A new pheromone updating strategy in ant colony optimization

Author

Sun, Jun ; Xiong, Sheng-wu ; Guo, Fu-Ming

Author_Institution

Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., Hubei, China

Volume

1

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

620

Abstract

This work presents a new pheromone updating strategy , which is used to optimize ACO (ant colony optimization) in solving the traveling salesman problem. At first, the paper introduces the principle, the characteristics, the construction and the realization method about the ACO. Then, an improved ant colony optimization algorithm using a new pheromone updating strategy is proposed. The pheromone trail of each edge is set with a lower limit at the beginning iterations of the algorithm, and the worst ant judged by its tour length like the best ant used in ACO is allowed to perform global trail updating. At last, we demonstrate the efficiency of the algorithm by means of experimental study.

Keywords

travelling salesman problems; ant colony optimization; global trail updating; pheromone updating strategy; traveling salesman problem; Ant colony optimization; Circuits; Cities and towns; Computer science; Conference management; Cybernetics; Machine learning; Machine learning algorithms; Sun; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1380766

Filename

1380766