A new method for solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques

Author

Chen, Shyi-Ming ; Chien, Chih-yao

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan

Volume

5

fYear

2010

fDate

11-14 July 2010

Firstpage

2477

Lastpage

2482

Abstract

In this paper, we present a new method for solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques. We also make experiments using the 25 data sets obtained from the TSPLIB and compare the experimental results of the proposed method with the existing methods. The experimental results show that both the average solution and the percentage deviation of the found average solution to the best known solution of the proposed method are better than the existing methods.

Keywords

genetic algorithms; particle swarm optimisation; simulated annealing; travelling salesman problems; ant colony system; genetic simulated annealing; particle swarm optimization; traveling salesman problem; Biological cells; Cities and towns; Genetics; Machine learning; Particle swarm optimization; Simulated annealing; Traveling salesman problems; Ant colony system; Genetic algorithms; Genetic simulated annealing ant colony system with particle swarm optimization techniques; Particle swarm optimization; Simulated annealing; Traveling salesman problem;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2010 International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4244-6526-2

Type

conf

DOI

10.1109/ICMLC.2010.5580809

Filename

5580809