2025/05/30 by Yezhuo Li, Li, Yezhuo, Qiong Zhang +5
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Advanced Statistical Process Monitoring #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.2506.00174
openalex publication_date 2025/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Bayesian Optimization, leveraging Gaussian process models, has proven to be a powerful tool for minimizing expensive-to-evaluate objective functions by efficiently exploring the search space. Extensions such as constrained Bayesian Optimization have further enhanced Bayesian Optimization's utility in practical scenarios by focusing the search within feasible regions defined by a black-box constraint function. However, constrained Bayesian Optimization in is developed based on the independence Gaussian processes assumption between objective and constraint functions, which may not hold in real-world applications. To address this issue, we use the bivariate Gaussian process model to characterize the dependence between the objective and constraint functions and developed the constrained expected improvement acquisition function under this model assumption. We show case the performance of the proposed approach with an application to cure process optimization in Manufacturing.