Electrical Engineering, Mah Taab Caspian Electricity Generation Company, N
Department of Electrical Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
Department of Electrical Engineering, Sari university
This contribution deals with an optimal design of a brushless DC motor, using optimization algorithms, based on collective intelligence. For this purpose, the case study motor is perfectly explained and its significant specifications are obtained as functions of the motor geometric parameters. In fact, the geometric parameters of the motor are considered as optimization variables. Then, the objective function has been defined. This function consists of three terms i.e. losses, construction cost and the volume of the motor which should be minimized simultaneously. Three algorithms i.e. cuckoo, genetic and particle swarm have been studied in this paper. It is noteworthy that, cuckoo optimization algorithm has been used for the first time for brushless DC motor design optimization. A comparative study between the mentioned optimization approaches shows that, cuckoo optimization algorithm has been converged to optimal response in less than 250 iterations and its standard deviation is , while the convergence rate of the genetic and particle swarm algorithms are about 400 and 450 with standard deviations of and , respectively for the case study motor. The obtained results show the best performance for cuckoo optimization algorithm among all mentioned algorithms in brushless DC motor design optimization.