A multidimensional Bayesian global search method which incorporates knowledge of an upper bound

The authors present a method of incorporating a priori information into an N-dimensional Bayesian method of global optimization. Many real-world optimization problems are of such variety that the objective function is not convex. Therefore, a global algorithm which considers the entire search space is necessary. A priori knowledge of a bound on the objective function can be incorporated into the search algorithm. The algorithm restricts its sampling to areas most likely to contain the global maximum of the function. Past work on 1D algorithms has provided a means of incorporating this information into a Bayesian method of global optimization to hasten convergence. Numerical results of this method on a standard test function are presented