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A multi-cycled sequential memetic computing approach for constrained optimisation

Sun, Jianyong; Garibaldi, Jonathan M.; Zhang, Yongquan; Al-Shawabkeh, Abdallah

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Authors

Jianyong Sun

Yongquan Zhang

Abdallah Al-Shawabkeh



Abstract

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.

Citation

Sun, J., Garibaldi, J. M., Zhang, Y., & Al-Shawabkeh, A. (2016). A multi-cycled sequential memetic computing approach for constrained optimisation. Information Sciences, 340-341, 175-190. https://doi.org/10.1016/j.ins.2016.01.003

Journal Article Type Article
Acceptance Date Jan 2, 2016
Online Publication Date Jan 11, 2016
Publication Date May 1, 2016
Deposit Date Jul 20, 2016
Publicly Available Date Jul 20, 2016
Journal Information Sciences
Print ISSN 0020-0255
Electronic ISSN 1872-6291
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 340-341
Pages 175-190
DOI https://doi.org/10.1016/j.ins.2016.01.003
Keywords Multi-cycled sequential memetic computing approach; Estimation of distribution algorithm; Constrained optimisation
Public URL https://nottingham-repository.worktribe.com/output/782871
Publisher URL http://www.sciencedirect.com/science/article/pii/S0020025516000050
Contract Date Jul 20, 2016

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