Human-algorithm collaborative Bayesian optimization for engineering systems

• Published in: Computers & chemical engineering Vol. 189; p. 108810
• Main Authors: Savage, Tom, del Rio Chanona, Ehecatl Antonio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2024
• Subjects: Bayesian optimization; Domain knowledge; Experimental design; Human-in-the-loop; Bayesian optimization; Experimental design; Domain knowledge; Human-in-the-loop
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2024.108810

Bayesian optimization has proven effective for optimizing expensive-to-evaluate functions in Chemical Engineering. However, valuable physical insights from domain experts are often overlooked. This article introduces a collaborative Bayesian optimization approach that re-integrates human input into the data-driven decision-making process. By combining high-throughput Bayesian optimization with discrete decision theory, experts can influence the selection of experiments via a discrete choice. We propose a multi-objective approach togenerate a set of high-utility and distinct solutions, from which the expert selects the desired solution for evaluation at each iteration. Our methodology maintains the advantages of Bayesian optimization while incorporating expert knowledge and improving accountability. The approach is demonstrated across various case studies, including bioprocess optimization and reactor geometry design, demonstrating that even with an uninformed practitioner, the algorithm recovers the regret of standard Bayesian optimization. By including continuous expert opinion, the proposed method enables faster convergence and improved accountability for Bayesian optimization in engineering systems. •New Bayesian optimization approach applying human knowledge in discrete choices.•Multi-objective high-throughput method balances solution utility and diversity.•Benchmarking shows performance gains with expert input across various scenarios.•Improves knowledge integration & accountability in decisions.•Case studies showcase real-world use in bioprocess optimization & reactor design.