Design optimization of permanent magnet motors using response surface methodology and genetic algorithms

Abstract

This paper describes work done on optimization of design of permanent magnet motors using response surface modeling and genetic algorithms (GAs). Finite element computations have been used for numerical experiments on geometrical design variables in order to evaluate the coefficients of a second-order model for the response surfaces representing machine parameters (/spl lambda//sub m/, L/sub d/, and L/sub q/). GAs were used to optimize the torque and speed of the machine in terms of these variables.