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Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 01 Dec 2008 English Publisher:MDPI AGJournal:Mathematical and Computational Applications, volume 13, pages 153-163 (eissn: 2297-8747, Copyright policy )
Authors: Çunkaş M.;

doi: 10.3390/mca13030153

handle: 20.500.12395/22855

Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms

Abstract

This paper presents a multiobjective fuzzy genetic algorithm optimization approach to design the submersible induction motor with two objective functions: the full load torque and the manufacturing cost. A multiobjective fuzzy optimization problem is formulated and solved using a genetic algorithm. The optimally designed motor is compared with an industrial motor having the same ratings. The results of optimal design show the reduction in the manufacturing cost, and the improvement in the full load torque of the motor.

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Keywords

Genetic Algorithms, Multiobjective Fuzzy Optimization, Submersible Induction motor

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Average
Top 10%
Average
gold
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