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    Rights statement: This is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 126, 2018 DOI: 10.1016/j.renene.2018.03.052

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Evolutionary computation for wind farm layout optimization

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Evolutionary computation for wind farm layout optimization. / Wilson, Dennis; Rodrigues, Silvio; Segura, Carlos et al.
In: Renewable Energy, Vol. 126, 10.2018, p. 681-691.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wilson, D, Rodrigues, S, Segura, C, Loshchilov, I, Hutter, F, Buenfil, GL, Kheiri, A, Keedwell, E, Ocampo-Pineda, M, Özcan, E, Peña, SIV, Goldman, B, Rionda, SB, Hernández-Aguirre, A, Veeramachaneni, K & Cussat-Blanc, S 2018, 'Evolutionary computation for wind farm layout optimization', Renewable Energy, vol. 126, pp. 681-691. https://doi.org/10.1016/j.renene.2018.03.052

APA

Wilson, D., Rodrigues, S., Segura, C., Loshchilov, I., Hutter, F., Buenfil, G. L., Kheiri, A., Keedwell, E., Ocampo-Pineda, M., Özcan, E., Peña, S. I. V., Goldman, B., Rionda, S. B., Hernández-Aguirre, A., Veeramachaneni, K., & Cussat-Blanc, S. (2018). Evolutionary computation for wind farm layout optimization. Renewable Energy, 126, 681-691. https://doi.org/10.1016/j.renene.2018.03.052

Vancouver

Wilson D, Rodrigues S, Segura C, Loshchilov I, Hutter F, Buenfil GL et al. Evolutionary computation for wind farm layout optimization. Renewable Energy. 2018 Oct;126:681-691. Epub 2018 Mar 23. doi: 10.1016/j.renene.2018.03.052

Author

Wilson, Dennis ; Rodrigues, Silvio ; Segura, Carlos et al. / Evolutionary computation for wind farm layout optimization. In: Renewable Energy. 2018 ; Vol. 126. pp. 681-691.

Bibtex

@article{b109ac1cfe574989b547c58cc506f55a,
title = "Evolutionary computation for wind farm layout optimization",
abstract = "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.",
keywords = "Wind farm layout optimization, Evolutionary algorithm, Competition",
author = "Dennis Wilson and Silvio Rodrigues and Carlos Segura and Ilya Loshchilov and Frank Hutter and Buenfil, {Guillermo L{\'o}pez} and Ahmed Kheiri and Ed Keedwell and Mario Ocampo-Pineda and Ender {\"O}zcan and Pe{\~n}a, {Sergio Ivvan Valdez} and Brian Goldman and Rionda, {Salvador Botello} and Arturo Hern{\'a}ndez-Aguirre and Kalyan Veeramachaneni and Sylvain Cussat-Blanc",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 126, 2018 DOI: 10.1016/j.renene.2018.03.052",
year = "2018",
month = oct,
doi = "10.1016/j.renene.2018.03.052",
language = "English",
volume = "126",
pages = "681--691",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Evolutionary computation for wind farm layout optimization

AU - Wilson, Dennis

AU - Rodrigues, Silvio

AU - Segura, Carlos

AU - Loshchilov, Ilya

AU - Hutter, Frank

AU - Buenfil, Guillermo López

AU - Kheiri, Ahmed

AU - Keedwell, Ed

AU - Ocampo-Pineda, Mario

AU - Özcan, Ender

AU - Peña, Sergio Ivvan Valdez

AU - Goldman, Brian

AU - Rionda, Salvador Botello

AU - Hernández-Aguirre, Arturo

AU - Veeramachaneni, Kalyan

AU - Cussat-Blanc, Sylvain

N1 - This is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 126, 2018 DOI: 10.1016/j.renene.2018.03.052

PY - 2018/10

Y1 - 2018/10

N2 - 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.

AB - 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.

KW - Wind farm layout optimization

KW - Evolutionary algorithm

KW - Competition

U2 - 10.1016/j.renene.2018.03.052

DO - 10.1016/j.renene.2018.03.052

M3 - Journal article

VL - 126

SP - 681

EP - 691

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -