An outer-approximation algorithm for a class of mixed-integer nonlinear programs
Mathematical Programming: Series A and B
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Nelder-Mead simplex modifications for simulation optimization
Management Science
Superlinear Convergence and Implicit Filtering
SIAM Journal on Optimization
Journal of Global Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Improved Strategies for Radial basis Function Methods for Global Optimization
Journal of Global Optimization
A review of recent advances in global optimization
Journal of Global Optimization
Discrete-valued, stochastic-constrained simulation optimization with compass
Proceedings of the Winter Simulation Conference
Global optimization of expensive black box problems with a known lower bound
Journal of Global Optimization
Hi-index | 0.00 |
In this paper a new methodology is developed for the solution of mixed-integer nonlinear programs under uncertainty whose problem formulation is complicated by both noisy variables and black-box functions representing a lack of model equations. A branch-and-bound framework is employed to handle the integer complexity whereby the solution to the relaxed nonlinear program subproblem at each node is obtained using both global and local information. Global information is obtained using kriging models which are used to identify promising neighborhoods for local search. Response surface methodology (RSM) is then employed whereby local models are sequentially optimized to refine the problem's lower and upper bounds. This work extends the capabilities of a previously developed kriging-response surface method enabling a wider class of problems to be addressed containing integer decisions and black box models. The proposed algorithm is applied to several small process synthesis examples and its effectiveness is evaluated in terms of the number of function calls required, number of times the global optimum is attained, and computational time.