Sea-horse optimizer

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Sea-horse optimizer: A novel nature-inspired meta-heuristic for global optimization problems

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Mise à jour 30 août 2023

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This paper proposes a novel swarm intelligence-based metaheuristic called as sea-horse optimizer (SHO), which is inspired by the movement, predation and breeding behaviors of sea horses in nature. The performance of SHO is evaluated on 23 well-known functions and CEC2014 benchmark functions compared with six state-of-the-art metaheuristic algorithms. Five real-world engineering problems are utilized to test the effectiveness of SHO. The experimental results demonstrate that SHO is a high-performance optimizer and positive adaptability to deal with constraint problems.

Cite this paper as: Zhao S, Zhang T, Ma S, et al. Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems[J]. Applied Intelligence, 2023, 53(10): 11833-11860. DOI: https://doi.org/10.1007/s10489-022-03994-3

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Zhao, Shijie, et al. “Sea-Horse Optimizer: a Novel Nature-Inspired Meta-Heuristic for Global Optimization Problems.” Applied Intelligence, vol. 53, no. 10, Springer Science and Business Media LLC, Sept. 2022, pp. 11833–60, doi:10.1007/s10489-022-03994-3.

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Sea-horse optimizer

Version	Publié le	Notes de version
4.0.0	30 août 2023	Cite update	Télécharger
3.0.0	15 oct. 2022	Cite update	Télécharger
2.0.0	13 sept. 2022	DOI update	Télécharger
1.0.0	6 août 2022		Télécharger

MLA	Zhao, Shijie, et al. “Sea-Horse Optimizer: a Novel Nature-Inspired Meta-Heuristic for Global Optimization Problems.” Applied Intelligence, vol. 53, no. 10, Springer Science and Business Media LLC, Sept. 2022, pp. 11833–60, doi:10.1007/s10489-022-03994-3.
APA	Zhao, S., Zhang, T., Ma, S., & Wang, M. (2022). Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems. Applied Intelligence, 53(10), 11833–11860. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs10489-022-03994-3
BibTeX	@article{Zhao_2022, doi = {10.1007/s10489-022-03994-3}, url = {https://doi.org/10.1007%2Fs10489-022-03994-3}, year = 2022, month = {sep}, publisher = {Springer Science and Business Media {LLC}}, volume = {53}, number = {10}, pages = {11833--11860}, author = {Shijie Zhao and Tianran Zhang and Shilin Ma and Mengchen Wang}, title = {Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems}, journal = {Applied Intelligence} }