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IEEE Transactions on Evolutionary Computation
Abstract
Abstract—Trial vector generation strategies and control parameters have a significant influence on the performance of differential evolution (DE). This paper studies whether the performance of DE can be improved by combining several effective trial vector generation strategies with some suitable control parameter settings. A novel method, called composite DE (CoDE), has been proposed in this paper. This method uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. CoDE has been tested on all the CEC2005 contest test instances. Experimental results show that CoDE is very competitive. Index Terms—Differential evolution, trial vector generation strategy, control parameters, global numerical optimization I.
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Cited by
Dong, Z., Danilevskaya, O., Abadie, T., Messina, C., Coles, N., & Cooper, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0043450 · Cited by 133
Insider threat detection using supervised machine learning algorithms
Manoharan, P., Yin, J., Wang, H., Zhang, Y., & Ye, W. (2024) · Telecommunication Systems · doi:10.1007/s11235-023-01085-3 · Cited by 34
Villard, J., Kılıç, M., & Rothlisberger, U. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01078 · Cited by 19
Evolutionary Algorithms for Wireless Communications — A Review of the State-of-the art
Goudos, S. K. (2014) · Contemporary Issues in Wireless Communications · doi:10.5772/59147 · Cited by 13
Metadata
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2009-08
- Volume
- 4
- Issue
- 3
- Pages
- C2-C2
- ISSN
- 1556-603X (ISSN-L)
- OpenAlex
- W4206237891
- Cited by
- 797
- References
- 0
- FWCI
- 30.36
- Type
- journal article
- Language
- en
- Open access
- closed
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