App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 10

Artificial bee colony algorithm with unlinear population size reduction based on cluster individual rank

Zhao Ming
Liu Shanzhi
Song Xiaoyu
Shen Xiaopeng
School of Computer Science & Engineering, Shenyang Jianzhu University, Shenyang 110168, China
DOI: 10.19734/j.issn.1001-3695.2024.02.0045

Abstract

Aiming at the problem that Artificial Bee Colony algorithm (ABC) has strong exploration but weak exploitation, which leads to slow convergence speed, this paper proposed an Unlinear Population Size Reduction strategy based on Cluster Individual Rank (UPSR-CIR) . Firstly, the strategy designed the long-tail unlinear population size reduction function which maintains a large population to explore fully in the early stage, and reduces the population size rapidly in the middle stage, so as to maintain a small population to strengthen exploitation in the late stage, while allocating relatively more computing resources for the late stage to accelerate convergence. Secondly, to ensure the diversity of the population, used K-means clustering dynamically to divide the population into clusters every a certain number of generations, and carried out the population size reduction in the unit of cluster; At the same time, when the population size reducing in the unit of cluster, determined the number of individuals deleted according to the rank of the best individual in the cluster, so as to reserve relatively more computing resources for the potential cluster with higher rank to further strengthen exploitation. This paper used 22 benchmark test functions to compare and analyze the UPSR-CIR strategy on ABC and its variants, and the results show that the UPSR-CIR strategy exhibits higher solution accuracy, stability and convergence speed. It is also universally applicable to ABC variants. Finally, used 12 classical Traveling Salesman Problem (TSP) cases to validate the practicality and superiority of the UPSR-CIR strategy on real application problem.

Foundation Support

国家自然科学基金资助项目(62073227)
辽宁省教育厅科研项目(LJKMZ20220916)
辽宁省科技厅科研项目(2023-MS-222)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.02.0045
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 10

Publish History

[2024-07-05] Accepted Paper

Cite This Article

赵明, 刘善智, 宋晓宇, 等. 基于分区个体排名的非线性种群缩减的人工蜂群算法 [J]. 计算机应用研究, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2024.02.0045. (Zhao Ming, Liu Shanzhi, Song Xiaoyu, et al. Artificial bee colony algorithm with unlinear population size reduction based on cluster individual rank [J]. Application Research of Computers, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2024.02.0045. )

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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