Late Acceptance Selection Hyper-heuristic for Wind Farm Layout Optimisation Problem

Abdulaziz, H. and Elnahas, A. and Daffalla, A. and Noureldien, Y. and Kheiri, A. and Özcan, E. (2018) Late Acceptance Selection Hyper-heuristic for Wind Farm Layout Optimisation Problem. In: 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, pp. 1-5. ISBN 9781538641231

[img]
Preview
PDF (ICCCEEE18B)
ICCCEEE18B.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (240kB)

Abstract

Wind is a promising source of renewable energy which can be harvested using wind turbines placed on farms. An efficient wind farm layout achieving various engineering and financial objectives is crucial to ensure the sustainability and continuity of energy production. In this study, a high-level search technique, namely late acceptance selection hyper-heuristic is applied to optimise the layout of wind farms. This approach aims to find the best placement of turbines at a given site, maximising the energy output while minimising the cost at the same time. The computational experiments indicate that the late acceptance selection hyper-heuristic improves upon the performance of a previously proposed genetic algorithm across all scenarios and an iterated local search over the majority of scenarios considering the best solutions obtained by each algorithm over the runs.

Item Type: Contribution in Book/Report/Proceedings
Additional Information: ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Subjects:
Departments: Lancaster University Management School > Management Science
ID Code: 131103
Deposited By: ep_importer_pure
Deposited On: 07 Mar 2019 12:00
Refereed?: Yes
Published?: Published
Last Modified: 18 Feb 2020 05:31
URI: https://eprints.lancs.ac.uk/id/eprint/131103

Actions (login required)

View Item View Item