Research of Improved Ant Colony Hybrid Algorithm

被引：0
|
作者：
Li Shijun [1 ]
Han Yu [1 ]
Gu Hongjun [1 ]
Gong He [1 ]
Li Jian [1 ]
机构：
[1] Jilin Agr Univ, Coll Informat & Technol, Changchun 130118, Peoples R China
来源：
PROCEEDINGS OF THE 2016 6TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS, ENVIRONMENT, BIOTECHNOLOGY AND COMPUTER (MMEBC) | 2016年 / 88卷
关键词：
ant colony algorithm; immune algorithm; artificial fish swarm algorithm; hybrid algorithm;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In order to extend the application of ant colony algorithm (ACA), many scholars combined the ant colony algorithm with immune algorithm (IA) or other algorithms to solve the problem of slow convergence. To fully solve the too long search time, easily falling into local optimization, slow convergence and some other defects, the immune algorithm and artificial fish swarm algorithm (AFSA) combine with the ant colony algorithm, and the ant colony hybrid algorithm is proposed. Then by solving the traveling salesman problem (TSP), the new algorithm is simulated, and the results show that improving algorithm is effective and feasible.
引用
收藏
页码：582 / 586
页数：5
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