A multi-group ant colony system algorithm for TSP

Author

Ouyang, Jun ; Yan, Gui-Rong

Author_Institution

Mechanical structure Strength & Vibration Laboratory, Xi´´an Jiaotong Univ., China

Volume

1

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

117

Abstract

As a new class of global searching algorithms, ant colony system algorithm could solve TSP (traveling salesman problem). These algorithms includes ACS, MAX-MIN ant system, et al. This work presents a new method named multi-group ant colony system algorithm. This algorithm avoids some defects of ACS and MAX-MIN ant system. These defects make algorithm not iterate when it has arrived at the stagnating state of the iteration or local optimum point. But for multi-group ant colony system, it creates new groups of ants to iterate when meeting those states. In this paper, a simple convergence proof is presented. At the end of this paper, the experimental result is presented to show the effectiveness of this method.

Keywords

convergence; travelling salesman problems; convergence proof; global searching algorithms; local optimum point; multi-group ant colony system algorithm; traveling salesman problem; Ant colony optimization; Chemicals; Computational modeling; Convergence; Cybernetics; Decision making; Laboratories; Machine learning; Traveling salesman problems; Vibrations;

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.2004.1380626

Filename

1380626