Bayesian optimization with local search
This work addresses global optimization challenges in real-world applications, but it appears incremental as it combines existing multi-start and Bayesian optimization techniques.
The authors tackled the problem of global optimization by proposing a multi-start algorithm that uses Bayesian optimization to select starting points for local searches, resulting in a method that constructs a new function with the same global optima as the original objective.
Global optimization finds applications in a wide range of real world problems. The multi-start methods are a popular class of global optimization techniques, which are based on the ideas of conducting local searches at multiple starting points. In this work we propose a new multi-start algorithm where the starting points are determined in a Bayesian optimization framework. Specifically, the method can be understood as to construct a new function by conducting local searches of the original objective function, where the new function attains the same global optima as the original one. Bayesian optimization is then applied to find the global optima of the new local search defined function.