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

Yusuke Tsurumi; Shinji Wakao

doi:10.1109/CEFC.2010.5481748

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

Yusuke Tsurumi^*, Shinji Wakao

^*Corresponding author for this work

School of Advanced Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2010
DOIs	https://doi.org/10.1109/CEFC.2010.5481748
Publication status	Published - 2010 Jul 26
Event	14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC2010 - Chicago, IL, United States Duration: 2010 May 9 → 2010 May 12

Publication series

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

Conference

Conference	14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC2010
Country/Territory	United States
City	Chicago, IL
Period	10/5/9 → 10/5/12

ASJC Scopus subject areas

Computational Theory and Mathematics
Electrical and Electronic Engineering

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10.1109/CEFC.2010.5481748

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

Multiobjective design optimization of electric machine by using genetic algorithm with aggressive species diversity. / Tsurumi, Yusuke; Wakao, Shinji.
Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2010. 2010. 5481748 (Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tsurumi, Y & Wakao, S 2010, Multiobjective design optimization of electric machine by using genetic algorithm with aggressive species diversity. in Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2010., 5481748, Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2010, 14th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC2010, Chicago, IL, United States, 10/5/9. https://doi.org/10.1109/CEFC.2010.5481748

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

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

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