Mode pursuing sampling intelligent exploring method considering expensive constraints

LONG Teng; MAO Nengfeng; SHI Renhe; WU Yufei; SHEN Dunliang

doi:10.7527/S1000-6893.2021.25060

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 4: 525060 - 525060

DOI: https://doi.org/10.7527/S1000-6893.2021.25060

Solid Mechanics and Vehicle Conceptual Design

Mode pursuing sampling intelligent exploring method considering expensive constraints

LONG Teng ,
MAO Nengfeng ,
SHI Renhe ,
WU Yufei ,
SHEN Dunliang

Expand

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 date: 2020-12-07

Revised date: 2020-12-30

Online published: 2021-02-02

Supported by

National Natural Science Foundation of China (51675047, 52005288); Aeronautical Science Foundation of China (2019ZC072003)

Fold

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

Cite this article

LONG Teng , MAO Nengfeng , SHI Renhe , WU Yufei , SHEN Dunliang . Mode pursuing sampling intelligent exploring method considering expensive constraints[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(4) : 525060 -525060 . DOI: 10.7527/S1000-6893.2021.25060

References

[1] 龙腾, 刘建, WANG G G, 等. 基于计算试验设计与代理模型的飞行器近似优化策略探讨[J]. 机械工程学报, 2016, 52(14):79-105. LONG T, LIU J, WANG G G, et al. Discuss on approximate optimization strategies using design of computer experiments and metamodels for flight vehicle design[J]. Journal of Mechanical Engineering, 2016, 52(14):79-105(in Chinese).
[2] 张嘉良, 秦伟伟, 秦庆强, 等. 考虑多重影响因素的巡航飞行器增程优化设计研究[J]. 航空兵器, 2020, 27(2):32-38. ZHANG J L, QING W W, QING Q Q, et al. Research on extended range optimization design of cruising aircraft considering multiple influencing factors[J]. Aero Weaponry, 2020, 27(2):32-38(in Chinese).
[3] 雷玉昌, 张登成, 张艳华, 等. 一种双后掠翼飞行器气动布局的多目标优化设计[J]. 系统仿真学报, 2019, 31(12):2885-2891. LEI Y C, ZHANG D C, ZHANG Y H, et al. Multi-objective optimization design of aerodynamic layout for twin swept-wing aircraft[J]. Journal of System Simulation, 2019, 31(12):2885-2891(in Chinese).
[4] 孙伟, 张呈林. 直升机桨叶气动外形多目标优化设计[J]. 航空动力学报, 2011, 26(7):1608-1614. SUN W, ZHANG C L. Multi-objective optimization for aerodynamic shape of helicopter blade[J]. Journal of Aerospace Power, 2011, 26(7):1608-1614(in Chinese).
[5] VIANA F A C, SIMPSON T W, BALABANOV V, et al. Metamodeling in multidisciplinary design optimization:how far have we really come?[J]. AIAA Journal, 2014, 52(4):670-690.
[6] QU Q, DING X F, WANG X Y. A filter and nonmonotone adaptive trust region line search method for unconstrained optimization[J]. Symmetry, 2020, 12(4):656.
[7] WANG G G, DONG Z M, AITCHISON P. Adaptive response surface method-A global optimization scheme for approximation-based design problems[J]. Engineering Optimization, 2001, 33(6):707-733.
[8] 武宇飞, 龙腾, 史人赫, 等. 针对高维优化问题的快速追峰采样方法[J]. 机械工程学报, 2019, 55(3):138-146. WU Y F, LONG T, SHI R H, et al. A rapid mode pursuing sampling method for high dimensional optimization problems[J]. Journal of Mechanical Engineering, 2019, 55(3):138-146(in Chinese).
[9] HE Y W, SUN J J, SONG P, et al. Dual Kriging assisted efficient global optimization of expensive problems with evaluation failures[J]. Aerospace Science and Technology, 2020, 105:106006.
[10] WANG L Q, SHAN S Q, WANG G G. Mode-pursuing sampling method for global optimization on expensive black-box functions[J]. Engineering Optimization, 2004, 36(4):419-438.
[11] YU H B, TAN Y, ZENG J C, et al. Surrogate-assisted hierarchical particle swarm optimization[J]. Information Sciences, 2018, 454:59-72.
[12] LI Y L, LIU L, LONG T, et al. Metamodel-based global optimization using fuzzy clustering for design space reduction[J]. Chinese Journal of Mechanical Engineering, 2013, 26(5):928-939.
[13] DUAN X, WANG G G, KANG X, et al. Performance study of mode-pursuing sampling method[J]. Engineering Optimization, 2009, 41(1):1-21.
