ISBN: 978-981-11-0008-6 DOI: 10.18178/wcse.2016.06.120
Sea Turtle Foraging Algorithm for Continuous Optimization Problems
Abstract— For several modern algorithms—such as Genetic Algorithm (GA), Bee Colony Foraging
Algorithm (BCFA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)—their
evolving searching and learning processes to obtain the best answer in a reasonable time imitate the behaviors
of animals in nature. This article presents a new algorithm called Sea Turtle Foraging Algorithm (STFA) that
imitates sea turtles’ food searching behavior of tracking the odor trail of Dimethyl Sulfide (DMS) originated
from food sources. The displacement of a turtle is dictated by its active swimming movement and its passive
movement due to ocean current. Our proposed STFA was performance tested with 5 standard test functions,
and it was found that STFA was very effective and efficient.
Index Terms— sea turtle foraging, nature inspired algorithm, continuous optimization problems.
Daranat Tansui, Arit Thammano
Computational Intelligence Laboratory, Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, THAILAND
Cite: Daranat Tansui, Arit Thammano, "Sea Turtle Foraging Algorithm for Continuous Optimization Problems," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 678-681, Tokyo, 17-19 June, 2016.