Abstract: Three kinds of training algorithms for efficient global optimization(EGO) method are investigated.A kind of training algorithm based on low-discrepancy sequences is proposed to reduce randomness of EGO method.Performance of EGO method depends on a good training algorithm.Since training problems in EGO are non-convex and non-smooth,meta-heuristic algorithms,random algorithm and low-discrepancy sequences are chosen to address five benchmark optimization problems and two aerodynamic shape optimization problems.In these problems,differential evolution algorithm was found the best in meta-heuristic algorithms.Training algorithm based on low-discrepancy sequences can effectively reduce randomness of EGO method and Faure sequence has the best performance.

Key words: computational fluid dynamic, aerodynamic shape optimization, Kriging model, global optimization