EconPapers    
Economics at your fingertips  
 

A New Hybrid BA_ABC Algorithm for Global Optimization Problems

Gülnur Yildizdan and Ömer Kaan Baykan
Additional contact information
Gülnur Yildizdan: Kulu Vocational School, Selcuk University, Kulu, 42770 Konya, Turkey
Ömer Kaan Baykan: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, 42250 Konya, Turkey

Mathematics, 2020, vol. 8, issue 10, 1-36

Abstract: Bat Algorithm (BA) and Artificial Bee Colony Algorithm (ABC) are frequently used in solving global optimization problems. Many new algorithms in the literature are obtained by modifying these algorithms for both constrained and unconstrained optimization problems or using them in a hybrid manner with different algorithms. Although successful algorithms have been proposed, BA’s performance declines in complex and large-scale problems are still an ongoing problem. The inadequate global search capability of the BA resulting from its algorithm structure is the major cause of this problem. In this study, firstly, inertia weight was added to the speed formula to improve the search capability of the BA. Then, a new algorithm that operates in a hybrid manner with the ABC algorithm, whose diversity and global search capability is stronger than the BA, was proposed. The performance of the proposed algorithm (BA_ABC) was examined in four different test groups, including classic benchmark functions, CEC2005 small-scale test functions, CEC2010 large-scale test functions, and classical engineering design problems. The BA_ABC results were compared with different algorithms in the literature and current versions of the BA for each test group. The results were interpreted with the help of statistical tests. Furthermore, the contribution of BA and ABC algorithms, which constitute the hybrid algorithm, to the solutions is examined. The proposed algorithm has been found to produce successful and acceptable results.

Keywords: artificial bee colony algorithm; bat algorithm; continuous optimization; heuristic algorithms; large-scale optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/10/1749/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/10/1749/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:10:p:1749-:d:426491

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1749-:d:426491