中文

Operations Research Transactions >

2024 , Vol. 28 >Issue 1: 89 - 100

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2024.01.007

An adaptive surrogate optimization method for expensive black-box problems with hidden constraints

Expand
  • 1. National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
    2. School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China

Received date: 2021-10-11

  Online published: 2024-03-16

Copyright

, 2024, All rights reserved, without authorization
Fold

Abstract

A surrogate optimization method with adaptive transition search strategy is proposed for expensive black-box problems with hidden constraints. In the sub-steps of the transition search, a variance related to the number of evaluated points is used for the generation of trial points by random perturbation to better balance the local and global searches. In order to better approximate the real black box objective function, an adaptively combined objective surrogate model is adopted. The effectiveness of the proposed algorithm is demonstrated by the results of the numerical experiments carried out on 50 test problems.

Key words： expensive black-box problems; global optimization; hidden constraints; surrogate optimization

Cite this article

Fusheng BAI, Mi LAN . An adaptive surrogate optimization method for expensive black-box problems with hidden constraints[J]. Operations Research Transactions, 2024 , 28(1) : 89 -100 . DOI: 10.15960/j.cnki.issn.1007-6093.2024.01.007

References

1 Carter R G , Gablonsky J M , Patrick A , et al. Algorithms for noisy problems in gas transmission pipeline optimization[J]. Optimization and Engineering, 2001, 2 (2): 139- 157.
2 Ji C , Dames E , Wang Y L , et al. Propagation and extinction of premixed C5-C12 n-alkane flames[J]. Combustion Flame, 2010, 157 (2): 277- 287.
3 Booker A J , Dennis J E , Frank P D , et al. A rigorous framework for optimization of expensive functions by surrogates[J]. Structural Optimization, 1999, 17 (1): 1- 13.
4 Juliane M , Marcus D . Surrogate optimization of computationally expensive black-box problems with hidden constraints[J]. INFORMS Journal on Computing, 2019, 31 (4): 689- 702.
5 Myers R H , Montgomery D C . Response Surface Methodology: Process and Product Optimization Using Designed Experiments[M]. New York: Wiley, 1995.
6 Digabel S L . Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm[J]. ACM Transactions on Mathematical Software, 2011, 37 (4): 1- 15.
7 Mueller J , Shoemaker C A . Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms forcomputationally expensive black-box global optimization problems[J]. Journal of Global Optimization, 2014, 60 (2): 123- 144.
8 Gutmann H M . A radial basis function method for global optimization[J]. Journal of Global Optimization, 2001, 19 (3): 201- 227.
9 白富生, 冯丹, 张柯. 求解昂贵黑箱全局优化问题的自适应采样组合响应面方法[J]. 运筹学学报, 2021, 25 (2): 1- 14.
10 Powell M J D. The theory of radial basis function approximation in 1990[M]//Advances in Numerical Analysis, Oxford: Oxford University Press, 105-210.
11 Jie H , Wu Y , Ding J . An adaptive metamodel-based global optimization algorithm for black-box type problems[J]. Engineering Optimization, 2015, 47 (11): 1459- 1480.
12 Regis R G , Shoemaker C A . Combining radial basis function surrogates dynamic coordinate search in high dimensional expensive black-box optimization[J]. Engineering Optimzation, 2013, 45 (5): 529- 555.
13 Juliane M , Marcus D . Online supplement for Surrogate optimization of computationally expensive black-box problems with hidden constraints[J]. INFORMS Journal on Computing, 2019, 31 (4): 689- 702.
14 Dolan E D , Moré J J . Benchmarking optimization software with performance profiles[J]. Mathematical Programming, 2001, 91 (2): 201- 213.
15 Moré J J , Wild S M . Benchmarking derivative-free optimization algorithms[J]. SIAM Journal on Optimization, 2009, 20 (1): 172- 191.
Options
Outlines

/

〈 〉