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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-88846-z