An Unsupervised Microwave Filter Design Optimization Method Based on a Hybrid Surrogate Model-Assisted Evolutionary Algorithm
Liyuan Xue, Bo Liu, Yang Yu, Qingsha S. Cheng, Muhammad Ali Imran, Tianrui Qiao
Abstract
In resonator-coupled bandpass filter 3D design, it is a routine that the filter optimization methods are guided/supervised by designers’ experience to carry out an iterative design optimization process. To realize automated or unsupervised filter 3D design optimization, a new method, called hybrid surrogate model-assisted evolutionary algorithm for filter optimization (H-SMEAFO), is proposed. H-SMEAFO aims to automatically obtain a highly optimal filter 3D design without designers’ interaction (i.e., unsupervised) and is also not restricted to certain kinds of filter structures. In H-SMEAFO, the key innovations include a hybrid response feature-based objective function and a hybrid surrogate model-assisted global optimization algorithm; both are designed bespoke for filter design landscape characteristics. The performance of H-SMEAFO is demonstrated by an 8th-order dual-band waveguide filter with four transmission zeros and a 6th-order waveguide filter with two transmission zeros, for which, unsupervised design optimization does not appear to be possible using existing methods. Numerical results show the effectiveness and advantages of H-SMEAFO.