The improved ant colony algorithm based on immunity system genetic algorithm and application

被引：0
作者：
Zhang, Caiqing [1 ]
Lu, Yanchao [1 ]
机构：
[1] North China Elect Power Univ, Dept Econ Management, Baoding 071003, Hebei, Peoples R China
来源：
PROCEEDINGS OF THE FIFTH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, VOLS 1 AND 2 | 2006年
关键词：
ant colony algorithm (ACA); genetic algorithm (GA); immunity system (IS); TSP;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, aims at the weakness of ant colony algorithm that leads to converge rashly to the non-overall superior solution and its calculating time is long, when deals with resolving large optimization problem, a improved ant colony algorithm is presented. The algorithm combines the overall hunting ability with expansibility of the genetic algorithm and the character of immunity system in guiding partial hunting for particular problem. It is applied to the process of searching for the optimization in TSP, compares with the result of GA and ACA, the result of the new algorithm closes to superior solution much more, the validity of the algorithm is verified.
引用
收藏
页码：726 / 731
页数：6
相关论文
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