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Artificial bee colony algorithm with time-varying strategy

Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui

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Authors

Quande Qin

Shi Cheng

Qingyu Zhang

LI LI li.li@nottingham.ac.uk
Senior Research Fellow

Yuhui Shi



Abstract

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.

Citation

Qin, Q., Cheng, S., Zhang, Q., Li, L., & Shi, Y. (2015). Artificial bee colony algorithm with time-varying strategy. Discrete Dynamics in Nature and Society, 2015, https://doi.org/10.1155/2015/674595

Journal Article Type Article
Acceptance Date Mar 17, 2015
Publication Date 2015
Deposit Date Oct 12, 2017
Publicly Available Date Oct 12, 2017
Journal Discrete Dynamics in Nature and Society
Print ISSN 1026-0226
Electronic ISSN 1607-887X
Publisher Hindawi
Peer Reviewed Peer Reviewed
Volume 2015
DOI https://doi.org/10.1155/2015/674595
Public URL https://nottingham-repository.worktribe.com/output/747556
Publisher URL https://www.hindawi.com/journals/ddns/2015/674595/
Contract Date Oct 12, 2017

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