基于粒子群算法和果蝇算法的四分之一圆开槽微带贴片天线优化及对比分析

E. Karpat, Fatih Imamoglu
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引用次数: 1

摘要

结合粒子群优化(PSO)和果蝇优化(FOA)等优化方法,对1.4 ~ 2.65 GHz频段的改进矩形微带天线进行了参数化研究。用优化方法设计天线时,需要得到所需参数的适应度函数。微带贴片天线的谐振频率取决于各种参数,没有标准的频率函数。在本研究中,根据所需的谐振频率设计了矩形贴片天线,并进行了圆形四分之一槽的修改。观察到频率随设计变量(衬底厚度和槽半径)的变化而变化。得到的共振频率是设计变量的函数,并在优化过程中使用它来最小化目标频率与计算频率之间的差异。将原有的FOA算法和粒子群算法应用于改进矩形贴片天线的设计问题:天线的谐振频率和设计。介绍了优化得到的设计参数值和优化方法的性能。结果表明,粒子群算法和FOA算法都能正确地找到尺寸。结果表明,FOA的灵敏度随着果蝇种群的增加而增加,收敛速度加快。研究结果表明,对于所提出的天线,粒子群算法比FOA算法具有更好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization and comparative analysis of quarter-circular slotted microstrip patch antenna using particle swarm and fruit fly algorithms
This paper proposes a parametric study of modified rectangular microstrip antenna in the frequency range between 1.4-2.65 GHz for wireless communication applications, incorporating with optimization methods Particle Swarm Optimization (PSO) and Fruit Fly Optimization (FOA). To design an antenna using optimization methods a fitness function of required parameters is needed. The resonance frequency of Microstrip Patch Antennas (MPAs) depends on various parameters and a standard frequency function does not exist for MPAs. In this study, a rectangular patch antenna is designed for the required resonance frequency and modified with circular quarter slots. The frequency-shift with the change of design variables, which are the substrate thickness and the radius of the slots, is observed. The resonance frequency is obtained as a function of the design variables and it is used in the optimization process to minimize the difference between the target frequency and the calculated one. The original algorithms FOA and PSO have been adapted for its application to a modified rectangular patch antenna design problem: resonance frequency and design of antenna. The design parameter values obtained via optimization and the performances of the optimization methods are presented. The results showed that both PSO and FOA find the dimensions correctly. It is also observed that the sensitivity of the FOA increases with the fruit fly population and the convergence gets faster. The outcomes of this paper show that the PSO algorithm gives better results when compared to the FOA for the proposed antenna.
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