GLOPT — A Program for Constrained Global Optimization

1997 ◽  
pp. 19-36 ◽  
Author(s):  
S. Dallwig ◽  
A. Neumaier ◽  
H. Schichl
Keyword(s):  
Global Optimization ◽  
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.

scholarly journals A CARTOPT METHOD FOR BOUND-CONSTRAINED GLOBAL OPTIMIZATION

2013 ◽  
Vol 55 (2) ◽  
pp. 109-128 ◽  
Author(s):  
B. L. ROBERTSON ◽  
C. J. PRICE ◽  
M. REALE
Keyword(s):  
Global Optimization ◽  
Random Sampling ◽  
Global Minimizer ◽  
Stochastic Algorithm ◽  
Constrained Global Optimization ◽  
Objective Functions ◽  
Search Region ◽  
Mild Conditions ◽  
Nonsmooth Problems ◽  
Classification And Regression

AbstractA stochastic algorithm for bound-constrained global optimization is described. The method can be applied to objective functions that are nonsmooth or even discontinuous. The algorithm forms a partition on the search region using classification and regression trees (CART), which defines a region where the objective function is relatively low. Further points are drawn directly from the low region before a new partition is formed. Alternating between partition and sampling phases provides an effective method for nonsmooth global optimization. The sequence of iterates generated by the algorithm is shown to converge to an essential global minimizer with probability one under mild conditions. Nonprobabilistic results are also given when random sampling is replaced with points taken from the Halton sequence. Numerical results are presented for both smooth and nonsmooth problems and show that the method is effective and competitive in practice.

Sign in / Sign up

Export Citation Format

Share Document