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Article

Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms

by
Mehmet Çunkaş
Department of Electronics and Computer Education, Selçuk University, 42003, Konya, Turkey
Math. Comput. Appl. 2008, 13(3), 153-163; https://doi.org/10.3390/mca13030153
Published: 1 December 2008
Versions Notes

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.
Keywords: Multiobjective Fuzzy Optimization; Submersible Induction motor; Genetic Algorithms Multiobjective Fuzzy Optimization; Submersible Induction motor; Genetic Algorithms

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MDPI and ACS Style

Çunkaş, M. Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms. Math. Comput. Appl. 2008, 13, 153-163. https://doi.org/10.3390/mca13030153

AMA Style

Çunkaş M. Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms. Mathematical and Computational Applications. 2008; 13(3):153-163. https://doi.org/10.3390/mca13030153

Chicago/Turabian Style

Çunkaş, Mehmet. 2008. "Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms" Mathematical and Computational Applications 13, no. 3: 153-163. https://doi.org/10.3390/mca13030153

APA Style

Çunkaş, M. (2008). Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms. Mathematical and Computational Applications, 13(3), 153-163. https://doi.org/10.3390/mca13030153

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