A hybrid swarm intelligent optimization algorithm for antenna design problems
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 na...
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| Format: | Article |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88846-z |
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| author | Supreet Singh Harbinder Singh Nitin Mittal Gurpreet Kaur Punj Lalit Kumar Kinde Anlay Fante |
| author_facet | Supreet Singh Harbinder Singh Nitin Mittal Gurpreet Kaur Punj Lalit Kumar Kinde Anlay Fante |
| author_sort | Supreet Singh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f541861c2a9e4b1f810df796643287a7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f541861c2a9e4b1f810df796643287a72025-02-09T12:31:27ZengNature PortfolioScientific Reports2045-23222025-02-0115111910.1038/s41598-025-88846-zA hybrid swarm intelligent optimization algorithm for antenna design problemsSupreet Singh0Harbinder Singh1Nitin Mittal2Gurpreet Kaur Punj3Lalit Kumar4Kinde Anlay Fante5School of Computer Science, UPESDepartment of Electronics & Communication Engineering, University Centre for Research and Development, Chandigarh UniversitySkill Faculty of Engineering and Technology, Shri Viswakarma Skill UniversityComputer Science & Engineering Department, Punjabi UniversitySkill Faculty of Engineering and Technology, Shri Viswakarma Skill UniversityFaculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma UniversityAbstract 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.https://doi.org/10.1038/s41598-025-88846-zSSASOANMRAPatch Antenna DesignAntenna Array Design |
| spellingShingle | Supreet Singh Harbinder Singh Nitin Mittal Gurpreet Kaur Punj Lalit Kumar Kinde Anlay Fante A hybrid swarm intelligent optimization algorithm for antenna design problems Scientific Reports SSA SOA NMRA Patch Antenna Design Antenna Array Design |
| title | A hybrid swarm intelligent optimization algorithm for antenna design problems |
| title_full | A hybrid swarm intelligent optimization algorithm for antenna design problems |
| title_fullStr | A hybrid swarm intelligent optimization algorithm for antenna design problems |
| title_full_unstemmed | A hybrid swarm intelligent optimization algorithm for antenna design problems |
| title_short | A hybrid swarm intelligent optimization algorithm for antenna design problems |
| title_sort | hybrid swarm intelligent optimization algorithm for antenna design problems |
| topic | SSA SOA NMRA Patch Antenna Design Antenna Array Design |
| url | https://doi.org/10.1038/s41598-025-88846-z |
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