Multilayer Machine Learning-Assisted Optimization-Based Robust Design
and Its Applications to Antennas and Arrays
Abstract
An efficient multilayer machine learning-assisted optimization
(ML-MLAO)-based robust design method is proposed for antenna and array
applications. Machine learning methods are introduced into multiple
layers of the robust design process, including worst-case analysis
(WCA), maximum input tolerance hypervolume (MITH) searching, and robust
optimization, considerably accelerating the whole robust design process.
First, based on a surrogate model mapping between the design parameters
and performance, WCA is performed using a genetic algorithm to ensure
reliability. MITH searching is then carried out using a double-layer
MLAO (DL-MLAO) framework to find the MITH of the given design point.
Next, based on the training set obtained using DL-MLAO, correlations
between the design parameters and the MITH are learned. The robust
design is carried out using surrogate models for both the performance
and the MITH, and these models are updated online following the ML-MLAO
scheme. Furthermore, two examples, including an array synthesis problem
and an antenna design problem, are used to verify the proposed ML-MLAO
method. Finally, the numerical results and computation time are
discussed to demonstrate the effectiveness of the proposed method.