Some feasibility sampling procedures in interval methods for constrained global optimization

2015 ◽  
Vol 67 (1-2) ◽  
pp. 379-397 ◽  
Author(s):  
Mengyi Ying ◽  
Min Sun
Keyword(s):  
Global Optimization ◽  
Interval Methods ◽  
Constrained Global Optimization ◽  
Sampling Procedures

New interval methods for constrained global optimization

2005 ◽  
Vol 106 (2) ◽  
pp. 287-318 ◽  
Author(s):  
M.Cs. Markót ◽  
J. Fernández ◽  
L.G. Casado ◽  
T. Csendes
Keyword(s):  
Global Optimization ◽  
Interval Methods ◽  
Constrained Global Optimization

scholarly journals A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Yaohui Li ◽  
Jingfang Shen ◽  
Ziliang Cai ◽  
Yizhong Wu ◽  
Shuting Wang
Keyword(s):  
Global Optimization ◽  
Pareto Frontier ◽  
Kriging Model ◽  
Optimization Method ◽  
Function Evaluation ◽  
Sampling Point ◽  
Constrained Global Optimization ◽  
Multi Objective ◽  
Sample Data ◽  
Sampling Points

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

Interval Methods for Global Optimization Using the Boxing Method

2001 ◽  
pp. 205-213 ◽  
Author(s):  
Andras Erik Csallner ◽  
Rudi Klatte ◽  
Dietmar Ratz ◽  
Andreas Wiethoff
Keyword(s):  
Global Optimization ◽  
Interval Methods
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