Multi-objective Optimization Design of 8/6 Switched Reluctance Motor using GA and PSO Algorithms

Abstract

In this work we tried to find the optimal values of the geometric parameters of a switched reluctance machine (SRM) such as the stator and rotor pole arc and ratios of the yoke thickness that satisfied two objectives functions: (i) minimizing the magnetic losses, (ii) and increasing the average torque. The weighting method was used to transform the multi-objective optimization into a single-objective problem. The approach using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) allowed finding the compromise surface of Pareto. The finite element analysis (FEA) was performed by coupling MATLAB with FEMM package software.