Hybrid algorithm combining ant colony optimization algorithm with genetic algorithm

被引:0
|
作者
Shang, Gao [1 ,2 ]
Jiang Xinzi [1 ]
Tang Kezong [1 ]
机构
[1] Jiangsu Univ Sci & Technol, Sch Elect & Informat, Zhenjiang 212003, Peoples R China
[2] Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R China
来源
PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 5 | 2007年
关键词
ant colony algorithm; genetic algorithm; traveling salesman problem;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
By use of the properties of ant colony algorithm and genetic algorithm, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopts genetic algorithm to give information pheromone to distribute. Second, it makes use of the ant colony algorithm to get several solutions through information pheromone accumulation and renewal. Finally, by using across and mutation operation of genetic algorithm, the effective solutions are obtained. Compare with the simulated annealing algorithm, the standard genetic algorithm, the standard ant colony algorithm, and statistics initial ant colony algorithm, all the 16 hybrid algorithms are proved effective. Especially the hybrid algorithm with across strategy B and mutation strategy B is a simple and effective better algorithm than others.
引用
收藏
页码:701 / +
页数:2
相关论文
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