An efficient hybrid sequential approximate optimization method for problems with computationally expensive objective and constraints

Abstract

To improve the computational cost and approximate optimization accuracy of solving engineering optimization problems with both expensive objective and constraints, a hybrid sequential approximate optimization (HSAO) method is proposed. First, the radial basis function neural network (RBFNN) and response surface method (RSM) approximate models fit the optimization objectives and constraints, respectively. Second, the fitted model searches for the optimal solution via global optimization algorithm, and then, the optimal solution is used as a newly added sample point for updating approximation model. The region of the optimal solution is iteratively updated until the optimal solution converges. The performance of the HSAO method is tested on numerical test functions. Compared with the traditional approximate model, adaptive RSM, and adaptive RBFNN approximate optimization, the results indicate that the proposed HSAO method is higher in search ability and efficiency. Finally, the method is applied in S-shaped tube crashworthiness optimization, and has achieved desired results.