Hybrid genetic algorithm for electromagnetic topology optimization

Author

Im, Chang-Hwan ; Jung, Hyun-Kyo ; Kim, Yong-Joo

Author_Institution

Sch. of Electr. Eng. & Comput. Sci., Seoul Nat. Univ., South Korea

Volume

39

Issue

5

fYear

2003

Firstpage

2163

Lastpage

2169

Abstract

This paper proposes a hybrid genetic algorithm (GA) for electromagnetic topology optimization. A two-dimensional (2-D) encoding technique, which considers the geometrical topology, is first applied to electromagnetics. Then, a 2-D geographic crossover is used as the crossover operator. A novel local optimization algorithm, called the on/off sensitivity method, hybridized with the 2-D encoded GA, improves the convergence characteristics. The algorithm was verified by applying it to various case studies, and the results are presented herein.

Keywords

brushless DC motors; convergence; electromagnetic field theory; genetic algorithms; machine theory; magnetoencephalography; 2-D geographic crossover; brushless DC motor optimization; convergence characteristics; crossover operator; current source optimization problem; electromagnetic topology optimization; geometrical topology; hybrid genetic algorithm; local optimization algorithm; magnetoencephalography source localization problem; on/off sensitivity method; reduced cogging torque; two-dimensional encoding technique; Application software; Circuit topology; Convergence; Design optimization; Electromagnetic devices; Encoding; Genetic algorithms; Optimization methods; Sensitivity analysis; Two dimensional displays;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/TMAG.2003.817094

Filename

1233027