An Improved Ant Colony Optimization with Subpath-Based Pheromone Modification Strategy

  • Xiangyang Deng
  • Limin Zhang
  • Jiawen FengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)


The performance of an ACO depends extremely on the cognition of each subpath, which is represented by the pheromone trails. This paper designs an experiment to explore a subpath’s exact role in the full-path generation. It gives three factors, sequential similarity ratio (SSR), iterative best similarity ratio (IBSR) and global best similarity ratio (GBSR), to evaluate some selected subpaths called r-rank subpaths in each iteration. The result shows that r-rank subpaths keep a rather stable proportion in the found best route. And then, by counting the crossed ants of a subpath in each iteration, a subpath-based pheromone modification rule is proposed to enhance the pheromone depositing strategy. It is combined with the iteration-best pheromone update rule to solve the traveling salesman problem (TSP), and experiments show that the new ACO has a good performance and robustness.


Ant colony optimization Subpath-based pheromone modification strategy Travel salesman problem Meta-heuristic algorithm Pheromone trails 


  1. 1.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the 1st European Conference on Artificial Life, Paris, pp. 134–142 (1991)Google Scholar
  2. 2.
    Stützle, T., Hoos, H.H.: The MAX-MIN ant system and local search for the traveling salesman problem. In: Back, T., Michalewicz, Z., Yao, X. (eds.) Proceedings of the 1997 IEEE International Conference on Evolutionary Computation (ICEC 1997), pp. 309–314 (1997)Google Scholar
  3. 3.
    Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank-based version of the ant system: a computational study. Central Eur. J. Oper. Res. Econ. 7(1), 25–38 (1999)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Huang, L., Wang, K., Zhou, C., et al.: Hybrid approach based on ant algorithm for solving traveling salesman problem. J. Jilin Univ. (Sci. Ed.) 40(4), 369–373 (2002)zbMATHGoogle Scholar
  5. 5.
    Hao, J., Shi, L., Zhou, J.: An ant system algorithm with random perturbation behavior for complex TSP problem. Syst. Eng.-Theory Pract. 9, 88–91 (2002)Google Scholar
  6. 6.
    Cheng, C.-B., Mao, C.-P.: A modfied ant colony system for solving the travelling salesman problem with time windows. Math. Comput. Model. 46, 1225–1235 (2007)CrossRefzbMATHGoogle Scholar
  7. 7.
    Yang, J., Shi, X., Marchese, M., et al.: An ant colony optimization method for generalized TSP problem. Prog. Nat. Sci. 18, 1417–1422 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Blum, C., Dorigo, M.: The hyper-cube framework for ant colony optimization. IEEE Trans. Syst. Man Cybern.-Part B. Also available as Technical report TR/IRIDIA/2003–03, IRIDIA, Universit Libre de Bruxelles, Belgium (2003)Google Scholar
  9. 9.
    Gambardella, L.M., Dorigo, M.: Ant-Q: a reinforcement learning approach to the traveling salesman problem. In: Proceedings of the 12th International Conference on Machine Learning, pp. 252–260 (1995)Google Scholar
  10. 10.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  11. 11.
    Deng, X., Zhang, L., Lin, H., et al.: Pheromone mark ant colony optimization with a hybrid node-based pheromone update strategy. Neurocomputing (in press).  10.1016/j.neucom.2012.12.084
  12. 12.
    Geng, J.Q., Weng, L.P., Liu, S.H.: An improved ant colony optimization algorithm for nonlinear resource-leveling problems. Comput. Math. Appl. 61, 2300–2305 (2011)CrossRefzbMATHGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Information FusionNaval Aeronautical and Astronautical UniversityYantaiChina
  2. 2.Institute of Electronic EngineeringNaval Engineering UniversityWuhanChina

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