Ensemble grey wolf Optimizer and its application for image segmentation

被引：32

作者：

Yu, Xiaobing ^{[1
]}

Wu, Xuejing ^{[1
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Res Ctr Risk Management & Emergency Decis Making, Nanjing 210044, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2022年 / 209卷

关键词：

Grey wolf optimizer; Ensemble; Exploitation; Image segmentation; ALGORITHM;

D O I：

10.1016/j.eswa.2022.118267

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Wolf Optimizer (GWO) is a novel optimization algorithm. It mimics the hierarchy of wolves and has shown potential optimization ability. However, it may suffer from poor performance as it is based on a single optimization strategy. An Ensemble GWO (EGWO) is developed using two strategies to boost the performance of GWO. The positions of the top three wolves are updated according to the difference with the alpha. The rest wolves adjust their positions by extending the position-updated equation to improve the exploitation. Then, an elitebased random search is introduced to advance the exploration. The proposed EGWO algorithm is validated on the CEC 2019 and image segmentation in the real world. The obtained results show that the proposed EGWO algorithm is reliable and effective for benchmark functions and real-world applications.

引用

页数：17

共 31 条

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