Multi-objective Quantum Evolutionary Algorithm And Its Application To Brushless DC Motor

Permanent magnet brushless DC motor has been currently used in aerospace,military,industrial automation control,machinery,medical equipment,machine tools and other industries.At present,it becomes a new trend to study,to develop,and to produce Brushless DC motor.Big data analysis shows that the average annual carbon dioxide emissions will be reduced by hundreds of millions of tons if all the internal combustion engines are replaced with high efficiency motors.Therefore,high efficiency motor design is in line with the national policies of energy-saving and the requirements of building a conservation-minded society.Experimental results show that Real-Coded Quantum-inspiried Evolutionary Algorithm(RCQEA)converges slowly in the early stage.It is because large amounts of random individuals in the population at the beginning will weaken their ability to guide the optimization direction.To solve this problem,this paper adopts eigen-rectangle encoding to effectively improve the flexibility of the individuals.A novel evolutionary algorithm,eigen-rectangle real-coded quantum-inspired evolutionary algorithm(ERCQEA),is proposed with the novel coding method and accordingly improved update mechanism.The proposed encoding method diversifies eigen-rectangle genes and updates quantum population with direction rather than changes the quantum genes homogenously as in conventional algorithm.With these improvements,it enables ERCQEA to outperform conventional algorithm.A large number of benchmark functions are used to verify the effectiveness of the algorithm.The comparison results show that the improved algorithm converges faster than other algorithms with smaller population size and less generations.Based on the excellent performance of the algorithm,ERCQEA is then extended to multi-objective eigen-rectangle quantum-inspired algorithm(MERCQEA).Benchmark testing Results show that MERCQEA have strong ability both in convergence and global search.The optimization problem of motor is a nonlinear constrained and mixed-discrete multiple variables optimization problem.The design scheme of motor is demanded to achieve better performance in accordance with the requirements of the national standards and user’s attention.In this paper,MERCQEA is applied to the optimization problem of Brushless DC motor.the optimization model is established with two objective functions,five optimization variables and a series of constraints.Among them,motor efficiency and motor weight are selected as the objective functions;stator diameter,magnetic flux density of different parts and current density are selected as optimization variables.The penalty function is used in this paper to deal with the constraints by forming augmented objective functions.The optimized design schemes improve the motor efficiency and reduce its weight without violating the constraints.