Title :
A comparison of Bayesian/sampling global optimization techniques
Author :
Stuckman, Bruce E. ; Easom, Eric E.
Author_Institution :
Dept. of Electr. Eng., Louisville Univ., KY, USA
Abstract :
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
Keywords :
Bayes methods; convergence of numerical methods; optimisation; Bayesian/sampling methods; Monte Carlo method; clustering algorithm; convergence; global optimization; search space; simulated annealing; Bayesian methods; Clustering algorithms; Computational modeling; Computer simulation; Cost function; Design optimization; Optimization methods; Sampling methods; Simulated annealing; Testing;
Journal_Title :
Systems, Man and Cybernetics, IEEE Transactions on
