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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Sea Lion Optimization Algorithm
Digital Object Identifier (DOI) : 10.14569/IJACSA.2019.0100548
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 5, 2019.
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)
Raja Masadeh, Basel A. Mahafzah and Ahmad Sharieh, “Sea Lion Optimization Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 10(5), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100548
@article{Masadeh2019,
title = {Sea Lion Optimization Algorithm},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0100548},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100548},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {5},
author = {Raja Masadeh and Basel A. Mahafzah and Ahmad Sharieh},
}
