A hybrid swarm intelligent optimization algorithm for antenna design problems

Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-...

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Published in:	Scientific reports Vol. 15; no. 1; pp. 4444 - 19
Main Authors:	Singh, Supreet, Singh, Harbinder, Mittal, Nitin, Kaur Punj, Gurpreet, Kumar, Lalit, Fante, Kinde Anlay
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 06.02.2025 Nature Publishing Group Nature Portfolio
Subjects:	639/166 639/705 Algorithms Antenna Array Design Convergence Humanities and Social Sciences multidisciplinary NMRA Optimization algorithms Patch Antenna Design Science Science (multidisciplinary) SOA SSA SSA Patch Antenna Design NMRA SOA Antenna Array Design
ISSN:	2045-2322, 2045-2322
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Abstract	Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-rats, has shown promise but suffers from poor convergence accuracy and a tendency to get trapped in local optima. To address these limitations, this paper proposes an enhanced version of NMRA, called Salp Swarm and Seagull Optimization-based NMRA (SSNMRA), which integrates the search mechanisms of the Seagull Optimization Algorithm (SOA) and the Salp Swarm Algorithm (SSA). This hybrid approach improves the exploration capabilities and convergence performance of NMRA. The effectiveness of SSNMRA is validated through the CEC 2019 benchmark test suite and applied to various electromagnetic optimization problems. Experimental results demonstrate that SSNMRA outperforms existing state-of-the-art algorithms, offering superior optimization capability and enhanced convergence accuracy, making it a promising solution for complex antenna design and other electromagnetic applications.
AbstractList	Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-rats, has shown promise but suffers from poor convergence accuracy and a tendency to get trapped in local optima. To address these limitations, this paper proposes an enhanced version of NMRA, called Salp Swarm and Seagull Optimization-based NMRA (SSNMRA), which integrates the search mechanisms of the Seagull Optimization Algorithm (SOA) and the Salp Swarm Algorithm (SSA). This hybrid approach improves the exploration capabilities and convergence performance of NMRA. The effectiveness of SSNMRA is validated through the CEC 2019 benchmark test suite and applied to various electromagnetic optimization problems. Experimental results demonstrate that SSNMRA outperforms existing state-of-the-art algorithms, offering superior optimization capability and enhanced convergence accuracy, making it a promising solution for complex antenna design and other electromagnetic applications. Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-rats, has shown promise but suffers from poor convergence accuracy and a tendency to get trapped in local optima. To address these limitations, this paper proposes an enhanced version of NMRA, called Salp Swarm and Seagull Optimization-based NMRA (SSNMRA), which integrates the search mechanisms of the Seagull Optimization Algorithm (SOA) and the Salp Swarm Algorithm (SSA). This hybrid approach improves the exploration capabilities and convergence performance of NMRA. The effectiveness of SSNMRA is validated through the CEC 2019 benchmark test suite and applied to various electromagnetic optimization problems. Experimental results demonstrate that SSNMRA outperforms existing state-of-the-art algorithms, offering superior optimization capability and enhanced convergence accuracy, making it a promising solution for complex antenna design and other electromagnetic applications.Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-rats, has shown promise but suffers from poor convergence accuracy and a tendency to get trapped in local optima. To address these limitations, this paper proposes an enhanced version of NMRA, called Salp Swarm and Seagull Optimization-based NMRA (SSNMRA), which integrates the search mechanisms of the Seagull Optimization Algorithm (SOA) and the Salp Swarm Algorithm (SSA). This hybrid approach improves the exploration capabilities and convergence performance of NMRA. The effectiveness of SSNMRA is validated through the CEC 2019 benchmark test suite and applied to various electromagnetic optimization problems. Experimental results demonstrate that SSNMRA outperforms existing state-of-the-art algorithms, offering superior optimization capability and enhanced convergence accuracy, making it a promising solution for complex antenna design and other electromagnetic applications. Abstract Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-rats, has shown promise but suffers from poor convergence accuracy and a tendency to get trapped in local optima. To address these limitations, this paper proposes an enhanced version of NMRA, called Salp Swarm and Seagull Optimization-based NMRA (SSNMRA), which integrates the search mechanisms of the Seagull Optimization Algorithm (SOA) and the Salp Swarm Algorithm (SSA). This hybrid approach improves the exploration capabilities and convergence performance of NMRA. The effectiveness of SSNMRA is validated through the CEC 2019 benchmark test suite and applied to various electromagnetic optimization problems. Experimental results demonstrate that SSNMRA outperforms existing state-of-the-art algorithms, offering superior optimization capability and enhanced convergence accuracy, making it a promising solution for complex antenna design and other electromagnetic applications.
ArticleNumber	4444
Author	Fante, Kinde Anlay Singh, Supreet Singh, Harbinder Kumar, Lalit Mittal, Nitin Kaur Punj, Gurpreet
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Cites_doi	10.1007/s11047-020-09811-5 10.1109/CEC.2019.8789904 10.1038/s41598-024-51218-0 10.1109/TAP.2013.2238654 10.1007/s00521-013-1367-1 10.1016/j.knosys.2018.11.024 10.1007/s00500-020-04749-3 10.1016/j.knosys.2021.106790 10.1108/02644401211235834 10.1109/4235.585893 10.1109/TAP.2019.2938703 10.1016/j.eswa.2014.05.009 10.1109/LAWP.2015.2490103 10.1016/j.eswa.2021.116146 10.1007/s13369-021-06059-8 10.1016/j.cor.2021.105226 10.1016/j.ejor.2009.01.008 10.1016/j.knosys.2023.111351 10.1109/MCI.2006.329691 10.1002/int.22535 10.1016/j.advengsoft.2017.07.002 10.1007/s11042-023-16300-1 10.1023/A:1008202821328 10.1016/j.asoc.2019.02.011 10.1155/2021/6379469 10.1016/j.engappai.2018.04.021 10.1016/j.ins.2020.05.033 10.1007/s00521-019-04464-7 10.1002/int.22470 10.1007/s10462-019-09718-3 10.1016/j.jocs.2020.101104 10.1016/j.eswa.2020.113428 10.1109/ICNN.1995.488968 10.3390/pr9091551 10.1016/j.compbiomed.2024.108803 10.1016/j.apm.2020.08.014 10.1109/ICCES.2006.320481 10.1016/j.advengsoft.2013.12.007 10.1109/TAP.2011.2109678 10.1007/s42235-024-00481-y 10.1016/j.cma.2021.113916
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Snippet	Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single... Abstract Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no...
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SubjectTerms	639/166 639/705 Algorithms Antenna Array Design Convergence Humanities and Social Sciences multidisciplinary NMRA Optimization algorithms Patch Antenna Design Science Science (multidisciplinary) SOA SSA
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Title	A hybrid swarm intelligent optimization algorithm for antenna design problems
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