A hybrid fast descent method for globally optimizing high dimensional functions

Yong Jun Wang; Jiang She Zhang

A hybrid fast descent method for globally optimizing high dimensional functions

Yong Jun Wang
, Jiang She Zhang

数学与统计学院

Xi'an Jiaotong University
China National Petroleum Corporation

科研成果: 期刊稿件 › 文章 › 同行评审

1 引用（Scopus）

摘要

A new algorithm built on a hybrid method (GRSA) for large scale global optimization problems is proposed. Unlike the previous proposed method that the original objective functions keep unchanged during the whole course of optimizing, a convexized auxiliary function on the obtained local minimizer so far is employed to improve the SA search ability. The experiments conducted show that the new method provides excellent results especially for large scale problems, compared to other state-of-the-art algorithm.

源语言	英语
页（从-至）	87-98
页数	12
期刊	Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms
卷	15
期	1
出版状态	已出版 - 2月 2008

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{80cd841edb204d5fab5916735172d175,

title = "A hybrid fast descent method for globally optimizing high dimensional functions",

abstract = "A new algorithm built on a hybrid method (GRSA) for large scale global optimization problems is proposed. Unlike the previous proposed method that the original objective functions keep unchanged during the whole course of optimizing, a convexized auxiliary function on the obtained local minimizer so far is employed to improve the SA search ability. The experiments conducted show that the new method provides excellent results especially for large scale problems, compared to other state-of-the-art algorithm.",

keywords = "Auxiliary function, Global optimization, Gradient algorithm, Simulated annealing method (SA)",

author = "Wang, \{Yong Jun\} and Zhang, \{Jiang She\}",

year = "2008",

month = feb,

language = "英语",

volume = "15",

pages = "87--98",

journal = "Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms",

issn = "1492-8760",

publisher = "Watam Press",

number = "1",

}

TY - JOUR

T1 - A hybrid fast descent method for globally optimizing high dimensional functions

AU - Wang, Yong Jun

AU - Zhang, Jiang She

PY - 2008/2

Y1 - 2008/2

N2 - A new algorithm built on a hybrid method (GRSA) for large scale global optimization problems is proposed. Unlike the previous proposed method that the original objective functions keep unchanged during the whole course of optimizing, a convexized auxiliary function on the obtained local minimizer so far is employed to improve the SA search ability. The experiments conducted show that the new method provides excellent results especially for large scale problems, compared to other state-of-the-art algorithm.

AB - A new algorithm built on a hybrid method (GRSA) for large scale global optimization problems is proposed. Unlike the previous proposed method that the original objective functions keep unchanged during the whole course of optimizing, a convexized auxiliary function on the obtained local minimizer so far is employed to improve the SA search ability. The experiments conducted show that the new method provides excellent results especially for large scale problems, compared to other state-of-the-art algorithm.

KW - Auxiliary function

KW - Global optimization

KW - Gradient algorithm

KW - Simulated annealing method (SA)

UR - https://www.scopus.com/pages/publications/39449116993

M3 - 文章

AN - SCOPUS:39449116993

SN - 1492-8760

VL - 15

SP - 87

EP - 98

JO - Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms

JF - Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms

IS - 1

ER -

A hybrid fast descent method for globally optimizing high dimensional functions

摘要

其它文件与链接

学术指纹

引用此