Helper and equivalent objectives: efficient approach for constrained optimization
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 16 - 1312308
- Type
- D - Journal article
- DOI
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10.1109/tcyb.2020.2979821
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 1
- Volume
- 00
- Issue
- -
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2020
- 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
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2
- Research group(s)
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A - Computing and Informatics Research Centre
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Proposed is new approach of designing efficient multiobjective evolutionary algorithms for solving constrained optimization problems through combining helper and equivalent objectives together. The significance of this paper is that the designed algorithm was ranked 1st in the IEEE Congress on Evolutionary Computation Competition on Constrained Real Parameter Optimization [https://github.com/P-N-Suganthan/CEC2017]. This was the first time that a multiobjective evolutionary algorithm beats all state-of-art single-objective evolutionary algorithms in the competition. This IEEE conference is one of the largest and most important annual international conferences in the field of evolutionary computation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
