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

doi:10.1007/s11227-022-04930-5

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	Improved team learning-based grey wolf optimizer for optimization tasks and engineering problems
	J. K. Cui; T. Y. Liu; M. C. Zhu and Z. B. Xu
	2022
发表期刊	Journal of Supercomputing
ISSN	0920-8542
页码	51
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.
DOI	10.1007/s11227-022-04930-5
URL	查看原文
收录类别	sci ; ei
语种	英语
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	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.

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