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A new hybrid PSOGSA algorithm for function optimization
Abstract
In this paper, a new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms' strength. Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA.
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Cites
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Cited by
Radosavljevic, J., Arsic, N., Milovanovic, M., & Ktena, A. (2020) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2019.000259 · Cited by 122
Ansari, S., Hannan, M. A., Ayob, A., Abdolrasol, M. G. M., & Dar, M. A. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2024.114711 · Cited by 20
Radosavljević, J., Ktena, A., Gajić, M., Milovanović, M., & Živić, J. (2023) · Energies · doi:10.3390/en16114356 · Cited by 10
Metadata
- Authors
- Mirjalili, S., & Hashim, S. Z. M.
- Venue
- 2010 International Conference on Computer and Information Application
- Publisher
- IEEE
- Published
- 2010-12
- Pages
- 374-377
- OpenAlex
- W2542691202
- Cited by
- 631
- References
- 1
- FWCI
- 5.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mirjalili, S., & Hashim, S. Z. M. (2010). A new hybrid PSOGSA algorithm for function optimization. 2010 International Conference on Computer and Information Application. https://doi.org/10.1109/iccia.2010.6141614
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