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