An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Nseef, Shams K. and Abdullah, Salwani and Turky, Ayad and Kendall, Graham (2016) An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. Knowledge-Based Systems, 104 . pp. 14-23. ISSN 0950-7051

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/800861
Keywords: Dynamic optimisation ; Artificial bee colony algorithm ; Adaptive multi-population method ; Meta-heuristics
Schools/Departments: University of Nottingham, Malaysia > Faculty of Science > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.knosys.2016.04.005
Depositing User: Kendall, Graham
Date Deposited: 05 Feb 2018 11:10
Last Modified: 04 May 2020 18:01
URI: http://eprints.nottingham.ac.uk/id/eprint/49534

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