Constrained Surrogates and Dimensionality Reduction for Low-Cost Multi-Objective Optimization of Compact Microwave Components

This paper addresses the problem of expedited surrogate-assisted multi-objective optimization of compact microwave passives. Our methodology adopts the concept of constrained modeling, enhanced by a reduction of the parameter space dimensionality. The latter is realized through the spectral analysis of the supplementary reference design set used to estimate the geometry of the Pareto front. As demonstrated using a 15-parameter impedance transformer, the combination of these mechanisms permits identification of the Pareto set at the cost of just a few hundred of EM circuit simulations. Benchmarking reveals considerable efficiency improvements achieved over the state-of-the-art surrogate-based MO algorithms.