Abstract:
This study presents a comprehensive evaluation of initial sampling techniques within the context of Bayesian Optimization (BO), a machine learning technique intended for the optimization of intricate and expensive functions. We assessed its efficacy in optimizing both theoretical benchmark functions and real-world applications. The findings reveal that, while BO is inherently robust and effective in a wide range of optimization problems, the integration of structured initial sampling methods, such as Latin Hypercube Sampling (LHS) and fractional factorial design (FFD) in the context of Design of Experiments (DoE), can significantly alter its performance. In addition, by systematically exploring different optimization strategies, the study highlights how LHS and FFD, followed by BO, can, for example, lead to substantial reductions in energy consumption—up to approximately 67.45% compared to average consumption. In conclusion, this study contributes to the growing body of knowledge on BO by demonstrating the value of early sampling techniques in enhancing BO effectiveness. This study offers a roadmap for future studies to build on in the pursuit of more efficient and effective optimization strategies in complex real-world scenarios.
