An improved ant colony algorithm based on adaptive pheromone updating strategy

被引：0
|
作者：
Qin, Ling [1 ]
Chen, Yixin
Wu, Yong
Chen, Ling
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Dept Comp Sci, Nanjing 210016, Peoples R China
[2] Washington Univ, Dept CS & Engn, St Louis, MO USA
[3] Univ Sci & Technol, Suzhou, Peoples R China
[4] Yangzhou Univ, Dept CS, Yangzhou 225009, Peoples R China
来源：
DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS | 2006年 / 13E卷
关键词：
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
An adaptive local pheromone updating strategy is added in the classical ant colony algorithm, besides the global updating operation, to improve the performance of the algorithm. And once the pheromone is updated, the control parameters alpha and beta will be adaptively modified by the pheromone distributing weight and make influence on the effect of pheromone and heuristic function. Experimental results on the traveling salesman problem show that this algorithm can not only accelerate the convergence speed but also avoid local convergence and precocity.
引用
收藏
页码：1133 / 1137
页数：5
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