Opposition-based Magnetic Optimization Algorithm with parameter adaptation strategy
- Submitting institution
-
University of Hertfordshire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20233905
- Type
- D - Journal article
- DOI
-
10.1016/j.swevo.2015.09.001
- Title of journal
- Swarm and Evolutionary Computation
- Article number
- -
- First page
- 97
- Volume
- 26
- Issue
- -
- ISSN
- 2210-6502
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2015
- 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
-
1
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a new parameter setting strategy for optimization algorithms. Setting the parameters of optimization algorithms is a time-consuming process, which is resolved in this research. Inspired by this research, some follow-on studies have addressed real-world problems including hardware optimization problems [1], predicting cervical hyperextension injury [2], etc.
[1] Das, Soumyadip and Sumitra Mukhopadhyay, Applied Soft Computing 72, 235-260 (2018).
[2] Liu, Guomin, et al., IEEE Access 8, 46895-46908 (2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -