Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Networks

Designing microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when executed with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective MO technique for microwave passive components utilizing a machine learning (ML) framework with artificial neural network (ANN) surrogates as the primary prediction tool. In this approach, multiple candidate solutions are extracted from the Pareto set via optimization using a multi-objective evolutionary algorithm (MOEA) applied to the current ANN model. These solutions expand the dataset of available (EM-simulated) parameter vectors and refine the surrogate model iteratively. To enhance computational efficiency, we employ variable-resolution EM models. Tested on two microstrip circuits, our methodology competes effectively against several surrogate-based approaches. The average computational cost of the algorithm is below three hundred EM analyses of the circuit, with the quality of generated Pareto sets surpassing those produced by the benchmark methods.