  In practice, objective functions of real-time control systems can have
multiple local minimums or can dramatically change over the function space,
making them hard to optimize. To efficiently optimize such systems, in this
paper, we develop a parallel global optimization framework that combines direct
search methods with Bayesian parallel optimization. It consists of an iterative
global and local search that searches broadly through the entire global space
for promising regions and then efficiently exploits each local promising
region. We prove the asymptotic convergence properties of the proposed
framework and conduct an extensive numerical comparison to illustrate its
empirical performance.
