Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 7
2003-2012

Differential evolution algorithm based on random neighborhood mutation and optimal opposition-based learning

Zuo Wenlua,b
Gao Yuelina,b
a. School of Mathematics & Information Sciences, b. Ningxia Key Laboratory of Intelligent Information & Big Data Processing, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2022.11.0785

Abstract

The traditional differential evolution(DE) algorithm balanced global exploration and local exploitation inadequately, and had problems with easily falling into local optimal solutions, low solution accuracy and slow convergence speed. Therefore, this paper proposed a differential evolution algorithm based on random neighborhood mutation and optimal opposition-based learning(RNODE) and analyzed for its complexity. Firstly, the algorithm generated a random neighborhood for each individual in the current population, and used the global best individual to guide the neighborhood best individual to generate a composite basis vector, combined with an adaptive update mechanism of the control parameters to constitute a random neighborhood mutation strategy, which enabled the algorithm maintained its exploration ability and guided the population towards the optimal direction. Secondly, to further help the algorithm jump out of the local optimum, the algorithm performed the optimal opposition-based learning strategy on the poorer individuals to expand the search area. Finally, this paper compared RNODE with 9 algorithms to verify the effectiveness and advancement of RNODE. The experimental results on 23 benchmark functions and 2 real-world engineering optimization problems show that the RNODE algorithm has a higher convergence accuracy, faster speed and a greater stability.

Foundation Support

宁夏自然科学基金重点资助项目(2022AAC02043)
宁夏高等教育一流学科建设基金资助项目(NXYLXK2017B09)
北方民族大学重大科研专项资助项目(ZDZX201901)
南京证券支持基础学科研究项目(NJZQJCXK202201)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.11.0785
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 7
Section: Algorithm Research & Explore
Pages: 2003-2012
Serial Number: 1001-3695(2023)07-013-2003-10

Publish History

[2023-02-27] Accepted Paper
[2023-07-05] Printed Article

Cite This Article

左汶鹭, 高岳林. 基于随机邻域变异和趋优反向学习的差分进化算法 [J]. 计算机应用研究, 2023, 40 (7): 2003-2012. (Zuo Wenlu, Gao Yuelin. Differential evolution algorithm based on random neighborhood mutation and optimal opposition-based learning [J]. Application Research of Computers, 2023, 40 (7): 2003-2012. )

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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