基于黄金正弦机制的天线S参数优化基于蜂的算法与帐混乱
Oluwatayomi Rereloluwa Adegboye1, Afi Kekeli Feda2, Meshack Magaji Ishaya3
1Management Information Systems, University of Mediterranean Karpasia, Nicosia, Mersin, 10, Turkey.
Heliyon
|November 30, 2023
概括
一个新的基于金色阴影机制的蜂蜜子算法与帐混乱 (GST-HBA) 优化天线S参数. 这种增强的方法克服了标准算法的局限性,在天线设计中实现了卓越的性能和准确性.
科学领域:
- 电磁和天线工程 电磁和天线工程
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 像蜂蜜子算法 (HBA) 这样的元启发优化算法是有价值的,但往往遭受过早的融合和有限的人口多样性.
- 天线S参数优化对设备性能至关重要,但现有的方法在效率和准确性方面面临挑战.
研究的目的:
- 引入一种新的优化方法,即基于Golden Sine机制的Tent chaos (GST-HBA) 的Honey Badger算法,用于增强天线S参数优化.
- 解决标准HBA的局限性,特别是过早的融合和缺乏人口多样性的局限性.
主要方法:
- 拟议的GST-HBA将黄金神经机制和帐混乱整合到蜂蜜子算法框架中.
- 该算法的有效性使用20个标准基准函数进行了评估,并随后应用于8个天线S参数函数.
- 性能与其他已建立的优化算法进行了比较.
主要成果:
- 与标准HBA相比,GST-HBA在融合速度和人口多样性方面表现出显著的改善.
- 对基准函数和天线S参数函数的测试证实了算法的稳定性和有效性.
- 对比分析显示,GST-HBA在天线S参数优化任务中表现优于现有的优化算法.
结论:
- GST-HBA为天线S参数优化提供了一种卓越的方法,有效地平衡勘探和开发.
- 黄金神经和帐混乱的集成显著提高了HBA的性能,为天线设计和其他复杂的优化问题提供了强大的工具.
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