The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems
Abstract
In recent years, several multi-method and multi-operator-based algorithms have been proposed for solving optimization problems. Generally, their performance is better than other algorithms that based on a single operator and/or algorithm. However, they do not perform consistently well over all the problems tested in the literature. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple differential evolution operators, with more emphasis placed on the best-performing operator. The performance of the proposed algorithm is tested by solving 10 problems with 5, 10, 15 and 20 dimensions taken from CEC2020 competition on single objective bound constrained optimization, with its results outperforming both single operator-based and different state-of-the-art algorithms.
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Cites
Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
Differential evolution with multi-population based ensemble of mutation strategies
Wu, G., Mallipeddi, R., Suganthan, P. N., Wang, R., & Chen, H. (2016) · Information Sciences · doi:10.1016/j.ins.2015.09.009 · Cited by 569
Ensemble of differential evolution variants
Wu, G., Shen, X., Li, H., Chen, H., Lin, A., & Suganthan, P. N. (2018) · Information Sciences · doi:10.1016/j.ins.2017.09.053 · Cited by 386
Cited by
Fire Hawk Optimizer: a novel metaheuristic algorithm
Azizi, M., Talatahari, S., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10173-w · Cited by 391
Di Valentino, E., Said, J. L., Riess, A., Pollo, A., Poulin, V., Gómez-Valent, A., Weltman, A., Palmese, A., Huang, C. D., Bruck, C. V. D., et al. (2025) · Physics of the Dark Universe · doi:10.1016/j.dark.2025.101965 · Cited by 319
Enhancing differential evolution algorithm with a fitness-distance-based selection strategy
Huang, Y., Qian, X., & Song, W. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06298-0 · Cited by 5
Metadata
- Authors
- Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K., & Ryan, M. J.
- Venue
- 2020 IEEE Congress on Evolutionary Computation (CEC)
- Publisher
- IEEE
- Published
- 2020-07
- Pages
- 1-8
- OpenAlex
- W3083328146
- Cited by
- 242
- References
- 3
- FWCI
- 11.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K., & Ryan, M. J. (2020). Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems. 2020 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/cec48606.2020.9185577
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