Abstract: In order to solve the nonlinear constraint engineering optimization problem which requires high computational resource，a self-adaptive constraint optimization algorithm based on multiple surrogates is proposed. First a candidate set including surrogate model will be assigned to the objective function and each constraint function in the optimization problem, then based on the cross-check, the ranking of the corresponding surrogate model in the candidate models will be determined by the fitting performance of the function; The surrogate model is selected to construct approximate model of the original optimization problem according to the result of the ranking, solved by the sequential quadratic programming algorithm. When the number of surrogate models is different in the candidate set, the priority is selected. The surrogate model will be kept or deleted in the candidate set is determined by the evaluation results of the fitting performance of the function, and the new sample in the algorithm is got by solving the approximate model and the combination of the application of the inhomogeneous variation operator and the hybrid operator. Finally application of the proposed constraint optimization algorithm to solve four representative mathematical optimization problems, the results show that the approximate optimization solutions based on the adaptive optimization algorithm are better approximated to the theoretical optimal solution; And used to optimize the design of the rivet head forming surface in the shaft clinching process of the vehicle wheel bearing unit, the optimization result is better.

Key words: multiple surrogates, self-adaptive, constraint optimization, random ranking, genetic operator