Strategy and Parameter Selection for Motor Optimization Based on Quantum Genetic Algorithm

When the quantum genetic algorithm is applied to optimization design of motor structure, it is necessary to deal with the relative strategies and parameters properly, which needs more user′s experience. In this study, taking a permanent magnet brushless DC double-rotor motor with an outer rotating magnetic system for example, the selection of rotation angle adjusting strategy and convergence criterion is discussed, the effects of different quantum crossover probabilities and quantum mutation probabilities on optimization result are analyzed, and the setting idea of population size and number of optimization variables is introduced. As a result, a scheme for applying quantum genetic algorithm to motor structure optimization is obtained. This scheme can also be adopted for solving other similar problems