Low‐cost multi‐criteria design optimization of compact microwave passives using constrained surrogates and dimensionality reduction

Abstract

Design of contemporary microwave circuits is a challenging task. Typically, it has to take into account several performance requirements and constraints.The design objectives are often conflicting and their simultaneous improvement may not be possible; instead, compromise solutions are to be sought. Representative examples are miniaturized microwave passives where reduction of the circuit size has a detrimental effect on its electrical characteristics. Acquiring information about the best possible design trade-offs is invaluable for the designer, yet it entails computationally expensive multi-objective optimization (MO). MO is typically conducted using population-based metaheuristic algorithms, the cost of which might be extremely high. If the circuit performance is evaluated using full-wave electromagnetic (EM) analysis, this cost is often prohibitive. A workaround is the employment of fast surrogate models, and a number of surrogate-assisted frameworks have been proposed in the literature. Unfortunately, a construction of reliable surrogates is hindered in higher dimensional parameter spaces. The recently proposed constrained modeling mitigates this issue to a certain extent by restricting the modeling process to the region containing the Pareto front to be found. This work proposes a novel surrogate-based MO technique that involves constrained modeling and explicit reduction of the surrogate domain dimensionality. The latter is achieved through the spectral analysis of the extreme Pareto-optimal design set obtained by local search routines. Our methodology is validated using a 15-parameter impedance-matching transformer with the Pareto set identified at the cost of a few hundred EM analyses of the circuit. The numerical experiments also demonstrate a significant reduction of the optimization cost as compared to the state-of-the-art surrogate-assisted MO methods.

A machine learning-based comparative analysis of surrogate models for design optimisation in computational fluid dynamics

“…Surrogate modelling techniques [ [29] , [30] , [31] , [32] , [33] ] have gained popularity in optimising engineering design problems, with researchers focusing on different applications. Koziel and Pietrenko-Dabrowska [ 34 , 35 ] contribute to this field by addressing the challenges of designing and optimising high-frequency systems, especially antennas and microwave circuits.…”

A novel near field radiation shaping technique by using data driven surrogate based optimization for nondestructive hyperthermia

“…A viable and efficient solution for this problem is the usage of data driven surrogate models 19–23 . Application of data driven surrogate models for repetitive optimization process is a well‐known technique which is being studied for many different type of microwave and RF research topics such as; modeling of RF small signal microwave transistors, 24–28 design and optimization of passive microwave stages such as filters, 29–42 Meta surfaces, 43–45 and microwave antenna designs 46–57 …”

“…A viable and efficient solution for this problem is the usage of data driven surrogate models. [19][20][21][22][23] Application of data driven surrogate models for repetitive optimization process is a well-known technique which is being studied for many different type of microwave and RF research topics such as; modeling of RF small signal microwave transistors, [24][25][26][27][28] design and optimization of passive microwave stages such as filters, [29][30][31][32][33][34][35][36][37][38][39][40][41][42] Meta surfaces, [43][44][45] and microwave antenna designs. [46][47][48][49][50][51][52][53][54][55][56][57] In this work, in order to achieve an efficient optimization process for focusing the EM energy into the tumor location, a data driven surrogate model of antenna array, which its data samples are generated using ARM method, is taken into the consideration.…”