AFSA-SLnO variants for enhanced global optimization

Norazian, Subari and Junita, Mohamad-Saleh and Noorazliza, Sulaiman (2022) AFSA-SLnO variants for enhanced global optimization. In: Lecture Notes in Electrical Engineering; 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021 , 5-6 April 2021 , Virtual, Online. pp. 513-522., 829 LNEE (272139). ISSN 1876-1100 ISBN 978-981168128-8

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Abstract

Artificial fish swarm algorithm (AFSA) is a strategy which imitates the natural behavior of fish swarm in the real environment. Many improvements and modifications have been proposed on AFSA to improve its optimization performance. To date, nevertheless, the existing algorithms are still unable to achieve a satisfactory global optimum. This paper presents incorporation of circle updating position from Sea Lion Optimization (SLnO) into AFSA to enhance the robustness and optimum value. Fifteen benchmarks function have been used to evaluate the performance of the proposed variants in comparison to the standard AFSA and SLnO. The proposed variants show better result compared to the standard AFSA and SLnO.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Artificial Fish Swarm Algorithm (AFSA); Benchmark function; Optimization; Sea Lion Optimization (SLnO); Swarm Intelligence
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	College of Engineering Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	13 Dec 2023 04:14
Last Modified:	13 Dec 2023 04:14
URI:	http://umpir.ump.edu.my/id/eprint/39634
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