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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| Format: | Article |
| Language: | English English |
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Elsevier
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39198/ 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 |
| _version_ | 1848825708537708544 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-15T03:33:13Z |
| format | Article |
| id | ump-39198 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:33:13Z |
| publishDate | 2024 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/39198/ http://umpir.ump.edu.my/id/eprint/39198/ http://umpir.ump.edu.my/id/eprint/39198/ 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 |