Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126986
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Type: Conference paper
Title: Optimisation of large wave farms using a multi-strategy evolutionary framework
Author: Neshat, M.
Alexander, B.
Sergiienko, N.Y.
Wagner, M.
Citation: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20), 2020 / vol.abs/2003.09594, pp.1150-1158
Publisher: Association for Computing Machinery
Publisher Place: New York
Issue Date: 2020
ISBN: 9781450371285
Conference Name: Genetic and Evolutionary Computation Conference (GECCO) (08 Jul 2020 - 12 Jul 2020 : Cancún, Mexico)
Statement of
Responsibility: 
Mehdi Neshat, Bradley Alexander, Nataliia Y. Sergiienko, and Markus Wagner
Abstract: Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fullysubmerged three-tether wave energy converters (WECs). Energy maximisation for large farms is a challenging search problem due to the costly calculations of the hydrodynamic interactions between WECs in a large wave farm and the high dimensionality of the search space. To address this problem, we propose a new hybrid multi-strategy evolutionary framework combining smart initialisation, binary population-based evolutionary algorithm, discrete local search and continuous global optimisation. For assessing the performance of the proposed hybrid method, we compare it with a wide variety of state-of-the-art optimisation approaches, including six continuous evolutionary algorithms, four discrete search techniques and three hybrid optimisation methods. The results show that the proposed method performs considerably better in terms of convergence speed and farm output.
Keywords: Wave Energy Converters; Large wave farm; Optimisation; Evolutionary Algorithms; Hybrid multi-strategy evolutionary method; Discrete local search
Rights: © 2020 Association for Computing Machinery.
RMID: 1000018649
DOI: 10.1145/3377930.3390235
Published version: https://dl.acm.org/doi/proceedings/10.1145/3377930
Appears in Collections:Computer Science publications

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