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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 12

Adaptive multi-strategy differential evolution algorithm for neighborhood elite collective information and population global information

Song Xiaoyu
Zhu Yanlin
Zhao Ming
Shenyang Jianzhu University, School of Computer Science & Engineering, Shenyang 110168, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0118

Abstract

This paper proposes a multi-strategy differential evolution algorithm (MSDE-NECPG) to better utilize individual neighborhood and global population information in the differential evolution algorithm (DE) . Firstly, we fully utilize the information of multiple elite individuals in the individual neighborhood to guide the mutation strategy, moving the search towards better directions and enhancing the exploitation capability. Secondly, we introduce an update mechanism for the neighborhood to ensure its state evolves continuously throughout the search process. When the optimal individual in the neighborhood fails to update consecutively for multiple generations, the neighborhood may fall into a local optimum. At this point, we expand the neighborhood radius to increase exploration capability. We introduce the mutation strategy "DE/current-to-pbest, " which is based on the global information of the population. These two strategies adaptively adjust based on the improvement rate of individuals, balancing between local and global information. Furthermore, to prevent parameter error interaction, the scaling factor F and crossover rate CR are updated based on successful historical accumulations, employing a grouped parameter adaptive mechanism to continuously adapt to the search process. Finally, to validate its effectiveness, experiments are conducted on 30 benchmark functions from CEC2014, comparing with five state-of-the-art differential evolution algorithms. The experimental results demonstrate that the proposed algorithm's accuracy, stability, and convergence speed are comparable to these five advanced algorithms.

Foundation Support

辽宁省教育厅重点科研项目(lnzd202004)
辽宁省科技厅自然科学基金计划项目(2023-MS-222)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0118
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 12

Publish History

[2024-09-02] Accepted Paper

Cite This Article

宋晓宇, 朱彦霖, 赵明. 邻域精英集体信息和种群全局信息自适应的多策略差分进化算法 [J]. 计算机应用研究, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0118. (Song Xiaoyu, Zhu Yanlin, Zhao Ming. Adaptive multi-strategy differential evolution algorithm for neighborhood elite collective information and population global information [J]. Application Research of Computers, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0118. )

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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