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Discrete Dynamics in Nature and Society
Volume 2015, Article ID 674595, 17 pages
Research Article

Artificial Bee Colony Algorithm with Time-Varying Strategy

1Department of Management Science, College of Management, Shenzhen University, Shenzhen 518060, China
2Research Institute of Business Analytics & Supply Chain Management, Shenzhen University, Shenzhen 518060, China
3Division of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China
4International Doctoral Innovation Centre, University of Nottingham Ningbo China, Ningbo 315100, China
5Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China

Received 29 October 2014; Revised 13 March 2015; Accepted 17 March 2015

Academic Editor: Ricardo López-Ruiz

Copyright © 2015 Quande Qin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.