Constrained Bayesian Optimization with Feasibility-Infeasibility Weighted Improvement Criterion
针对现有约束贝叶斯优化方法需要初始可行点且限制探索可行域的问题，提出一种新的改进型采集函数，在可行与不可行区域间平衡探索与利用，并在四个基准问题、地下水修复和神经网络超参数调优中验证了有效性。
In Bayesian optimization, Expected Improvement (EI) is widely used for unconstrained optimization but lacks effective handling of constraints. Existing approaches modify EI by incorporating feasibility probabilities, requiring an initial feasible point, and often restricting exploration to the feasible region. This article introduces a novel improvement-based acquisition function designed to address these limitations. The proposed function strikes a balance between exploration and exploitation across both feasible and infeasible regions. We evaluate our framework against state-of-the-art methods on four benchmark problems and apply it to groundwater remediation in hydrology and hyperparameter tuning of neural networks.