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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Sea Lion Optimization Algorithm

Author 1: Raja Masadeh Author 2: Basel A. Mahafzah Author 3: Ahmad Sharieh
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 10, No. 5 · Published 2019 · Cited by 204

DOI: https://doi.org/10.14569/IJACSA.2019.0100548

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Abstract

This paper suggests a new nature inspired metaheuristic optimization algorithm which is called Sea Lion Optimization (SLnO) algorithm. The SLnO algorithm imitates the hunting behavior of sea lions in nature. Moreover, it is inspired by sea lions' whiskers that are used in order to detect the prey. SLnO algorithm is tested with 23 well-known test functions (Benchmarks). Optimization results show that the SLnO algorithm is very competitive compared to Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA) and Dragonfly Algorithm (DA).

Keywords

Optimization Metaheuristic optimization algorithms Benchmarks Sea Lion Optimization Algorithm (SLnO)

How to Cite this Article

Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. International Journal of Advanced Computer Science and Applications, 10(5). https://doi.org/10.14569/IJACSA.2019.0100548

Masadeh, Raja, et al.. "Sea Lion Optimization Algorithm." International Journal of Advanced Computer Science and Applications, vol. 10, no. 5, 2019, https://doi.org/10.14569/IJACSA.2019.0100548.

@article{Masadeh2019,
  title     = {Sea Lion Optimization Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {5},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Raja Masadeh and Basel A. Mahafzah and Ahmad Sharieh},
  doi       = {10.14569/IJACSA.2019.0100548},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100548}
}

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