An Improved Bat Algorithm Based on Hybrid with Ant Lion Optimizer

Thi-Kien Dao, Shu-Chuan Chu, Jeng-Shyang Pan, Trong-The Nguyen, Truong-Giang Ngo, Trinh-Dong Nguyen, Huu-Trung Tran

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computing
Subtitle of host publication Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019
EditorsJeng-Shyang Pan, Jerry Chun-Wei Lin, Yongquan Liang, Shu-Chuan Chu
Place of PublicationSingapore
Number of pages11
ISBN (Electronic)9789811533082
ISBN (Print)9789811533075
Publication statusPublished - 1 Jan 2020
Externally publishedYes

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1107 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


  • Ant Lion Optimizer
  • Bat algorithm
  • Improved Bat algorithm
  • Optimization


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