A comparison of Bayesian/sampling global optimization techniques
B.E. Stuckman, E.E. Easom · IEEE Transactions on Systems Man and Cybernetics · 1992
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.>