Convergence Proof of a Class of Adaptive Ant Colony Algorithm

被引：0
|
作者：
Zhao, Baojiang [1 ]
机构：
[1] Mudanjing Normal Univ, Mudanjing 157011, Heilongjiang, Peoples R China
来源：
PROCEEDINGS OF THE 2015 4TH INTERNATIONAL CONFERENCE ON COMPUTER, MECHATRONICS, CONTROL AND ELECTRONIC ENGINEERING (ICCMCEE 2015) | 2015年 / 37卷
关键词：
Ant colony optimization; markov process; convergence; OPTIMIZATION;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper presents a class of adaptive ant colony optimization algorithm and proves its convergence properties. The global searching and convergence ability are improved by adaptively changing the pheromone trails evaporation factors and decreasing lower pheromone bound. Markov process analysis is used to prove convergence properties of the algorithms. It is shown that its current solutions of the system converge, with probability one, to an optimal solution of the system.
引用
收藏
页码：976 / 979
页数：4
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