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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Late Acceptance Selection Hyper-heuristic for Wind Farm Layout Optimisation Problem. / Abdulaziz, H.; Elnahas, A.; Daffalla, A.; Noureldien, Y.; Kheiri, A.; Özcan, E.

2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2018. p. 1-5.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Abdulaziz, H, Elnahas, A, Daffalla, A, Noureldien, Y, Kheiri, A & 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. https://doi.org/10.1109/ICCCEEE.2018.8515808

APA

Abdulaziz, H., Elnahas, A., Daffalla, A., Noureldien, Y., Kheiri, A., & 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) (pp. 1-5). IEEE. https://doi.org/10.1109/ICCCEEE.2018.8515808

Vancouver

Abdulaziz H, Elnahas A, Daffalla A, Noureldien Y, Kheiri A, Özcan E. Late Acceptance Selection Hyper-heuristic for Wind Farm Layout Optimisation Problem. In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE. 2018. p. 1-5 https://doi.org/10.1109/ICCCEEE.2018.8515808

Author

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

Bibtex

@inproceedings{b879dbadadc845049b8ee2acb5985bf1,
title = "Late Acceptance Selection Hyper-heuristic for Wind Farm Layout Optimisation Problem",
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.",
keywords = "genetic algorithms, search problems, wind power plants, wind turbines, renewable energy, wind farm layout optimisation problem, late acceptance selection hyper-heuristic, wind farms, Wind turbines, Layout, Wind farms, Genetic algorithms, Optimization, Production, Wind Energy Generation, Renewable Energy Sources, Heuristic Algorithms, Genetic Algorithms",
author = "H. Abdulaziz and A. Elnahas and A. Daffalla and Y. Noureldien and A. Kheiri and E. {\"O}zcan",
note = "{\textcopyright}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.",
year = "2018",
month = nov
day = "1",
doi = "10.1109/ICCCEEE.2018.8515808",
language = "English",
pages = "1--5",
booktitle = "2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Late Acceptance Selection Hyper-heuristic for Wind Farm Layout Optimisation Problem

AU - Abdulaziz, H.

AU - Elnahas, A.

AU - Daffalla, A.

AU - Noureldien, Y.

AU - Kheiri, A.

AU - Özcan, E.

N1 - ©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.

PY - 2018/11/1

Y1 - 2018/11/1

N2 - 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.

AB - 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.

KW - genetic algorithms

KW - search problems

KW - wind power plants

KW - wind turbines

KW - renewable energy

KW - wind farm layout optimisation problem

KW - late acceptance selection hyper-heuristic

KW - wind farms

KW - Wind turbines

KW - Layout

KW - Wind farms

KW - Genetic algorithms

KW - Optimization

KW - Production

KW - Wind Energy Generation

KW - Renewable Energy Sources

KW - Heuristic Algorithms

KW - Genetic Algorithms

U2 - 10.1109/ICCCEEE.2018.8515808

DO - 10.1109/ICCCEEE.2018.8515808

M3 - Conference contribution/Paper

SP - 1

EP - 5

BT - 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)

PB - IEEE

ER -