Solving Traveling Salesman Problem by Ant Colony Optimization Algorithm with Association Rule

Author

Shang, Gao ; Lei, Zhang ; Fengting, Zhuang ; Chunxian, Zhang

Author_Institution

Jiangsu Univ. of Sci. & Technol., Zhenjiang

Volume

3

fYear

2007

Firstpage

693

Lastpage

698

Abstract

The traveling salesman problem (TSP) is among the most important combinatorial problems. Ant colony optimization (ACO) algorithm is a recently developed algorithm which has been successfully applied to several NP-hard problems, such as traveling salesman problem, quadratic assignment problem and job-shop problem. Association rule (AR) is the key in knowledge in data mining for finding the best data sequence. A new algorithm which integrates ACO and AR is proposed to solve TSP problems. Compare with the simulated annealing algorithm, the standard genetic algorithm and the standard ant colony algorithm, the new algorithm is better than ACO.

Keywords

combinatorial mathematics; data mining; travelling salesman problems; NP-hard problems; ant colony optimization algorithm; association rule; combinatorial problems; data mining; data sequence; job-shop problem; quadratic assignment problem; traveling salesman problem; Ant colony optimization; Association rules; Cities and towns; Data mining; Euclidean distance; Genetic algorithms; Joining processes; NP-hard problem; Simulated annealing; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2007. ICNC 2007. Third International Conference on

Conference_Location

Haikou

Print_ISBN

978-0-7695-2875-5

Type

conf

DOI

10.1109/ICNC.2007.675

Filename

4344600