BNN-LSTM-DE Surrogate Model–Assisted Antenna Optimization Method Based on Data Selection

Abstract

The use of surrogate models in assisting evolutionary algorithms for antenna optimization has achieved significant research outcomes. The construction of surrogate model primarily depends on two aspects; one is the selection of datasets, and the other is the model’s structure and performance. This paper proposes a novel dataset selection method aimed at enhancing the performance of the constructed surrogate model. Additionally, based on Bayesian neural network (BNN) and leveraging the advantages of handling sequence data with long short-term memory (LSTM), a BNN-LSTM surrogate model is introduced. After training, this surrogate model is used as the fitness evaluation function, enabling optimization design based on differential evolution (DE) algorithm. Experimental validations are conducted using the optimizations of a dual-frequency slotted patch antenna and a rectangular cut-corner ultrawideband antenna as examples. Results demonstrate that the proposed surrogate model exhibits high accuracy, providing a guidance for antenna optimization.