Shuffled Frog Leaping Algorithm for Multi-objective Design Optimization of Transverse Flux Linear Motor

Abstract

This article presents a multi-objective design optimization of the transverse flux linear motor with an inner mover using the shuffled frog leaping algorithm. The optimization target is to reduce the transverse flux linear motor weight while maximizing its thrust force as well as minimizing its detent force. The design variables of the optimization problem are the stator pole length, air-gap length, winding window width, and stator pole width, where these variables affect the motor characteristics. The response surface methodology was used to obtain a mathematical model of the motor weight, detent force, and thrust force in terms of the design variables. Finite-element computations were used for numerical experiments on the geometrical design variables to determine the coefficients of a second-order analytical model for the response surface methodology. The finite-element analysis model is verified with the experimental results of a scaled-down model of the transverse flux linear motor. The effectiveness of the shuffled frog leaping algorithm model is compared with that of the genetic algorithm model and particle swarm optimization model. With the proposed shuffled frog leaping algorithm technique, the weight of the initially designed transverse flux linear motor and its detent force is reduced and its thrust force is increased.