Ant Colony Algorithm Research based on Pheromone Update Strategy

被引：7
|
作者：
Zhai, Yahong [1 ]
Xu, Longyan [1 ]
Yang Yanxia [2 ]
机构：
[1] Hubei Univ Automot Technol, Sch Elect & Informat Engn, Shiyan, Peoples R China
[2] Wuhan Univ Sci & Technol, City Coll, Fac Informat Engn, Wuhan, Peoples R China
来源：
2015 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS IHMSC 2015, VOL I | 2015年
关键词：
nearest neighbor ants (NN-Ants) algorithm; minimal spanning tree(MST) algorithm; ant colony optimization; knowledge guide; pheromone update strategy; OPTIMIZATION;
D O I：
10.1109/IHMSC.2015.143
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new pheromone control strategy based on knowledge guide is presented. The main idea is to use the prior knowledge and group knowledge from statistical learning to guide the ant search. It has more targeted for ants search which combined with new pheromone updating strategy, thus contributing to a better solution for the algorithm. The experimental results indicate that the overall performance of the ant colony algorithm based on knowledge guiding pheromone control strategy is obviously better than other ant colony optimization algorithm.
引用
收藏
页码：38 / 41
页数：4
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