考虑高耗时约束的追峰采样智能探索方法

针对现代飞行器设计等工程优化问题中面临的约束高耗时难题，在标准追峰采样（MPS）方法的基础上，提出了一种基于过滤器的MPS-DCP设计空间智能探索方法（FMPS-DCP），训练径向基函数网络预示高耗时目标函数与约束条件响应，利用KS方程聚合高耗时约束并根据过滤器思想筛选优质简单样本点，定制了一套新增样本点选择策略引导优化过程快速向全局可行最优解收敛，从而提高了求解高耗时约束优化问题的效率。采用一组标准约束测试算例验证FMPS-DCP方法的性能，并与CiMPS、Extended ConstrLMSRBF、ARSM-ISES和KRG-CDE智能探索方法进行对比。结果表明，FMPS-DCP在优化效率与鲁棒性方面具有显著的性能优势。最后，通过全电推进卫星平台多学科设计优化案例，验证了FMPS-DCP的工程实用性。

Mode pursuing sampling intelligent exploring method considering expensive constraints

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.