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 C.D. Perttunen (1990), B.E. Stuckman (1988), J.B. Mockus (1989), A. Zilinskas (1980), and V.K. Shaltenis and G. Dzemyda (1982) are compared with 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 which has a global solution within an area which is small in comparison with the search space.<
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