Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques

Optimization Letters 2019

Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear…

Nonlinear Variable Selection Algorithms for Surrogate Modeling

AIChE Journal 2019

Jianyuan Zhai Fani Boukouvala

Having the ability to analyze, simulate and optimize complex systems is becoming more important in all engineering disciplines. Decision-making using complex systems usually leads to nonlinear optimization problems, which rely on computationally expensive simulations. Therefore, it is often challenging to…

Optimization of black-box problems using Smolyak grids and polynomial approximations

Journal of Global Optimization 2018

C.A. Kieslich F. Boukouvala C.A. Floudas

A surrogate-based optimization method is presented, which aims to locate the global optimum of box-constrained problems using input–output data. The method starts with a global search of the n-dimensional space, using a Smolyak (Sparse) grid which is constructed using Chebyshev extrema…

Global optimization of grey-box computational systems using surrogate functions and application to highly constrained oil-field operations

Computers & Chemical Engineering 2018

B. Beykal F. Boukouvala C.A. Floudas N. Sorek H. Zalavadia E. Gildin

This work presents recent advances within the AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems (ARGONAUT) framework, developed for optimization of systems which lack analytical forms and derivatives. A new parallel version of ARGONAUT (p-ARGONAUT) is introduced to solve…

Optimal design of energy systems using constrained grey-box multi-objective optimization

Computers & Chemical Engineering 2018

B. Beykal F. Boukouvala C.A. Floudas E.N. Pistikopoulos

The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the…