Quande Qin
Artificial bee colony algorithm with time-varying strategy
Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui
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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Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
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