Helper and Equivalent Objectives : Efficient Approach for Constrained Optimization
- Submitting institution
-
Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 38976366
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2020.2979821
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 1
- Volume
- N/A
- Issue
- N/A
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
https://ieeexplore.ieee.org/ielx7/6221036/6352949/9049409/supp1-2979821.pdf?tp=&arnumber=9049409
- 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
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Presents a novel multi-objective method for constrained optimisation, published in IEEE TEC, which is one of the top-rated outlets across all areas of computer science and informatics (JCR-Clarivate), having an accepatance rate of ~12%. The underlying algorithm archived in this paper has been the winner of the internationally renowned Competition on Constrained Real Parameter Optimisation organised by CEC-2019; CEC is the largest and most important annual international conference in the field of evolutionary computation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -