BBO和PSO算法在Yagi-Uda天线设计优化中的演化性能

S. Singh, S. Tayal, G. Sachdeva
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引用次数: 11

摘要

粒子群优化(PSO)和基于生物地理的优化(BBO)是最流行的基于群体的优化算法,它们比其他进化算法(ea)表现出令人印象深刻的性能。Yagi-Uda天线具有高增益、低成本和结构简单等优点,是高频和超高频应用最广泛的天线设计之一。设计Yagi-Uda天线需要确定线元长度和它们之间的间距,这些长度和间距与天线增益、阻抗和特定工作频率下的单瓣电平(SLL)具有高度复杂的非线性关系。为了更快地优化天线设计以获得最大增益,本文对粒子群算法和BBO算法进行了比较研究。最后给出了最佳天线设计,并绘制了BBO、PSO及其组合迭代性能的10次蒙特卡罗模拟的平均值。
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Evolutionary performance of BBO and PSO algorithms for Yagi-Uda antenna design optimization
Particle Swarm Optimization (PSO) and Biogeography Based Optimization (BBO) are most popular swarm based optimization algorithms those have shown impressive performance over other Evolutionary Algorithms (EAs). Yagi-Uda is one of most widely antenna designs used at High Frequency (HF) and Ultra High Frequency (UHF) due its high gain, low cost and constructional ease. Designing a Yagi-Uda antenna involves determination of wire-element lengths and their spacings in between them those bear highly complex and non-linear relationships with antenna gain, impedance and Single Lobe Level (SLL) at a particular frequency of operation. In this paper, a comparative study between PSO and BBO is presented for faster optimization of antenna designs for maximum gain. The best antenna designs are tabulated and average of 10 monte-carlo simulations are plotted for BBO, PSO and their combinational iterative performances in the ending sections.
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