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Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application

This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of thes...

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Main Authors:	Ahmed, Marzia, Mohd Herwan, Sulaiman, Ahmad Johari, Mohamad, Rahman, Mostafijur
Format:	Article
Language:	English English
Published:	Elsevier 2024
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/39198/1/Gooseneck%20barnacle%20optimization%20algorithm-%20A%20novel%20nature%20.pdf http://umpir.ump.edu.my/id/eprint/39198/2/Gooseneck%20barnacle%20optimization%20algorithm-%20A%20novel%20nature_FULL.pdf http://umpir.ump.edu.my/id/eprint/39198/ https://doi.org/10.1016/j.matcom.2023.10.006 https://doi.org/10.1016/j.matcom.2023.10.006
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id	my.ump.umpir.39198
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spelling	my.ump.umpir.391982024-04-23T07:27:42Z http://umpir.ump.edu.my/id/eprint/39198/ Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application Ahmed, Marzia Mohd Herwan, Sulaiman Ahmad Johari, Mohamad Rahman, Mostafijur TK Electrical engineering. Electronics Nuclear engineering This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of these microorganisms that have thrived since the Jurassic period. In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. The algorithm incorporates essential factors, such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, creating two vital optimisation stages: exploration and exploitation. Real-world case studies and mathematical test functions serve as qualitative and quantitative benchmarks. The results demonstrate that GBO outperforms well-known algorithms, including the previous BMO, by effectively improving the initial random population for a given problem, converging to the global optimum, and producing significantly better optimisation outcomes Elsevier 2024-04 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39198/1/Gooseneck%20barnacle%20optimization%20algorithm-%20A%20novel%20nature%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/39198/2/Gooseneck%20barnacle%20optimization%20algorithm-%20A%20novel%20nature_FULL.pdf Ahmed, Marzia and Mohd Herwan, Sulaiman and Ahmad Johari, Mohamad and Rahman, Mostafijur (2024) Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application. Mathematics and Computers in Simulation, 218. pp. 248-265. ISSN 0378-4754. (Published) https://doi.org/10.1016/j.matcom.2023.10.006 https://doi.org/10.1016/j.matcom.2023.10.006
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	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Ahmed, Marzia Mohd Herwan, Sulaiman Ahmad Johari, Mohamad Rahman, Mostafijur Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
description	This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of these microorganisms that have thrived since the Jurassic period. In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. The algorithm incorporates essential factors, such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, creating two vital optimisation stages: exploration and exploitation. Real-world case studies and mathematical test functions serve as qualitative and quantitative benchmarks. The results demonstrate that GBO outperforms well-known algorithms, including the previous BMO, by effectively improving the initial random population for a given problem, converging to the global optimum, and producing significantly better optimisation outcomes
format	Article
author	Ahmed, Marzia Mohd Herwan, Sulaiman Ahmad Johari, Mohamad Rahman, Mostafijur
author_facet	Ahmed, Marzia Mohd Herwan, Sulaiman Ahmad Johari, Mohamad Rahman, Mostafijur
author_sort	Ahmed, Marzia
title	Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
title_short	Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
title_full	Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
title_fullStr	Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
title_full_unstemmed	Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
title_sort	gooseneck barnacle optimization algorithm: a novel nature inspired optimization theory and application
publisher	Elsevier
publishDate	2024
url	http://umpir.ump.edu.my/id/eprint/39198/1/Gooseneck%20barnacle%20optimization%20algorithm-%20A%20novel%20nature%20.pdf http://umpir.ump.edu.my/id/eprint/39198/2/Gooseneck%20barnacle%20optimization%20algorithm-%20A%20novel%20nature_FULL.pdf http://umpir.ump.edu.my/id/eprint/39198/ https://doi.org/10.1016/j.matcom.2023.10.006 https://doi.org/10.1016/j.matcom.2023.10.006
_version_	1822924217930219520
score	13.232414

Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application

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