标题:An improved bat algorithm based on hybrid with ant lion optimizer
作者:Dao, Thi-Kien ;Chu, Shu-Chuan ;Pan, Jeng-Shyang ;Nguyen, Trong-The ;Ngo, Truong-Giang ;Nguyen, Trinh-Dong ;Tran, Huu-Trung
通讯作者:Nguyen, TrongThe
作者机构:[Dao, Thi-Kien ;Pan, Jeng-Shyang ;Nguyen, Trong-The ] Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, 更多
会议名称:13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019
会议日期:1 November 2019 through 3 November 2019
来源:Advances in Intelligent Systems and Computing
出版年:2020
卷:1107 AISC
页码:50-60
DOI:10.1007/978-981-15-3308-2_6
关键词:Ant Lion Optimizer; Bat algorithm; Improved Bat algorithm; Optimization
摘要:Bat Algorithm (BA) is one of the fundamental algorithms for solving optimization problems. However, the BA still exists weaknesses in terms of exploitation and exploration. In this paper, an enhancing capability of exploration and exploitation for BA by hybridizing BA with Ant Lion Optimizer (ALO) is proposed for the global optimization problems. In the experimental section, several benchmark functions are used to test the performance of the proposed approach. Compared results with other algorithms literature show that the proposed method provides a new competitive algorithm. © Springer Nature Singapore Pte Ltd. 2020.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081990103&doi=10.1007%2f978-981-15-3308-2_6&partnerID=40&md5=b3b148d245c15656d4111665662482bf
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