基于单纯形梯度的多起点全局优化算法

吉林大学学报(理学版)

基于单纯形梯度的多起点全局优化算法

刘二涛^1, 刘红卫^1, 刘泽显^1,2

1. 西安电子科技大学数学与统计学院, 西安 710126； 2. 贺州学院理学院, 广西贺州 542899

收稿日期:2016-03-22 出版日期:2016-11-26 发布日期:2016-11-29
通讯作者: 刘二涛 E-mail:ertaoliu@163.com

Multistart Global Optimization Algorithm Based on Simplex Gradient

LIU Ertao¹, LIU Hongwei¹, LIU Zexian^1,2

1. School of Mathematics and Statistics, Xidian University, Xi’an 710126, China;2. School of Sciences, Hezhou University, Hezhou 542899, Guangxi Zhuang Autonomous Region, China

Received:2016-03-22 Online:2016-11-26 Published:2016-11-29
Contact: LIU Ertao E-mail:ertaoliu@163.com

摘要/Abstract

摘要： 用已知样本点信息构造单纯形梯度及插值函数, 提出一种基于单纯形梯度的局部搜索算法. 该算法结合有效样本点集Ω的混合选取策略, 改进了多起点聚类全局优化算法. 结果表明, 新算法在效率和稳定性方面均有较大改进, 并可有效处理原算法针对“窄谷”类函数估值次数过高的问题.

关键词: 单链接聚类, 单纯形梯度, 全局算法, 无导数

Abstract: Using the information of known sample points to construct simplex gradient and interpolation function, we proposed a local search algorithm based on the simplex gradient. The algorithm combined the effective sample point set Ω with a hybrid selection strategy, and improved the multistart clustering global optimization algorithm (GLOBAL). Experimental results show that the new algorithm has great improvement in efficiency and stability, and can effectively deal with the problem of high evaluation of the original algorithm for the “narrow valley” function.

Key words: derivativefree; single link clustering, simplex gradient, global algorithm

中图分类号:

O221.2

刘二涛, 刘红卫, 刘泽显. 基于单纯形梯度的多起点全局优化算法[J]. 吉林大学学报(理学版), 2016, 54(06): 1282-1288.

LIU Ertao, LIU Hongwei, LIU Zexian. Multistart Global Optimization Algorithm Based on Simplex Gradient[J]. Journal of Jilin University Science Edition, 2016, 54(06): 1282-1288.