Hybrid ant colony algorithm based on scale compression

被引：0
|
作者：
Yan, Jian-Feng [1 ]
Li, Na [2 ]
Li, Wei-Hua [1 ]
Shi, Hao-Bin [1 ]
机构：
[1] NorthWestern Polytech Univ, Sch Comp Sci, Shaanxi 710072, Peoples R China
[2] China Elect Technol Grp, Res Inst 54th, HeBei 050081, Peoples R China
来源：
PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2007年
关键词：
ant colony; TSP; intersection; scale compression; state transition rule;
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：
To improve performance of ant colony algorithm when solving large-scale TSP problem, a hybrid ant colony algorithm based on scale compression is proposed. First we use genetic algorithm to generate a suboptimal solution set and calculate their intersection. By eliminating all cities mapped by the elements among the intersection in the primal TSP problem, we convert the original problem into a new one with smaller scale. In addition, we design a new optimal state transition rule based on regional characteristic of optimal solutions to accelerate convergence speed. Simulation results show our approach possess high searching ability and excellent convergence performance.
引用
收藏
页码：885 / +
页数：2
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