
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 9
Section: Algorithm Research & Explore
Pages: 2701-2708,2751
Serial Number: 1001-3695(2022)09-022-2701-08

Publish History

[2022-04-26] Accepted Paper
[2022-09-05] Printed Article

Cite This Article

徐杰, 鲁海燕, 赵金金, 等. 拉丁超立方抽样的自适应高斯小孔成像蝴蝶优化算法 [J]. 计算机应用研究, 2022, 39 (9): 2701-2708,2751. (Xu Jie, Lu Haiyan, Zhao Jinjin, et al. Self-adaptive Gaussian keyhole imaging butterfly optimization algorithm based on Latin hypercube sampling [J]. Application Research of Computers, 2022, 39 (9): 2701-2708,2751. )

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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