A novel global optimization method - Genetic Pattern Search

被引：4

作者：

Zhang, Yudong ^{[1
]}

Wu, Lenan ^{[1
]}

Huo, Yuankai ^{[1
]}

Wang, Shuihua ^{[1
]}

机构：

[1] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China

来源：

FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE, PTS 1-4 | 2011年 / 44-47卷

关键词：

global optimization; genetic algorithm; coarse search; fine search; PARTICLE SWARM OPTIMIZATION; ALGORITHM; MODEL;

D O I：

10.4028/www.scientific.net/AMM.44-47.3240

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A novel global optimization method is proposed to find global minimal points more effectively and quickly. The new algorithm is based on both genetic algorithms (GA) and pattern search (PS) algorithms, thus, we have named it genetic pattern search. The procedure involves two-phases: First, GA executes a coarse search, PS then executes a fine search. Experiments on four different test functions (consisting of Hump, Powell, Rosenbrock, and Woods) demonstrate that this proposed new algorithm is superior to improved GA and improved PS with respect to success rate and computation time. Therefore, genetic pattern search is an effective and viable global optimization method.

引用

页码：3240 / 3244

页数：5

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