Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1321876
- Type
- D - Journal article
- DOI
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10.1016/j.renene.2016.12.022
- Title of journal
- Renewable Energy
- Article number
- -
- First page
- 473
- Volume
- 105
- Issue
- -
- ISSN
- 0960-1481
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 30
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This study is the first to investigate the performance of selection hyper-heuristics, embedding different algorithmic components and managing evolutionary algorithms for multi-objective wind farm layout optimisation. The best performing multi-objective hyper-heuristic enabled the analyses of trade-off solutions for various wind farm case scenarios, providing novel insights into the interactions between objectives. The paper was used to showcase our capability in multi-objective optimisation and led to a KTP with EventMap (10618), as well as links with Romax and EON. The paper is within the top 10% of highly cited papers in Computer Science according to the InCites Essential Science Indicators.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
