A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference points
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 4 - 911110
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2019.02.040
- Title of journal
- Applied Soft Computing
- Article number
- S1568494619301048
- First page
- 447
- Volume
- 78
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Access exception
- Month of publication
- March
- Year of publication
- 2019
- 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
-
3
- Research group(s)
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A - Computing and Informatics Research Centre
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "The significance of this paper is a new two-archive mechanism for improving the performance of the classical Non-dominated Sorting Genetic Algorithm-III. This two-archive method has been recognised by other researchers, with comments like “the convergence and diversity of solutions were guaranteed by two archives respectively in
NSGA-III-UE and achieved good results” [https://doi.org/10.1007/s10489-020-02053-z]” and two archives “establish repositories to maintain convergence and diversity performance of the
approximate Pareto solution” [https://doi.org/10.1016/j.asoc.2020.106199]. Following from this work, another double-archive mechanism was proposed by other researchers in a multi-objective optimization algorithm [https://doi.org/10.1109/ACCESS.2019.2960472]. This research leads to a new cooperation with Harbin Engineering University."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
