Evolutionary computation for wind farm layout optimization
This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farm...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
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Elsevier
2018
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| Online Access: | https://eprints.nottingham.ac.uk/50864/ |
| _version_ | 1848798358585475072 |
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| author | Wilson, Dennis Rodrigues, Silvio Segura, Carlos Loshchilov, Ilya Huttor, Frank Buenfil, Guillermo López Kheiri, Ahmed Keedwell, Ed Ocampo-Pineda, Mario Özcan, Ender Peña, Sergio Ivvan Valdez Goldman, Brian Rionda, Salvador Botello Hernández-Aguirre, Arturo Veeramachaneni, Kalyan Sylvain, Cussat-Blanc |
| author_facet | Wilson, Dennis Rodrigues, Silvio Segura, Carlos Loshchilov, Ilya Huttor, Frank Buenfil, Guillermo López Kheiri, Ahmed Keedwell, Ed Ocampo-Pineda, Mario Özcan, Ender Peña, Sergio Ivvan Valdez Goldman, Brian Rionda, Salvador Botello Hernández-Aguirre, Arturo Veeramachaneni, Kalyan Sylvain, Cussat-Blanc |
| author_sort | Wilson, Dennis |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results. |
| first_indexed | 2025-11-14T20:18:30Z |
| format | Article |
| id | nottingham-50864 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:18:30Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-508642020-05-04T19:49:24Z https://eprints.nottingham.ac.uk/50864/ Evolutionary computation for wind farm layout optimization Wilson, Dennis Rodrigues, Silvio Segura, Carlos Loshchilov, Ilya Huttor, Frank Buenfil, Guillermo López Kheiri, Ahmed Keedwell, Ed Ocampo-Pineda, Mario Özcan, Ender Peña, Sergio Ivvan Valdez Goldman, Brian Rionda, Salvador Botello Hernández-Aguirre, Arturo Veeramachaneni, Kalyan Sylvain, Cussat-Blanc This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results. Elsevier 2018-10-01 Article PeerReviewed Wilson, Dennis, Rodrigues, Silvio, Segura, Carlos, Loshchilov, Ilya, Huttor, Frank, Buenfil, Guillermo López, Kheiri, Ahmed, Keedwell, Ed, Ocampo-Pineda, Mario, Özcan, Ender, Peña, Sergio Ivvan Valdez, Goldman, Brian, Rionda, Salvador Botello, Hernández-Aguirre, Arturo, Veeramachaneni, Kalyan and Sylvain, Cussat-Blanc (2018) Evolutionary computation for wind farm layout optimization. Renewable Energy, 126 . pp. 681-691. ISSN 1879-0682 wind farm layout optimization evolutionary algorithm competition https://www.sciencedirect.com/science/article/pii/S096014811830363X doi:10.1016/j.renene.2018.03.052 doi:10.1016/j.renene.2018.03.052 |
| spellingShingle | wind farm layout optimization evolutionary algorithm competition Wilson, Dennis Rodrigues, Silvio Segura, Carlos Loshchilov, Ilya Huttor, Frank Buenfil, Guillermo López Kheiri, Ahmed Keedwell, Ed Ocampo-Pineda, Mario Özcan, Ender Peña, Sergio Ivvan Valdez Goldman, Brian Rionda, Salvador Botello Hernández-Aguirre, Arturo Veeramachaneni, Kalyan Sylvain, Cussat-Blanc Evolutionary computation for wind farm layout optimization |
| title | Evolutionary computation for wind farm layout optimization |
| title_full | Evolutionary computation for wind farm layout optimization |
| title_fullStr | Evolutionary computation for wind farm layout optimization |
| title_full_unstemmed | Evolutionary computation for wind farm layout optimization |
| title_short | Evolutionary computation for wind farm layout optimization |
| title_sort | evolutionary computation for wind farm layout optimization |
| topic | wind farm layout optimization evolutionary algorithm competition |
| url | https://eprints.nottingham.ac.uk/50864/ https://eprints.nottingham.ac.uk/50864/ https://eprints.nottingham.ac.uk/50864/ |