A Comparison of Improved Artificial Bee Colony Algorithms Based on Differential Evolution

Jianfeng Qiu, Jiwen Wang, Dan Yang, Juan Xie


The Artificial Bee Colony (ABC) algorithm is an active field of optimization based on swarm intelligence in recent years. Inspired by the mutation strategies used in Differential Evolution (DE) algorithm, this paper introduced three types strategies (“rand”,” best”, and “current-to-best”) and one or two numbers of disturbance vectors to ABC algorithm. Although individual mutation strategies in DE have been used in ABC algorithm by some researchers in different occasions, there have not a comprehensive application and comparison of the mutation strategies used in ABC algorithm. In this paper, these improved ABC algorithms can be analyzed by a set of testing functions including the rapidity of the convergence. The results show that those improvements based on DE achieve better performance in the whole than basic ABC algorithm.


DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3343


Artificial Bee Colony; Differential Evolution; Search Strategy; Best; Rand; Current-to-Best

Full Text:



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics