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Grey wolf optimizer based on Aquila exploration method
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AI
DOI：10.1016/j.eswa.2022.117629.png)
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Abstract
En 中文
The grey wolf optimizer(GWO) is an effective meta-heuristic algorithm. However, since the update of the search agent's position often depends on the alpha wolf, it is easy to fall into a local optimal solution. Therefore, this paper proposes an improved GWO algorithm to solve the global optimization. The improved algorithm is inspired by the Aquila Optimizer(AO), which enables some wolves to have the ability to fly, expand their search range to improve the global search ability and reduce the possibility of falling into the local optimum. At the same time, a new reduction strategy is proposed, which mainly emphasizes the exploitation ability of grey wolf and the exploration ability of Aquila, which is used to balance the two stages of exploration and exploitation. In order to verify the effectiveness of the algorithm, the algorithm was benchmarked on 23 well-known test functions and compared with other popular meta-heuristic algorithms. The results show that our proposed algorithm has good performance. Finally, it is applied to four practical engineering problems, and the results show that the algorithm is suitable for challenging problems with unknown search space. Matlab codes of AO are available at http s://ww2.mathworks.cn/matlabcentral/fileexchange/110410-grey-wolf-optimizer-based-on-aquila-explorati on-method.
Keywords:
Optimization
Grey wolf optimizer
Meta-heuristics
Swarm intelligence
Aquila Optimizer
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W