A Hybrid Algorithm Based on NSGA-II and MOPSO for Multi-objective Designs of Electromagnetic Devices

被引：1
|
作者：
Li Yilun [1 ]
Xie Zhengwei [1 ]
Yang Shiyou [2 ]
Ren Zhuoxiang [3 ]
机构：
[1] Donghua Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
[2] Zhejiang Univ, Coll Elect Engn, Hangzhou, Peoples R China
[3] Sorbonne Univ, Grp Elect & Elect Engn Paris, Paris, France
来源：
TWENTIETH BIENNIAL IEEE CONFERENCE ON ELECTROMAGNETIC FIELD COMPUTATION (IEEE CEFC 2022) | 2022年
关键词：
Multi-objective optimization; electromagnetic optimization; evolutionary algorithms; TEAM; 22; benchmark;
D O I：
10.1109/CEFC55061.2022.9940652
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, a hybrid algorithm is proposed by combining the NSGA-II with MOPSO algorithm. The original NSGA-II is improved by adopting dynamical selection of crossover and mutation operators. The performance of the proposed hybrid algorithm is verified using standard test functions and it is applied to the multi-objective optimization benchmark problem TEAM 22. Numerical results demonstrate the effectiveness and superiority of the proposed hybrid algorithm.
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