[14] CHENG G H, YOUNIS A, HAJIKOLAEI K H, et al. Trust region based mode pursuing sampling method for global optimization of high dimensional design problems[J]. Journal of Mechanical Design, 2015, 137(2):021407.
[15] KAZEMI M, WANG G G, RAHNAMAYAN S, et al. Metamodel-based optimization for problems with expensive objective and constraint functions[J]. Journal of Mechanical Design, 2011, 133(1):014505.
[16] 粟华, 谷良贤, 龚春林. 求解黑箱优化问题的动态模式跟踪抽样算法[J]. 计算机集成制造系统, 2013, 19(7):1553-1558. SU H, GU L X, GONG C L. Dynamic mode-pursuing sampling method for black-box function optimization problems[J]. Computer Integrated Manufacturing Systems, 2013, 19(7):1553-1558(in Chinese).
[17] WU Y F, LONG T, SHI R H, et al. Mode-pursuing sampling method using discriminative coordinate perturbation for high-dimensional expensive black-box optimization[J]. Journal of Mechanical Design, 2021, 143(4):041703.
[18] 龙腾, 刘建, 陈余军, 等. 基于约束EGO的对地观测卫星多学科设计优化[J]. 机械工程学报, 2018, 54(10):133-142. LONG T, LIU J, CHEN Y J, et al. Multi-disciplinary design optimization of earth observation satellite based on constrained EGO[J]. Journal of Mechanical Engineering, 2018, 54(10):133-142(in Chinese).
[19] LONG T, WU D, GUO X S, et al. Efficient adaptive response surface method using intelligent space exploration strategy[J]. Structural and Multidisciplinary Optimization, 2015, 51(6):1335-1362.
[20] CROSSLEY W A. A genetic algorithm with the Kreisselmeier-Steinhauser function for multiobjective constrained optimization of rotor systems[C]//Proceeding of the 35th Aerospace Sciences Meeting and Exhibit. Reston:AIAA, 1997.
[21] SHI R H, LIU L, LONG T, et al. Filter-based sequential radial basis function method for spacecraft multidisciplinary design optimization[J]. AIAA Journal, 2018, 57(3):1019-1031.
[22] 周志华. 机器学习[M]. 北京:清华大学出版社, 2016. ZHOU Z H. Machine learning[M]. Beijing:Tsinghua University Press, 2016(in Chinese).
[23] POON N M K, MARTINS J R R A. An adaptive approach to constraint aggregation using adjoint sensitivity analysis[J]. Structural Multidisciplinary Optimization, 2007, 34:61-73.
[24] 叶年辉, 龙腾, 武宇飞, 等. 基于Kriging代理模型的约束差分进化算法[J/OL]. 航空学报, 2020,[2021-01-14]. http://hkxb.buaa.edu.cn/CN/10.7527/S1000-6893.2020.24580. YE N H, LONG T, WU Y F, et al. Kriging assisted constrained differential evolution algorithm[J/OL]. Acta Aeronautica et Astronautica Sinica, 2020,[2021-01-14]. http://hkxb.buaa.edu.cn/CN/10.7527/S1000-6893.2020.24580(in Chinese).
[25] COELLO A C C, MEZURA-MONTES E. Constraint-handling in genetic algorithms through the use of dominance-based tournament selection[J]. Advanced Engineering Informatics, 2002, 16:193-203.
[26] FLOUDAS A C, PARDALOS P M. A collection of test problems for constrained global optimization algorithms[M]. Berlin:Springer Science & Business Media, 1990.
[27] MICHALEWICZ Z, SCHOENAUER M. Evolutionary algorithms for constrained parameter optimization problems[J]. Evolutionary Computation, 1996, 4(1):1-32.
[28] MEZURA-MONTES E, CETINA-DOMINGUEZ O. Empirical analysis of a modified artificial bee colony for constrained numerical optimization[J]. Applied Mathematics and Computation, 2012, 218(22):10943-10973.
[29] LIANG J J, RUNARSSON T P, MEZURA-MONTES E, et al. Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization[J]. Applied Mechanics, 2006, 418(8):8-31.
[30] WU G H, MALLIPEDDI R, SUGANTHAN P N. Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization[R]. Changsha:National University of Defense Technology, 2017.
[31] SHI R H, LIU L, LONG T, et al. Surrogate assisted multidisciplinary design optimization for an all-electric GEO satellite[J]. Acta Astronautica, 2017, 138:301-317.
[32] REGIS R G. Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points[J]. Engineering Optimization, 2014, 46(2):218-243.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References