Journal of Intelligent Manufacturing

, Volume 21, Issue 4, pp 393–402 | Cite as

Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

  • Mehmet Çunkaş


In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor’s performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors.


Multiobjective fuzzy optimization Genetic algorithms Induction motor 


A1m, Ab

Cross-sectional area of stator and rotor conductor, respectively

Ar, Ag

Cross-sectional area of end-ring and air-gap, respectively


Cost of unit weight of copper


Stator diameter at centers of stator slots


Stator outer diameter


Rotor diameter


Cost of unit weight of iron


End winding factor

L1, L2

Axial length of stator and rotor, respectively


Number of phase


Density of the iron sheet

psw, prw

Density of stator and rotor conductors, respectively


Total copper losses of stator and rotor


Total iron losses




Stacking factor

S1, S2

Number of stator and rotor slot, respectively

wa, wr

Rotor end rings axial and radial width, respectively


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Electronics and Computer EducationSelçuk UniversityKonyaTurkey

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