Whale optimisation algorithm with role labour division for multimodal optimisation

被引：0
|
作者：
Wu, Bowen [1 ,2 ]
Xiao, Renbin [1 ,3 ]
机构：
[1] School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Hubei, Wuhan,430074, China
[2] Institute of Artificial Intelligence, Huazhong University of Science and Technology, Hubei, Wuhan,430074, China
[3] Key Laboratory of Image Processing and Intelligent Control, Ministry of Education of China, Huazhong University of Science and Technology, Hubei, Wuhan,430074, China
来源：
International Journal of Innovative Computing and Applications | 2024年 / 15卷 / 01期
关键词：
The multimodal optimisation problem challenges the balance between diversity and convergence; which poses a great degree of challenge to traditional population-based intelligence optimisers. The whale optimisation algorithm has the limitation of easily falling into local optimum when facing multimodal optimisation. To address this problem; this paper proposes a whale optimisation algorithm with role labour division (WOA-RDL). In WOA-RDL; a multi-stage role division of labour mechanism is designed to divide the optimisation process into two stages of group hunting. In the first stage; the task allocation in the hunting process is considered as two types of search tasks: exploration and exploitation. The external environmental stimulus of the task and the internal response threshold of the individual were designed to allow individuals to switch between exploration and exploitation tasks flexibly. In the second stage; the distribution mechanism of different roles was designed to simulate the 'strong to weak' distribution rule for the distribution of results after the hunt. The elite roles are selected to further strengthen their capabilities or remain robust; while the inferior roles are predicted and eliminated to improve the overall optimisation of the population and reduce the risk of falling into a local optimum. The results of the optimisation experiments using multimodal; unimodal; CEC2017 combined functions and CEC2009 dynamic functions to validate the effectiveness of the algorithm. Copyright © 2024 Inderscience Enterprises Ltd;
D O I：
10.1504/IJICA.2024.143405
中图分类号：
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页码：26 / 37
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