JACIII Vol.11 No.4 pp. 433-442
doi: 10.20965/jaciii.2007.p0433


Improving Ant Colony Optimization Algorithms for Solving Traveling Salesman Problems

Kuo-Sheng Hung*, Shun-Feng Su*,**, and Zne-Jung Lee***

*Dept. of Electrical Eng., National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd. Taipei, 106, Taiwan

**Dept. of Electrical Eng., National Taipei University of Technology, No. 1, Sec. 3, Chung-Hsiao E. Rd., Taipei, 106, Taiwan

***Dept. of Information Management, Huafan University, Taipei, Taiwan

April 17, 2006
September 5, 2006
April 20, 2007
ant colony optimization, traveling salesman problems, entropy

Ant colony optimization (ACO) has been successfully applied to solve combinatorial optimization problems, but it still has some drawbacks such as stagnation behavior, long computational time, and premature convergence. These drawbacks will be more evident when the problem size increases. In this paper, we reported the analysis of using a lower pheromone trail bound and a dynamic updating rule for the heuristic parameters based on entropy to improve the efficiency of ACO in solving Traveling Salesman Problems (TSPs). TSPs are NP-hard problem. Even though the problem itself is simple, when the number of city is large, the search space will become extremely large and it becomes very difficult to find the optimal solution in a short time. From our experiments, it can be found that the proposed algorithm indeed has superior search performance over traditional ACO algorithms do.

Cite this article as:
Kuo-Sheng Hung, Shun-Feng Su, and Zne-Jung Lee, “Improving Ant Colony Optimization Algorithms for Solving Traveling Salesman Problems,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.4, pp. 433-442, 2007.
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