Abstract
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.
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References
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)
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)
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)
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)
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)
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)
Yang, J., Shi, X., Marchese, M., et al.: An ant colony optimization method for generalized TSP problem. Prog. Nat. Sci. 18, 1417–1422 (2008)
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)
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)
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)
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
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)
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Deng, X., Zhang, L., Feng, J. (2017). An Improved Ant Colony Optimization with Subpath-Based Pheromone Modification Strategy. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_28
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DOI: https://doi.org/10.1007/978-3-319-61824-1_28
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