An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems
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 wi...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/49534/ |
| _version_ | 1848798018330951680 |
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| author | Nseef, Shams K. Abdullah, Salwani Turky, Ayad Kendall, Graham |
| author_facet | Nseef, Shams K. Abdullah, Salwani Turky, Ayad Kendall, Graham |
| author_sort | Nseef, Shams K. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:13:06Z |
| format | Article |
| id | nottingham-49534 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:13:06Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-495342020-05-04T18:01:19Z https://eprints.nottingham.ac.uk/49534/ An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems Nseef, Shams K. Abdullah, Salwani Turky, Ayad Kendall, Graham 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. Elsevier 2016-07-15 Article PeerReviewed Nseef, Shams K., Abdullah, Salwani, 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 Dynamic optimisation ; Artificial bee colony algorithm ; Adaptive multi-population method ; Meta-heuristics https://www.sciencedirect.com/science/article/pii/S0950705116300363 doi:10.1016/j.knosys.2016.04.005 doi:10.1016/j.knosys.2016.04.005 |
| spellingShingle | Dynamic optimisation ; Artificial bee colony algorithm ; Adaptive multi-population method ; Meta-heuristics Nseef, Shams K. Abdullah, Salwani Turky, Ayad Kendall, Graham An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems |
| title | An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems |
| title_full | An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems |
| title_fullStr | An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems |
| title_full_unstemmed | An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems |
| title_short | An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems |
| title_sort | adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems |
| topic | Dynamic optimisation ; Artificial bee colony algorithm ; Adaptive multi-population method ; Meta-heuristics |
| url | https://eprints.nottingham.ac.uk/49534/ https://eprints.nottingham.ac.uk/49534/ https://eprints.nottingham.ac.uk/49534/ |