Random Class Optimization Algorithm To Optimize The Design Of Applied Research, In The Motor

This paper mainly presents the method of solving inverse electromagnetic problems, the improvement of the stochastic optimization algorithm and the optimal designing of outer-rotor permanent motor.Inverse electromagnetic problem has been paid attention in the designing of electromagnetic devices for long time. Because of the multimodal of the solution of the inverse problem, traditional deterministic optimization algorithm is often useless to solve this kind of problems. Due to its character of "uphill climbing", stochastic optimization algorithm is widely used to solve inverse problems. However, the weakness of the stochastic algorithm is the slow convergence speed. Therefore, how to improve the convergence speed of the stochastic algorithm is the main task in the research of electromagnetic inverse problems.This paper improves some commonly used stochastic optimization algorithms, such as Genetic Algorithm, Simulated Annealing algorithm and Tabu Search algorithm, and improved algorithms are verified by the standard mathematical functions. Then, improved algorithms are used to solve practical electromagnetic problems and the items of practical application of algorithms worthy of paying attention to are emphasized. Computed results show that improved algorithms not only obtain better results, but also are timesaving and more robust compared with original algorithms. Moreover, based on numerical solving the electromagnetic field by means of Finite Element Method, the Tabu Search algorithm is used to optimize the structure of the outer-rotor permanent motor. The optimization result is satisfying.