A novel immune evolutionary algorithm incorporating chaos optimization

被引：54

作者：

Guo, ZL ^{[1
]}

Wang, S ^{[1
]}

Zhuang, J ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shan Xi, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2006年 / 27卷 / 01期

关键词：

immune evolutionary algorithm; chaos search; chaos immune evolutionary algorithm;

D O I：

10.1016/j.patrec.2005.06.014

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Making use of the ergodicity and internal randomness of chaos iterations, a novel immune evolutionary algorithm based on the chaos optimization algorithm and immune evolutionary algorithm is presented to improve the convergence performance of the immune evolutionary algorithm. The novel algorithm integrates advantages of the immune evolutionary algorithm and chaos optimization algorithm. Chaos variables are loaded into the variable colony of the immune algorithm in the immune evolutionary algorithm, tiny disturbance is introduced into the memory colony, and the disturbance amplitude is gradually adjusted based on the characteristic of chaos search. The experimental results indicate that the new immune evolutionary algorithm improves the convergence performance and search efficiency of the immune evolutionary algorithm. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：2 / 8

页数：7

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