A parallel surrogate model assisted evolutionary algorithm for electromagnetic design optimization
- Submitting institution
-
University of Glasgow
- Unit of assessment
- 12 - Engineering
- Output identifier
- 12-09328
- Type
- D - Journal article
- DOI
-
10.1109/TETCI.2018.2864747
- Title of journal
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Article number
- -
- First page
- 93
- Volume
- 3
- Issue
- 2
- ISSN
- 2471-285X
- Open access status
- Exception within 3 months of publication
- Month of publication
- April
- Year of publication
- 2019
- URL
-
http://eprints.gla.ac.uk/209552/
- 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
-
4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper significantly improves the optimization quality and efficiency of state-of-the-art antenna design, solving more than 20 antenna designs previously unsolved by other tools (samples at http://ai-dac.com/antenna-design-gallery/). It was ranked first in comparisons carried out by The MathWorks and is to be embedded in MATLAB. It is now an essential tool for University design teams and their industry partners.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
