The paper proposes an enhanced approach, called OCTHaGOn, for solving black-box global optimization problems by approximating nonlinear constraints using machine learning models and incorporating adaptive sampling and robust optimization techniques. The approach is tested on 81 instances and shows improvements in solution feasibility and optimality compared to existing methods.

https://arxiv.org/abs//2311.01742

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