Multistage Collaborative Machine Learning and its Application to Antenna Modeling and Optimization

Abstract

A multistage collaborative machine learning (MS-CoML) method that can be applied to efficient multiobjective antenna modeling and optimization is proposed. Machine learning methods, including single-output Gaussian process regression (SOGPR) and symmetric and asymmetric multioutput GPR (MOGPR) methods, are introduced to collaboratively build highly accurate multitask surrogate models for antennas. Variable-fidelity electromagnetic (EM) models are simulated, with their responses utilized to build separate MOGPR surrogate models. By combining the three machine-learning methods in a multistage framework, mappings between the same and different responses of the EM models with variable fidelity are learned, therein helping to substantially reduce the computational effort under a negligible loss of predictive power. Three antenna designs aiming at single-band, broadband, and multiband applications are selected as examples. And, for illustrating the applicability and superiority of the proposed MS-CoML method, a reference point-based multiobjective antenna optimization algorithm is used to optimize these three antennas. Simulation results show that using the MS-CoML method can significantly reduce the total optimization time without compromising modeling accuracy and optimized performance.