A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs

被引:3
|
作者
Xie, Zhengwei [1 ]
Li, Yilun [1 ]
Yang, Shiyou [2 ]
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
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 23期
关键词
multi-objective optimization; inverse problem; evolutionary algorithm; adaptive operator; BIOGEOGRAPHY-BASED OPTIMIZATION; PARAMETER; ALGORITHM;
D O I
10.3390/app122312110
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Optimization algorithms play a critical role in electromagnetic device designs due to the ever-increasing technological and economical competition. Although evolutionary algorithm-based methods have successfully been applied to different design problems, these methods exhibit deficiencies when solving complex problems with multimodal and discontinuous objective functions, which is quite common in electromagnetic device optimization designs. In this paper, a hybrid multi-objective optimization algorithm based on a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective particle swarm optimization method (MOPSO) is proposed. In order to enhance the convergence and diversity performance of the algorithm, a new population update mechanism of MOPSO is introduced. Moreover, an adaptive operator involving crossover and mutation is presented to achieve a better balance between global and local searches. The performance of the hybrid algorithm is validated using standard test functions and the multi-objective design of a superconducting magnetic energy storage (SMES) device. Numerical results demonstrate the effectiveness and superiority of the proposed method.
引用
收藏
页数:14
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