Improvement Parameters for Design Brushless DC Motor by Moth Flame Optimization

Abstract

This contribution deals with an improved 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; losses, construction cost and the volume of the motor which should be minimized simultaneously. The three algorithms are Moth Flame, Genetic and Particle Swarm have been studied in this paper. It is noteworthy that Moth flame optimization (MFO) algorithm has been used for the first time for brushless DC motor design optimization. A comparative study between the mentioned optimization approaches shows that moth flame optimization algorithm has been converged to optimal response in less than 250 iterations and its standard deviation is ± 0.03, while the convergence rate of the genetic and particle swarm algorithms are about 400 and 450 iterations with standard deviations of ± 0.07 and ± 0.06, respectively for the case study motor. The obtained results show the best performance for the Moth Flame Optimization algorithm among all mentioned algorithms in brushless DC motor design optimization.