Global Optimization Using Diffusion Perturbations with Large Noise Intensity

作     者：G. Yin K. Yin 

作者机构：Department of Mathematics Wayne State University Detroit MI 48202 Department of Bio-based Products University of Minnesota Saint Paul MN 55108 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2006年第22卷第4期

页      面：529-542页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070105[理学-运筹学与控制论] 0701[理学-数学] 

基　　金：Supported by the National Science Foundation under grants DMS-0304928 and CMS-0510655, the National Natural Science Foundation of China(No.60574069) the U.S.Department of Agriculture,Minnesota Agricultural Experiment Stations 

主　　题：Global optimization random perturbation diffusion 

摘      要：This work develops an algorithm for global optimization. The algorithm is of gradient ascent type and uses random perturbations. In contrast to the annealing type procedures, the perturbation noise intensity is large. We demonstrate that by properly varying the noise intensity, approximations to the global maximum can be achieved. We also show that the expected time to reach the domain of attraction of the global maximum, which can be approximated by the solution of a boundary value problem, is finite. Discrete-time algorithms are proposed; recursive algorithms with occasional perturbations involving large noise intensity are developed. Numerical examples are provided for illustration.