Hybrid multi-objective optimization algorithm for PM motor design

This paper proposes an innovative hybrid multi-objective (MO), fast converging optimization algorithm, enabling global optimum tracking in permanent magnet traction motor design. The methodology developed is based on Artificial Bee Colony (ABC) technique, strength pareto evolutionary algorithm (SPEA2) and Differential Evolution (DE) strategy ensuring fast and reliable convergence to the optimal Pareto front. An application example concerning an unequal teeth surface mounted permanent magnet wheel motor design is given.