Experienced Grey Wolf Optimizer through Reinforcement Learning and Neural Networks
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 037-125950-15394
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2016.2634548
- Title of journal
- Ieee Transactions On Neural Networks And Learning Systems
- Article number
- -
- First page
- 681
- Volume
- 29
- Issue
- 3
- ISSN
- 2162-2388
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2017
- URL
-
https://ieeexplore.ieee.org/document/7812570
- 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
-
2
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 55
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper, published in a journal ranked 3rd among 108 in Computer Science: Theory and Methods, presents EGWO, a novel technique that proves very efficient while compared with similar state-of-the-art methods for solving large scale optimisation problems. EGWO can also be easily extended to other similar optimisation methods, using exactly the same framework. Furthermore, the paper has been used as a reference in a large number of publications in the field that are either proposing new methods or are survey papers: Ibrahim, Elaziz, &Lu (2018) https://doi.org/10.1016/j.eswa.2018.04.028, Aljarah et all (2018) https://doi.org/10.1016/j.asoc.2018.07.040
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
