A COMPARISON OF BAYESIAN SAMPLING GLOBAL OPTIMIZATION TECHNIQUES

被引：24

作者：

STUCKMAN, BE

EASOM, EE

机构：

[1] Department of Electrical Engineering, of Louisville., Louisville, KY

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS | 1992年 / 22卷 / 05期

关键词：

D O I：

10.1109/21.179841

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1024 / 1032

页数：9