A multi-cycled sequential memetic computing approach for constrained optimisation
In this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles. At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/35193/ |
| _version_ | 1848795025179148288 |
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| author | Sun, Jianyong Garibaldi, Jonathan M. Zhang, Yongquan Al-Shawabkeh, Abdallah |
| author_facet | Sun, Jianyong Garibaldi, Jonathan M. Zhang, Yongquan Al-Shawabkeh, Abdallah |
| author_sort | Sun, Jianyong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles. At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables the learning of useful knowledge from previous cycles and the transfer of the knowledge to facilitate search in latter cycles. Specifically, we propose to apply an estimation of distribution algorithm (EDA) to explore the search space until convergence at each cycle. A local optimiser, called DONLP2, is then applied to improve the best solution found by the EDA. New cycle starts after the local improvement if the computation budget has not been exceeded. In the developed EDA, an adaptive fully-factorized multivariate probability model is proposed. A learning mechanism, implemented as the guided mutation operator, is adopted to learn useful knowledge from previous cycles.
The developed algorithm was experimentally studied on the benchmark problems in the CEC 2006 and 2010 competition. Experimental studies have shown that the developed probability model exhibits excellent exploration capability and the learning mechanism can significantly improve the search efficiency under certain conditions. The comparison against some well-known algorithms showed the superiority of the developed algorithm in terms of the consumed fitness evaluations and the solution quality. |
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| format | Article |
| id | nottingham-35193 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:25:31Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-351932020-05-04T17:44:13Z https://eprints.nottingham.ac.uk/35193/ A multi-cycled sequential memetic computing approach for constrained optimisation Sun, Jianyong Garibaldi, Jonathan M. Zhang, Yongquan Al-Shawabkeh, Abdallah In this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles. At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables the learning of useful knowledge from previous cycles and the transfer of the knowledge to facilitate search in latter cycles. Specifically, we propose to apply an estimation of distribution algorithm (EDA) to explore the search space until convergence at each cycle. A local optimiser, called DONLP2, is then applied to improve the best solution found by the EDA. New cycle starts after the local improvement if the computation budget has not been exceeded. In the developed EDA, an adaptive fully-factorized multivariate probability model is proposed. A learning mechanism, implemented as the guided mutation operator, is adopted to learn useful knowledge from previous cycles. The developed algorithm was experimentally studied on the benchmark problems in the CEC 2006 and 2010 competition. Experimental studies have shown that the developed probability model exhibits excellent exploration capability and the learning mechanism can significantly improve the search efficiency under certain conditions. The comparison against some well-known algorithms showed the superiority of the developed algorithm in terms of the consumed fitness evaluations and the solution quality. Elsevier 2016-05-01 Article PeerReviewed Sun, Jianyong, Garibaldi, Jonathan M., Zhang, Yongquan and Al-Shawabkeh, Abdallah (2016) A multi-cycled sequential memetic computing approach for constrained optimisation. Information Sciences, 340-341 . pp. 175-190. ISSN 1872-6291 Multi-cycled sequential memetic computing approach; Estimation of distribution algorithm; Constrained optimisation http://www.sciencedirect.com/science/article/pii/S0020025516000050 doi:10.1016/j.ins.2016.01.003 doi:10.1016/j.ins.2016.01.003 |
| spellingShingle | Multi-cycled sequential memetic computing approach; Estimation of distribution algorithm; Constrained optimisation Sun, Jianyong Garibaldi, Jonathan M. Zhang, Yongquan Al-Shawabkeh, Abdallah A multi-cycled sequential memetic computing approach for constrained optimisation |
| title | A multi-cycled sequential memetic computing approach for constrained optimisation |
| title_full | A multi-cycled sequential memetic computing approach for constrained optimisation |
| title_fullStr | A multi-cycled sequential memetic computing approach for constrained optimisation |
| title_full_unstemmed | A multi-cycled sequential memetic computing approach for constrained optimisation |
| title_short | A multi-cycled sequential memetic computing approach for constrained optimisation |
| title_sort | multi-cycled sequential memetic computing approach for constrained optimisation |
| topic | Multi-cycled sequential memetic computing approach; Estimation of distribution algorithm; Constrained optimisation |
| url | https://eprints.nottingham.ac.uk/35193/ https://eprints.nottingham.ac.uk/35193/ https://eprints.nottingham.ac.uk/35193/ |