An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
- Submitting institution
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The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9014494_4
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2014.2308305
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 201
- Volume
- 19
- Issue
- 2
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Citation count
- 208
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work proposes a non-dominated sorting method with time complexity much lower than that of all existing algorithms. The significance of this work is that a tree-based sorting algorithm was also developed based on ideas proposed in this work, significantly reducing the computational complexity particularly when the number of objectives is high. The sorting algorithm has been very well received in the evolutionary multi-objective optimization community, and can also be extended to other tasks involving sorting items having multiple conflicting criteria. Specifically, the reduction in time complexity supported the practical application of many-objective optimisation to automotive engineering (UoA 11 ICS).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -