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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2712-2721

Snake optimization algorithm combining elite initialisation and K-nearest neighbors

Wang Lijuan1
Liu Shuhan1
Wang Jian2
Tian Yaqi1
1. School of Electrical Engineering, North China University of Water Resources & Electric Power, Zhengzhou 450045, China
2. School of Artificial Intelligence & Automation, Huazhong University of Science & Technology, Wuhan 430074, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0019

Abstract

The survival behavior of snakes in nature generates the snake optimization(SO), a meta-heuristic optimization algorithm. But the original snake optimization algorithm suffers from slow convergence and easy to fall into the local optimum, so this paper proposed an improved snake optimization algorithm that combined elite initialization and K-nearest neighbors improved snake optimizer(EKISO). Firstly, in order to improve the quality of the initial population, it proposed elite initialization during the population initialization stage, which generated high-quality initial population individuals based on elite individuals of the population. Secondly, optimizing the spiral foraging strategy with a shock factor expanded the search range during the global exploration stage and enhanced the algorithms local escape capability. Finally, in the local exploitation stage, it proposed a position update method based on the K-nearest neighbor concept, enhancing the information interaction capability among population individuals, thus accelerating convergence speed and improving convergence accuracy. This method compared with seven other optimization algorithms using 14 classical test functions and 4 CEC2017 test functions, which proves that EKISO converges faster, has higher accuracy and is not easy to fall into local optimum. To further validate the practicality and feasibility of EKISO, this paper applied it to pressure vessel design problems. Experimental comparative analysis reveals that EKISO possesses certain advantages in dealing with real optimization problems.

Foundation Support

国家自然科学基金面上项目(72071084)
河南省教育厅高等学校重点科研项目(22A120008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0019
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Algorithm Research & Explore
Pages: 2712-2721
Serial Number: 1001-3695(2024)09-021-2712-10

Publish History

[2024-05-13] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

王丽娟, 刘姝含, 王剑, 等. 结合精英初始化和K近邻的蛇优化算法 [J]. 计算机应用研究, 2024, 41 (9): 2712-2721. (Wang Lijuan, Liu Shuhan, Wang Jian, et al. Snake optimization algorithm combining elite initialisation and K-nearest neighbors [J]. Application Research of Computers, 2024, 41 (9): 2712-2721. )

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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