Recent Advances in Surrogate-Assisted Optimization Techniques Using Feature and Feature Sensitivity for Microwave Filter Design

In electromagnetic (EM) optimization for microwave circuits, a poorly chosen initial point can easily lead to falling into local optima that fail to satisfy design specifications. This paper presents two recent EM optimization techniques utilizing features and feature sensitivities to address this challenge. The first technique extracts features and their sensitivities in advance to build a multifeature surrogate model, enhancing optimization efficiency. The second technique calculates feature and their sensitivities by constructing the neuro-transfer function (TF) surrogate model to accelerate gradient-based optimization. Each technique is illustrated with a microwave filter example.