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Improved team learning-based grey wolf optimizer for optimization tasks and engineering problems

文献类型：期刊论文

作者J. K. Cui; T. Y. Liu; M. C. Zhu and Z. B. Xu
刊名Journal of Supercomputing
出版日期2022
页码51
ISSN号0920-8542
DOI10.1007/s11227-022-04930-5
英文摘要Optimization refers to finding the optimal solution to minimize or maximize the objective function. In the field of engineering, this plays an important role in designing parameters and reducing manufacturing costs. Meta-heuristics such as the grey wolf optimizer (GWO) are efficient ways to solve optimization problems. However, the GWO suffers from premature convergence or low accuracy. In this study, a team learning-based grey wolf optimizer (TLGWO), which consists of two strategies, is proposed to overcome these shortcomings. The neighbor learning strategy introduces the influence of neighbors to improve the local search ability, whereas the random learning strategy provides new search directions to enhance global exploration. Four engineering problems with constraints and 21 benchmark functions were employed to verify the competitiveness of the TLGWO. The test results were compared with three derivatives of the GWO and nine other state-of-the-art algorithms. Furthermore, the experimental results were analyzed using the Friedman and mean absolute error statistical tests. The results show that the proposed TLGWO can provide superior solutions to the compared algorithms on most optimization tasks and solve engineering problems with constraints.
URL标识查看原文
语种英语
源URL[http://ir.ciomp.ac.cn/handle/181722/66750]  
专题中国科学院长春光学精密机械与物理研究所
推荐引用方式
GB/T 7714		 J. K. Cui,T. Y. Liu,M. C. Zhu and Z. B. Xu. Improved team learning-based grey wolf optimizer for optimization tasks and engineering problems[J]. Journal of Supercomputing,2022:51.
APA J. K. Cui,T. Y. Liu,&M. C. Zhu and Z. B. Xu.(2022).Improved team learning-based grey wolf optimizer for optimization tasks and engineering problems.Journal of Supercomputing,51.
MLA J. K. Cui,et al."Improved team learning-based grey wolf optimizer for optimization tasks and engineering problems".Journal of Supercomputing (2022):51.

入库方式： OAI收割

来源：长春光学精密机械与物理研究所

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