Title | Surrogate-based optimization for mixed-integer nonlinear problems |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Kim, SHye, Boukouvala, F |
Journal | Computers & Chemical Engineering |
Volume | 140 |
Pagination | 106847 |
Date Published | sep |
ISSN | 00981354 |
Keywords | 9.3, BP5Q4, Modeling and Simulation, Project 9.3 |
Abstract | Simulation-based optimization using surrogate models enables decision-making through the exchange of data from high-fidelity models and development of approximations. Many chemical engineering optimization problems, such as process design and synthesis, rely on simulations and contain both discrete and continuous decision variables. Surrogate-based optimization with continuous variables has been studied extensively; however, there are many open challenges for the case of mixed-variable inputs. In this work, we propose an algorithm for mixed-integer nonlinear simulation-based problems that uses adaptive sampling and surrogate modeling with one-hot encoding. We propose techniques for the design of experiments for mixed-variable problems, surrogate modeling for mixed-variable response surfaces, and iterative approximation-optimization procedure that leads to optimal solutions. Results show that one-hot encoding leads to accurate and robust mixed-variable Gaussian Process and Neural Network models that are effective surrogates for optimization. The proposed algorithm is tested on mixed-integer nonlinear benchmark problems and a chemical process synthesis case study. |
URL | https://www.sciencedirect.com/science/article/pii/S0098135419306970 |
DOI | 10.1016/j.compchemeng.2020.106847 |