Expensive Highly Constrained Antenna Design Using Surrogate-Assisted Evolutionary Optimization

Abstract

Antenna structure design constitutes a computationally expensive optimization problem due to the requirement for full-wave electromagnetic (EM) simulations. Surrogate-assisted evolutionary algorithms offer a promising approach for addressing such challenges. However, several challenges remain in solving expensive, highly constrained antenna design problems. This paper introduces a surrogate-assisted dynamic constrained multi-objective evolutionary algorithm framework to tackle expensive and highly constrained antenna design optimization tasks. A multi-layer perceptron (MLP) is employed as the surrogate model to approximate EM evaluations and alleviate the computational burden, while a dynamic scale-constrained boundary strategy is implemented to handle highly constraints. The effectiveness of the proposed method is validated on a set of constrained benchmark problems and two antenna design cases.