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Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation

Li, Wenwen; Özcan, Ender; John, Robert

Authors

Wenwen Li

Robert John



Abstract

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. However, due to the complexity of handling multiple objectives simultaneously, many approaches proposed in the literature often focus on the optimisation of a single objective when deciding the locations for a set of wind turbines spread across a given region. In this study, we tackle a multi-objective wind farm layout optimisation problem. Different from the previously proposed approaches, we are applying a high-level search method, known as selection hyper-heuristic to solve this problem. Selection hyper-heuristics mix and control a predefined set of low-level (meta)heuristics which operate on solutions. We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem. Our hyper-heuristic approaches manage three well-known multi-objective evolutionary algorithms as low-level metaheuristics. The empirical results indicate the success and potential of selection hyper-heuristics for solving this computationally difficult problem. We additionally explore other objectives in wind farm layout optimisation problems to gain a better understanding of the conflicting nature of those objectives.

Citation

Li, W., Özcan, E., & John, R. (2017). Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation. Renewable Energy, 105, https://doi.org/10.1016/j.renene.2016.12.022

Journal Article Type Article
Acceptance Date Dec 11, 2016
Online Publication Date Dec 18, 2016
Publication Date May 1, 2017
Deposit Date Dec 20, 2016
Publicly Available Date Dec 20, 2016
Journal Renewable Energy
Print ISSN 0960-1481
Electronic ISSN 1879-0682
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 105
DOI https://doi.org/10.1016/j.renene.2016.12.022
Keywords Wind farm; Layout design; Optimisation; Hyper-heuristics; Evolutionary algorithms; Operation research
Public URL http://eprints.nottingham.ac.uk/id/eprint/39371
Publisher URL http://www.sciencedirect.com/science/article/pii/S0960148116310709?via%3Dihub
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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