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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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -