A COMPARISON OF BAYESIAN SAMPLING GLOBAL OPTIMIZATION TECHNIQUES

A survey of current global optimization techniques for continuous variables is presented, inspired by recent publications of computer coding of several popular Bayesian/sampling methods. The methods of Perttunen, Stuckman, Mockus, Zilinskas, and Shaltenis are compared with other global optimization algorithms, specifically, a clustering algorithm, a simulated annealing algorithm and the Monte Carlo method. Results are given for these methods based upon the experimental rate of convergence on a series of standard test functions. A new test function is presented that has a global solution within an area that is small in comparison with the search space.