Meeting Ant Colony Optimization

被引:0
作者
Zhang Fei Jun [1 ]
Gao Wei [2 ]
机构
[1] Wuhan Polytech Univ, Postgrad Coll, Wuhan, Peoples R China
[2] Wuhan Polytech Univ, Civil Engn, Wuhan, Peoples R China
来源
2008 IEEE INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING WORKSHOP PROCEEDINGS, VOLS 1 AND 2 | 2008年
关键词
ant colony optimization; meeting strategy;
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
The ant system is a new meta-heuristic mainly for hard combinatorial optimization problems. It has been unexpectedly successful and known as Ant Colony Optimization (ACO) in recent years. Nowadays, a series of improvements have been made to the ACO, most of which focus on the exploitation of gather information to guide the search of ant colony towards better solution space but neglect the exploration of new tours. In order to enlarge the ants' searching space and diversify the searching solutions, Meeting ACO is proposed here. The main strategy used in this new algorithm is to combine pairs of searching ants together to expand the diversification of the search. To make up the influence caused by limited number of meeting ants, a threshold constant is applied to make the algorithm function normally. As proved by the simulation experiments, the Meeting ACO is ranked among the best ACO for tackling the TSP problems.
引用
收藏
页码:972 / +
页数:2
相关论文
共 8 条
[1]  
[Anonymous], 2004, Ant colony optimization
[2]   Ant colony optimization: Introduction and recent trends [J].
Blum, Christian .
PHYSICS OF LIFE REVIEWS, 2005, 2 (04) :353-373
[3]  
Bullnheimer B., 1999, CENTRAL EUROPEAN J O, V7, P25
[4]  
Cordon Garcia O., 2002, MATHW SOFT COMPUT, V9, P177
[5]  
Dorigo M., 2007, APPL PHYS LETT, V14, P1
[6]   Ant colony optimization -: Artificial ants as a computational intelligence technique [J].
Dorigo, Marco ;
Birattari, Mauro ;
Stuetzle, Thomas .
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2006, 1 (04) :28-39
[7]   Leaf characteristics and shape of sago palm (Metroxylon sagu rottb.) for developing a method of estimating leaf area [J].
Nakamura, S ;
Nitta, Y ;
Goto, Y .
PLANT PRODUCTION SCIENCE, 2004, 7 (02) :198-203
[8]   MAX-MIN Ant System [J].
Stützle, T ;
Hoos, HH .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2000, 16 (08) :889-914