Constrained Offline Black-Box Optimization via Risk Evaluation and Management

Offline black-box optimization aims to identify the optimal solution of a black-box objective function under the guidance of a surrogate model constructed solely from a pre-collected dataset. It is commonly used in industrial scenarios, which often involve constraints, i.e., constrained offline optimization (COO). Offline optimization has progressed in addressing the out-of-distribution (OOD) issue caused by its inherent inability to interact with the objective function. However, there is not enough research in addressing more difficult scenarios, which must simultaneously address OOD issues and constrained issues to find stable, high-quality (i.e., high-scoring and feasible) solutions. To bridge this gap, this paper proposes a method called constrained offline optimization via risk evaluation and management (COOREM), which is capable of consistently surpassing the offline dataset under the condition of satisfying constraints. Specifically, COOREM employs a dual-energy model to separately evaluate OOD risk and constrained risk. This separation strategy aims to distinguish and address two difficult cases: the infeasible but not OOD solutions and the feasible but OOD solutions. Moreover, COOREM effectively manages OOD risk and constrained risk, ensuring the identification of high-quality solutions. Extensive experiments on real-world tasks, e.g., space missions, process synthesis, and design problems, showcase COOREM's effectiveness in managing both OOD risk and constrained risk. Furthermore, our findings indicate that COOREM could outperform online methods that need to access the objective function in certain space missions.