典型文献
Multi-fidelity Bayesian algorithm for antenna optimization
文献摘要：
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 func-tion, 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 subse-quently 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 tradi-tional algorithms, the MFBO performs more effectively than the HFBO, and sometimes a superior optimization result can be achieved by reusing the LF simulation data.
文献关键词：
中图分类号：
作者姓名：
LI Jianxing;YANG An;TIAN Chunming;YE Le;CHEN Badong
作者机构：
School of Information and Communications Engineering,Xi'an Jiaotong University,Xi'an 710049,China;Institute of Microelectronics,Peking University,Beijing 100871,China;Institute of Artificial Intelligence and Robotics,Xi'an Jiaotong University,Xi'an 710049,China
文献出处：
引用格式：
[1]LI Jianxing;YANG An;TIAN Chunming;YE Le;CHEN Badong-.Multi-fidelity Bayesian algorithm for antenna optimization)[J].系统工程与电子技术（英文版）,2022(06):1119-1126
A类：
MFBO,HFBO
B类：
Multi,fidelity,Bayesian,optimization,In,this,work,simulation,driven,its,advanced,proposed,optimize,antennas,Firstly,multiple,objective,targets,constraints,fused,into,one,comprehensive,func,which,facilitates,end,way,Then,increase,efficiency,surrogate,construction,we,model,introduced,theory,output,Gaussian,process,To,further,low,LF,data,modified,subse,quently,By,picking,most,potential,points,from,simulating,them,high,has,possibility,obtain,better,result,negligible,overhead,compared,Finally,two,testify,algorithms,It,shows,that,outperforms,tradi,tional,more,effectively,than,sometimes,superior,can,achieved,by,reusing
AB值：
0.483227
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