  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.
