Multiobjective design optimization of electric machine by using genetic algorithm with aggressive species diversity

Yusuke Tsurumi, Shinji Wakao

研究成果: Conference contribution

抄録

In the design optimization of electric machine, there is a strong need to comprehend in detail the tradeoff relationships among the various objective functions. Therefore, it is important to obtain the sufficiently diverse pareto solutions for appropriately designing electric machine. However, the conventional genetic algorithm (GA) doesn't necessarily find out the diverse pareto solutions. In this paper, we propose a GA with new concept of crowding distance which enables us to obtain the sufficiently diverse pareto solution. Some numerical examples which demonstrate the validity of the proposed method is presented.

本文言語English
ホスト出版物のタイトルDigests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2010
DOI
出版ステータスPublished - 2010 7 26
イベント14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC2010 - Chicago, IL, United States
継続期間: 2010 5 92010 5 12

出版物シリーズ

名前Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2010

Conference

Conference14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC2010
国/地域United States
CityChicago, IL
Period10/5/910/5/12

ASJC Scopus subject areas

  • 計算理論と計算数学
  • 電子工学および電気工学

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