A novel hybrid firefly algorithm for global optimization
- Submitting institution
-
Middlesex University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1411
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0163230
- Title of journal
- PLoS ONE
- Article number
- e0163230
- First page
- 1
- Volume
- 11
- Issue
- 9
- ISSN
- 1932-6203
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
http://eprints.mdx.ac.uk/20900/
- 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
-
3
- Research group(s)
-
-
- Citation count
- 44
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Global optimization can be very challenging to solve due to its nonlinearity and multimodality. This work formulated a new hybrid firefly algorithm (DFA) by co-evolving two different populations with enhanced diversity and design space explorability. The work is significant because rigorous numerical tests and validation using more than a dozen highly nonlinear and multimodal benchmarks show that the DFA has significantly better performance than differential evolution and particle swarm optimization. This hybrid approach has also provided new insights into new search mechanisms for avoiding local minima and increasing the convergence rate of optimisation algorithms.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -