Multi-fidelity Bayesian algorithm for antenna optimization

In this work, the multi-fidelity (MF) simulation driven Bayesian optimization (BO) and its advanced form are proposed to optimize antennas. Firstly, the multiple objective targets and the constraints are fused into one comprehensive objective function, which facilitates an end-to-end way for optimization. Then, to increase the efficiency of surrogate construction, we propose the MF simulation-based BO (MFBO), of which the surrogate model using MF simulation is introduced based on the theory of multi-output Gaussian process. To further use the low-fidelity (LF) simulation data, the modified MFBO (M-MFBO) is subsequently proposed. By picking out the most potential points from the LF simulation data and re-simulating them in a high-fidelity (HF) way, the M-MFBO has a possibility to obtain a better result with negligible overhead compared to the MFBO. Finally, two antennas are used to testify the proposed algorithms. It shows that the HF simulation-based BO (HFBO) outperforms the traditional algorithms, the MFBO performs more effectively than the HFBO, and sometimes a superior optimization result can be achieved by reusing the LF simulation data.