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

Volume

22

Issue

5

fYear

1992

Firstpage

1024

Lastpage

1032

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;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.179841

Filename

179841