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.