A multi-cycled sequential memetic computing approach for constrained optimisation

Sun, Jianyong and Garibaldi, Jonathan M. and 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

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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.

Item Type: Article
Keywords: Multi-cycled sequential memetic computing approach; Estimation of distribution algorithm; Constrained optimisation
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.ins.2016.01.003
Depositing User: Garibadi, Prof Jon
Date Deposited: 20 Jul 2016 13:18
Last Modified: 20 Sep 2016 16:19
URI: http://eprints.nottingham.ac.uk/id/eprint/35193

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