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

被引：3
|
作者：
Li, Yilun [1 ]
Xie, Zhengwei [1 ]
Yang, Shiyou [2 ]
Ren, Zhuoxiang [3 ]
机构：
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China
[2] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China
[3] Sorbonne Univ, Grp Elect & Elect Engn Paris, CNRS, F-75005 Paris, France
来源：
IEEE TRANSACTIONS ON MAGNETICS | 2023年 / 59卷 / 05期
关键词：
Evolutionary algorithm; inverse problem; multi-objective optimization (MOO); TEAM; 22; benchmark; OPTIMIZATION;
D O I：
10.1109/TMAG.2023.3250319
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this article, a hybrid algorithm is proposed by combining the non-dominated sorting genetic algorithm (NSGA-II) with multi-objective particle swarm optimization (MOPSO) algorithm. The original NSGA-II is improved by using logistic mapping initialization and a dynamic selection mechanism of crossover and mutation operators is proposed. The performance of the proposed hybrid algorithm is verified using standard test functions and it is applied to the multi-objective optimization (MOO) benchmark problem TEAM 22. Numerical results demonstrate the effectiveness and superiority of the proposed hybrid algorithm.
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页数：4
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