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

Yusuke Tsurumi, Shinji Wakao

    研究成果: Conference contribution

    抜粋

    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.

    元の言語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
    出版物ステータスPublished - 2010
    イベント14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC2010 - Chicago, IL
    継続期間: 2010 5 92010 5 12

    Other

    Other14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC2010
    Chicago, IL
    期間10/5/910/5/12

      フィンガープリント

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Electrical and Electronic Engineering

    これを引用

    Tsurumi, Y., & Wakao, S. (2010). Multiobjective design optimization of electric machine by using genetic algorithm with aggressive species diversity. : Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2010 [5481748] https://doi.org/10.1109/CEFC.2010.5481748