基于Harris Hawks优化算法的介质波导滤波器优化设计

Harris Hawks优化(Harris Hawks optimization, HHO)算法是一种模拟鸟群合作捕食行为的新型群智能算法. 介质波导滤波器是当前5G移动通信设备急需的器件，因此如何利用新型优化算法高效且精确地对介质波导滤波器进行优化设计十分重要. 文中首先描述了HHO算法流程，并结合滤波器优化问题提出了一种通用框架；然后基于稳态假设对HHO算法的更新方程进行了理论分析，依据所导出的方程分析了算法的动态特性及收敛行为；最后利用HHO算法实现了两款介质波导滤波器的优化设计. 为验证算法性能，将本文算法与三个著名的群智能算法进行比较. 实验结果表明，HHO算法的收敛速度、效率和精度都明显优于目前业内主流应用的自适应差分进化算法、花粉授粉优化算法和灰狼优化算法.

Design of dielectric waveguide filters using Harris Hawks optimization

Harris Hawks optimization (HHO) is a novel swarm intelligence optimization algorithm, which simulates the cooperative behavior of birds in nature. Dielectric waveguide filters are an urgently needed device for current 5G mobile communication equipment. Therefore, it is important to design and optimize dielectric waveguide filters efficiently and accurately by using the new algorithm optization. In this paper, HHO algorithm is first described, and a general framework combined with filter optimization problems is proposed. Then, the updating equations of HHO is derived based on the steady-state assumption. According to these equations, both dynamic characteristics and convergence behavior of HHO are analyzed. Finally, the HHO algorithm is used to design two dielectric waveguide filters to improve the convergence speed, efficiency and accuracy of filter design. To demonstrate the efficiency, HHO is compared with other three famous optimization techniques. The experimental results show that the HHO algorithm are significantly better in the convergence speed, efficiency and accuracy than the self-adaptive differential evolution (SADE), flower pollination algorithm (FPA) and gray wolf optimizer (GWO).