App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

Multi-objective wolf pack algorithm with elite guidance and information interaction

Chen Fujun1a,1b
Wu Runxiu1a,1b
Xiao Renbin2
Wang Hui1a,1b
Zhao Jia1a,1b
1. a. School of Information Engineering, b. Nanchang Key Laboratory of IoT Perception & Collaborative Computing for Smart City, Nanchang Institute of Technology, Nanchang 330099, China
2. School of Artificial Intelligence & Automation, Huazhong University of Science & Technology, Wuhan 430074, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0587

Abstract

In consideration of the superior performance of the wolf pack algorithm on single-objective optimization problems, combining with the biological habits of wolves to apply it to multi-objective optimization problems, this paper proposed a multi-objective wolf pack algorithm with elite guidance and information interaction (MOWPA-EGII) . Firstly, MOWPA-EGII proposed an elite guidance strategy, using the elite wolf in the external file and the head wolf of the current sub-population to jointly guide the population movement, so that the artificial wolves uniformly distributes in the whole search space, and the strategy enhances the global search ability of the algorithm; Secondly, it designed the information interaction mechanism to simulate the information transfer in the wolf hunting process, so that individuals with different advantages can transfer information to each other to ensure the hunting efficiency of the wolves and improve the ability of the algorithm to explore the optimal solution of the Pareto; Finally, it added the mutation operator to perturb the moving direction of the artificial wolves, so as to let the algorithm jump out of the local optimum and enhance the local search ability of the algorithm. In order to verify the effectiveness of MOWPA-EGII, comparing it with 5 classical algorithms and 10 recent algorithms, and the results show that MOWPA-EGII possesses good convergence and diversity, which proves that the present algorithm has a better optimization performance.

Foundation Support

国家自然科学基金资助项目(52069014)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0587
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-04-08] Accepted Paper

Cite This Article

陈福军, 吴润秀, 肖人彬, 等. 精英引导和信息交互的多目标狼群算法 [J]. 计算机应用研究, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0587. (Chen Fujun, Wu Runxiu, Xiao Renbin, et al. Multi-objective wolf pack algorithm with elite guidance and information interaction [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0587. )

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  • Application Research of Computers Monthly Journal
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    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.

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