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AFSA-SLnO variants for enhanced global optimization

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 a...

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Main Authors:	Norazian, Subari, Junita, Mohamad-Saleh, Noorazliza, Sulaiman
Format:	Conference or Workshop Item
Language:	English English
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/39634/1/AFSA-SLnO%20Variants%20for%20Enhanced%20Global%20Optimization.pdf http://umpir.ump.edu.my/id/eprint/39634/2/AFSA-SLnO%20variants%20for%20enhanced%20global%20optimization_ABS.pdf http://umpir.ump.edu.my/id/eprint/39634/ https://doi.org/10.1007/978-981-16-8129-5_79
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id	my.ump.umpir.39634
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spelling	my.ump.umpir.396342023-12-13T04:14:38Z http://umpir.ump.edu.my/id/eprint/39634/ AFSA-SLnO variants for enhanced global optimization Norazian, Subari Junita, Mohamad-Saleh Noorazliza, Sulaiman T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering 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. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39634/1/AFSA-SLnO%20Variants%20for%20Enhanced%20Global%20Optimization.pdf pdf en http://umpir.ump.edu.my/id/eprint/39634/2/AFSA-SLnO%20variants%20for%20enhanced%20global%20optimization_ABS.pdf 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 https://doi.org/10.1007/978-981-16-8129-5_79
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Norazian, Subari Junita, Mohamad-Saleh Noorazliza, Sulaiman AFSA-SLnO variants for enhanced global optimization
description	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.
format	Conference or Workshop Item
author	Norazian, Subari Junita, Mohamad-Saleh Noorazliza, Sulaiman
author_facet	Norazian, Subari Junita, Mohamad-Saleh Noorazliza, Sulaiman
author_sort	Norazian, Subari
title	AFSA-SLnO variants for enhanced global optimization
title_short	AFSA-SLnO variants for enhanced global optimization
title_full	AFSA-SLnO variants for enhanced global optimization
title_fullStr	AFSA-SLnO variants for enhanced global optimization
title_full_unstemmed	AFSA-SLnO variants for enhanced global optimization
title_sort	afsa-slno variants for enhanced global optimization
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/39634/1/AFSA-SLnO%20Variants%20for%20Enhanced%20Global%20Optimization.pdf http://umpir.ump.edu.my/id/eprint/39634/2/AFSA-SLnO%20variants%20for%20enhanced%20global%20optimization_ABS.pdf http://umpir.ump.edu.my/id/eprint/39634/ https://doi.org/10.1007/978-981-16-8129-5_79
_version_	1822923983780052992
score	13.239859

AFSA-SLnO variants for enhanced global optimization

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