［Purposes］To propose a multiple surrogates assisted evolutionary algorithm for solving expensive black-box optimization problems. ［Methods］The ESAO algorithm is improved by performing 10 generations of evolutionary operations in global search to reduce the instability of the solution, and using the adaptive distance criterion to judge the conversion between the global search and the local search, so as to improve the accuracy of the solution. ［Findings］A new multiple surrogates assisted evolutionary algorithm for expensive black-box optimization problems is obtained. ［Conclusions］The numerical results of the new algorithm are evaluated by using 22 test problems, and the results show that the new algorithm has significant advantages over the ESAO algorithm.