Shams K. Nseef
An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems
Nseef, Shams K.; Abdullah, Salwani; Turky, Ayad; Kendall, Graham
Authors
Salwani Abdullah
Ayad Turky
Graham Kendall
Abstract
Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results.
Citation
Nseef, S. K., Abdullah, S., Turky, A., & Kendall, G. (2016). An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. Knowledge-Based Systems, 104, https://doi.org/10.1016/j.knosys.2016.04.005
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 3, 2016 |
Online Publication Date | Apr 8, 2016 |
Publication Date | Jul 15, 2016 |
Deposit Date | Feb 5, 2018 |
Publicly Available Date | Feb 5, 2018 |
Journal | Knowledge-Based Systems |
Print ISSN | 0950-7051 |
Electronic ISSN | 1872-7409 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 104 |
DOI | https://doi.org/10.1016/j.knosys.2016.04.005 |
Keywords | Dynamic optimisation ; Artificial bee colony algorithm ; Adaptive multi-population method ; Meta-heuristics |
Public URL | https://nottingham-repository.worktribe.com/output/800861 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S0950705116300363 |
Contract Date | Feb 5, 2018 |
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Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
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