A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 079-207748-5133
- Type
- D - Journal article
- DOI
-
10.1109/tcyb.2019.2925015
- Title of journal
- Ieee Transactions On Cybernetics
- Article number
- -
- First page
- 1085
- Volume
- 51
- Issue
- 2
- ISSN
- 2168-2267
- Open access status
- Not compliant
- Month of publication
- July
- Year of publication
- 2019
- URL
-
http://bura.brunel.ac.uk/handle/2438/21857
- 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
-
5
- Research group(s)
-
2 - Software, Systems & Security (SSS)
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A novel particle swarm optimization algorithm capable of fast convergence is proposed by employing a new adaptive weighting strategy to update the acceleration coefficients with sigmoid function. The algorithm is comprehensively evaluated via eight well-known benchmark functions which demonstrate that it outperforms some classic PSO algorithms. The work is published in IEEE TC that is ranked No 1 among all journals in Computer Science: Cybernetics according to the Web of Science.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
