A hybrid swarm intelligent optimization algorithm for antenna design problems.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-02-06 DOI:10.1038/s41598-025-88846-z

Supreet Singh, Harbinder Singh, Nitin Mittal, Gurpreet Kaur Punj, Lalit Kumar, Kinde Anlay Fante

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引用次数: 0

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.

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天线设计问题的混合群智能优化算法。

元启发式优化算法由于其在解决复杂问题方面的不同应用而取得了重大进展。然而，没有一种算法可以有效地解决所有的优化挑战。受裸鼹鼠交配模式启发的裸鼹鼠-老鼠算法（NMRA）显示出了希望，但存在收敛精度差和陷入局部最优的倾向。为了解决这些限制，本文提出了一种增强版本的NMRA，称为基于Salp Swarm and Seagull Optimization based NMRA (SSNMRA)，它集成了Seagull Optimization Algorithm （SOA）和Salp Swarm Algorithm （SSA）的搜索机制。这种混合方法提高了NMRA的探测能力和收敛性能。通过CEC 2019基准测试套件验证了SSNMRA的有效性，并应用于各种电磁优化问题。实验结果表明，SSNMRA优于现有的最先进算法，具有卓越的优化能力和更高的收敛精度，使其成为复杂天线设计和其他电磁应用的有前途的解决方案。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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