NDLI: A Kriging-based constrained global optimization algorithm for expensive black-box functions with infeasible initial points

Content Provider	Springer Nature Link
Author	Li, Yaohui Wu, Yizhong Zhao, Jianjun Chen, Liping
Copyright Year	2016
Abstract	In many engineering optimization problems, the objective and the constraints which come from complex analytical models are often black-box functions with extensive computational effort. In this case, it is necessary for optimization process to use sampling data to fit surrogate models so as to reduce the number of objective and constraint evaluations as soon as possible. In addition, it is sometimes difficult for the constrained optimization problems based on surrogate models to find a feasible point, which is the premise of further searching for a global optimal feasible solution. For this purpose, a new Kriging-based Constrained Global Optimization (KCGO) algorithm is proposed. Unlike previous Kriging-based methods, this algorithm can dispose black-box constrained optimization problem even if all initial sampling points are infeasible. There are two pivotal phases in KCGO algorithm. The main task of the first phase is to find a feasible point when there is no feasible data in the initial sample. And the aim of the second phase is to obtain a better feasible point under the circumstances of fewer expensive function evaluations. Several numerical problems and three design problems are tested to illustrate the feasibility, stability and effectiveness of the proposed method.
Starting Page	343
Ending Page	366
Page Count	24
File Format	PDF
ISSN	09255001
Journal	Journal of Global Optimization
Volume Number	67
Issue Number	1-2
e-ISSN	15732916
Language	English
Publisher	Springer US
Publisher Date	2016-07-22
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Constrained global optimization Black-box functions Surrogate models Kriging Infill search criterion Optimization Operation Research/Decision Theory Real Functions Computer Science
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Applied Mathematics Management Science and Operations Research Business, Management and Accounting Computer Science Applications

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