Sea-horse optimizer
Sea-horse optimizer: A novel nature-inspired meta-heuristic for global optimization problems
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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
It has been consistently selected for ESI-HOT Paper/Highly Cited Papers since July 2024.
Cite As
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.
General Information
- Version 5.0.0 (6.54 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
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- macOS
- Linux
