A Dynamic-Neighborhood-Based Switching Particle Swarm Optimization Algorithm
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 093-228683-5133
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2020.3029748
- Title of journal
- Ieee Transactions On Cybernetics
- Article number
- -
- First page
- 1
- Volume
- in press
- Issue
- -
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2020
- URL
-
http://bura.brunel.ac.uk/handle/2438/21856
- 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
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in a journal ranked No 1 in Computer Science: Cybernetics according to the Web of Science, the paper proposes a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm. The main innovations include a new velocity updating mechanism, a novel switching learning strategy and a differential evolution approach.
Extensive experimental results demonstrate that DNSPSO outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
