考虑高耗时约束的追峰采样智能探索方法

The engineering optimization practices such as modern flight vehicle design often encounter expensive constraints. Based on the standard Mode Pursuing Sampling (MPS) method, a Filter-based Mode Pursuing Sampling intelligent exploring method using Discriminative Coordinate Perturbation (FMPS-DCP) is proposed in this work for constrained optimization problems. In this work, the radial based function network is trained for predicting the values of expansive objective function and constraint functions, and KS function is used to aggregate constraints. Then a filter is constructed for deciding whether to accept sampling points, and a sample point selection strategy is designed to lead the algorithm converge to global feasible optimal value rapidly. FMPS-DCP is tested on a number of standard numerical benchmark problems and compared with CiMPS, Extended ConstrLMSRBF, ARSM-ISES and KRG-CDE. The optimization results indicate that the optimization efficiency of FMPS-DCP is higher than others with lower standard deviation for multiple runs. Finally, the practicality of FMPS-DCP is demonstrated by an all-electric propulsion satellite platform multidisciplinary design optimization problem.