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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2021, Vol. 42 ›› Issue (4): 525060-525060.doi: 10.7527/S1000-6893.2021.25060

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

Mode pursuing sampling intelligent exploring method considering expensive constraints

LONG Teng1,2, MAO Nengfeng1, SHI Renhe3, WU Yufei1, SHEN Dunliang4   

  1. 1. School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China;
    2. Key Laboratory of Dynamics and Control of Flight Vehicle of Ministry of Education, Beijing Institute of Technology, Beijing 100081, China;
    3. School of Aerospace Engineering, Tsinghua University, Beijing 100084, China;
    4. Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China
  • Received:2020-12-07 Revised:2020-12-30 Published:2021-02-02
  • Supported by:
    National Natural Science Foundation of China (51675047, 52005288); Aeronautical Science Foundation of China (2019ZC072003)

Abstract: 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.

Key words: mode pursuing sampling, intelligent exploring method, expensive constraints, approximate optimization, KS function, filter

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