Preference-Aware Bayesian Optimization for Interactive Decision Making

In real-world scenarios, optimization problems generally exhibit multiobjective characteristics, necessitating a balance among conflicting evaluation criteria. An effective approach to multiobjective optimization (MOO) is to approximate the Pareto front. However, completely solving for the Pareto front incurs extremely high computational costs, making it almost infeasible in practical applications. In practical scenarios, decision makers (DMs) typically require only a single most preferred solution from the Pareto-optimal set. Existing optimization methods often struggle to incorporate real-time DMs' preferences during the optimization process. To address this limitation, this article proposes a preference-aware Bayesian optimization (PABO) framework for interactive decision-making that seamlessly integrates DMs' feedback throughout the entire optimization process. By embedding preference information into the candidate solution generation stage, PABO dynamically adjusts the balance between exploration of uncertain regions and exploitation of preference-aligned solutions, thereby achieving efficient preference satisfaction. Experiments on benchmark functions and real-world engineering cases demonstrate that PABO achieves comparable or superior solution quality with significantly fewer expensive evaluations than state-of-the-art methods. This achievement indicates that PABO demonstrates significant advantages in improving optimization efficiency and reducing costs, providing a more feasible technical approach for the practical application of MOO problems.