A new approach for solving large traveling salesman problem using evolutionary ant rules

Author

Tsai, Cheng-Fa ; Tsai, Chun-Wei

Author_Institution

Dept. of Manage. Inf. Syst., Nat. Pingtung Univ. of Sci. & Technol., Taiwan

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1540

Lastpage

1545

Abstract

This paper presents a new metaheuristic method called EA algorithm for solving the TSP (traveling salesman problem). We introduce a genetic exploitation mechanism in ant colony system from genetic algorithm to search solutions space for solving the traveling salesman problem. In addition, we present a method called nearest neighbor (NN) to EA to improve TSPs thus obtain good solutions quickly. According to our simulation results, the EA algorithm outperforms the ant colony system (ACS) in tour length comparison of traveling salesman problem. In this work it is observed that EA or ACS with NN approach as initial solutions can provide a significant improvement for obtaining a global optimum solution or a near global optimum solution in large TSPs

Keywords

genetic algorithms; search problems; travelling salesman problems; evolutionary ant rules; genetic exploitation mechanism; global optimum solution; metaheuristic method; near global optimum solution; search solutions space; simulation results; tour length comparison; traveling salesman problem; Ant colony optimization; Cities and towns; Genetic algorithms; Management information systems; Nearest neighbor searches; Neural networks; Partitioning algorithms; Space exploration; Space technology; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007746

Filename

1007746