Error-based global optimization approach for electric motor design

This paper proposes a new global optimization approach for electric machine design problems. The algorithm uses an error function between the actual objective function, at a given computational step, and its limit value to search for the global minimum. The error function provides a good indication of how far or close the objective function approaches its ultimate solution. This method is considered to be self adaptive in a sense that the higher the error is, the larger the step size of the variables will be. This feature helps the search to escape local solutions and to reach eventually the global solution. The proposed method was tested on various typical multimodal functions and particularly applied to induction motor design optimization problems. This algorithm is very simple and easy to be implemented, yet powerful and effective as indicated by the test results