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Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms
Department of Electronics and Computer Education, Selçuk University, 42003, Konya, Turkey
Published: 1 December 2008
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.
This is an open access article distributed under the Creative Commons Attribution License
(CC BY 3.0).
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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.
Çunkaş M. Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms. Mathematical and Computational Applications. 2008; 13(3):153-163.
Çunkaş, Mehmet. 2008. "Design Optimization of Electric Motors by Multiobjective Fuzzy Genetic Algorithms." Math. Comput. Appl. 13, no. 3: 153-163.
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