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Computational Intelligence and Neuroscience
Volume 2016, Article ID 8932896, 13 pages
http://dx.doi.org/10.1155/2016/8932896
Research Article

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

Department of Computer Science, Faculty of Computing and Information Technology, University of Science and Technology, Sana’a, Yemen

Received 22 June 2016; Revised 15 October 2016; Accepted 19 October 2016

Academic Editor: Elio Masciari

Copyright © 2016 Abdulqader M. Mohsen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.