• May 03, 2016 News! IJIET Vol. 5, No. 10 has been indexed by EI (Inspec).   [Click]
  • Jun 28, 2017 News!Vol. 7, No. 9 has been indexed by Crossref.
  • Jun 22, 2017 News!Vol. 7, No. 9 issue has been published online!   [Click]
General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2011 Vol.1(5): 404-409 ISSN: 2010-3689
DOI: 10.7763/IJIET.2011.V1.67

Solving Traveling Salesman Problem by Using Improved Ant Colony Optimization Algorithm

Zar Chi Su Su Hlaing and May Aye Khine

Abstract—Ant colony optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization problems and is taken as one of the high performance computing methods for Traveling salesman problem (TSP). TSP is one of the most famous combinatorial optimization (CO) problems and which has wide application background.. ACO has very good search capability for optimization problems, but it still remains a computational bottleneck that the ACO algorithm costs too much time to convergence and traps in local optima in order to find an optimal solution for TSP problems. The presented paper proposes an improved ant colony optimization algorithm with two highlights. First, candidate set strategy is adopted to rapid convergence speed. Second, a dynamic updating rule for heuristic parameter based on entropy to improve the performance in solving TSP. Algorithms are tested on benchmark problems from TSPLIB and test results are presented. From our experiments, the proposed algorithm has better performance than the conventional ACO algorithm and the results of the proposed algorithms are found to be satisfactory.

Index Terms—Ant colony optimization, entropy, traveling salesman problem

Authors are with the University of Computer Studies, Yangon, Myanmar (e-mail: zarchisusuhlaing@ gmail.com).

[PDF]

Cite: Zar Chi Su Su Hlaing and May Aye Khine, "Solving Traveling Salesman Problem by Using Improved Ant Colony Optimization Algorithm," International Journal of Information and Education Technology vol. 1, no. 5, pp. 404-409, 2011.

Copyright © 2008-2017. International Journal of Information and Education Technology. All rights reserved.
E-mail: ijiet@ejournal.net