  This paper presents a method called sampling-computation-optimization (SCO)
to design batch Bayesian optimization. SCO does not construct new
high-dimensional acquisition functions but samples from the existing one-site
acquisition function to obtain several candidate samples. To reduce the
uncertainty of the sampling, the general discrepancy is computed to compare
these samples. Finally, the genetic algorithm and switch algorithm are used to
optimize the design. Several strategies are used to reduce the computational
burden in the SCO. From the numerical results, the SCO designs were less
uncertain than those of other sampling-based methods. As for application in
batch Bayesian optimization, SCO can find a better solution when compared with
other batch methods in the same dimension and batch size. In addition, it is
also flexible and can be adapted to different one-site methods. Finally, a
complex experimental case is given to illustrate the application value and
scenario of SCO method.
