Minimal function calls approach with on-line learning and dynamic weighting for computationally intensive design optimization

Design/optimization processes requiring intensive finite-element computation can be made significantly more efficient, while preserving good accuracy, by combining the Response Surface Methodology with on-line learning and dynamic weighting. The paper presents such a new development and uses the multi-parameter design of a brushless pm motor to illustrate the approach.