Hybrid simplex-improved genetic algorithm for global numerical optimization

被引：21

作者：

Ren, Zi-Wu ^{[1
]}

San, Ye ^{[1
]}

Chen, Jun-Feng ^{[2
]}

机构：

[1] Control and Simulation Centre, Harbin Institute of Technology

[2] College of Computer and Information Engineering, Hohai University

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2007年 / 33卷 / 01期

关键词：

Competition and selection; Genetic algorithm; Mutation scaling; Simplex method;

D O I：

10.1360/aas-007-0091

中图分类号：

学科分类号：

摘要：

In this paper, a hybrid simplex-improved genetic algorithm (HSIGA) which combines simplex method (SM) and genetic algorithm (GA) is proposed to solve global numerical optimization problems. In this hybrid algorithm some improved genetic mechanisms, for example, non-linear ranking selection, competition and selection among several crossover offspring, adaptive change of mutation scaling and stage evolution, are adopted; and new population is produced through three approaches, i.e. elitist strategy, modified simplex strategy and improved genetic algorithm (IGA) strategy. Numerical experiments are included to demonstrate effectiveness of the proposed algorithm.

引用

页码：91 / 95

页数：4

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