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Quantification-Based Ant Colony System for TSP

  • Ming Zhao
  • Jeng-Shyang Pan
  • Chun-Wei Lin
  • Lijun Yan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 238)

Abstract

Ant Colony Optimization (ACO) is one of the swarm intelligent methods for solving computational problems, especially in finding the optimal paths through graphs. In the past, floating point is widely used to represent the pheromone in ACO, thus requiring large amounts of memory to find the optimal solutions. In this paper, the quantification-based ACS (QACS) is thus proposed to reduce the space complexity. New updating rules of pheromone with no decay parameters are also designed in the proposed QACS for simplifying the updating processing of pheromone. Based on the experimental results of proposed QACS, the convergence rate can be improved with less memory space for solving Traveling Salesman Problem (TSP).

Keywords

Ant colony Optimization pheromone updating rule Qualificationbased TSP 

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References

  1. 1.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the European Conference on Artificial Life, Paris, France, pp. 134–142 (1991)Google Scholar
  2. 2.
    Bullnheimer, B., Hartl, R., Strauss, C.: An Improved Ant System Algorithm for the Vehicle Routing Problem. Annals of Operations Research 89, 319–328 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bullnheimer, B., Hartl, R., Strauss, C.: A new rank-based version of the ant system: a computational study. Central European Journal for Operations Research and Economics 7(1), 25–38 (1999)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Nezamabadi-pour, H., Rashedi, S.: Edge detection using ant algorithms. Soft Computing 10(7), 623–628 (2006)CrossRefGoogle Scholar
  5. 5.
    Yan, H., Shen, X.Q., Li, X., Wu, M.H.: An improved ant algorithm for job scheduling in grid computing. Machine Learning and Cybernetics, 2957–2961 (2005)Google Scholar
  6. 6.
  7. 7.
    Zhang, J., Chen, W.-N., Zhong, J.-H., Tan, X., Li, Y.: Continuous Function Optimization Using Hybrid Ant Colony Approach with Orthogonal Design Scheme. In: Wang, T.-D., Li, X., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 126–133. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Abbaspour, K.C., Schulin, R., Van Genuchten, M.T.: Estimating unsaturated soil hydraulic parameters using ant colony optimization. Advances In Water Resources 24(8), 827–841 (2001)CrossRefGoogle Scholar
  9. 9.
    Wang, L., Wu, Q.D.: Linear system parameters identification based on ant system algorithm. In: Proceedings of the IEEE Conference on Control Applications, Mexico, pp. 401–406 (2001)Google Scholar
  10. 10.
    Dorigo, M.: Optimization, learning, and natural algorithms. Ph.D. Thesis, Dip. Elettronica, Politecnico di Milano, Italy (1992)Google Scholar
  11. 11.
    Dorigo, M., Luca Gambardella, M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  12. 12.
    Dorigo, M., Stutzle, T.: Ant colony optimization. The MIT Press, London (2004)CrossRefzbMATHGoogle Scholar
  13. 13.
    Dorigo, M., Maniezzo, V.: Ant system for job-shop scheduling. Belgian Journal of Operations Research, Statistics and Computer Science 34, 39–53 (1994)zbMATHGoogle Scholar
  14. 14.
    Luca Gambardella, M., Dorigo, M.: Ant-Q: A Reinforcement Learning approach to the traveling salesman problem. In: Proceedings of the International Conference on Machine Learning, Tahoe, California, pp. 252–260 (1995)Google Scholar
  15. 15.
    Luca Gambardella, M., Taillard, E., Agazzi, G.: A multiple ant system for vehicle routing problem with time windows. New Ideas on Optimization, 285–296 (1999)Google Scholar
  16. 16.
    David, P., Matthieu, C., Arnaud, R.: Image Retrieval over Networks: Active Learning using Ant Algorithm. IEEE Transactions on Multimedia 10(7), 1356–1365 (2008)CrossRefGoogle Scholar
  17. 17.
    Stützle, T., Hoosb, H.: MAX–MIN Ant System and Local search for the traveling salesman problem. In: Proceedings of the IEEE International Conference on Evolutionary Computation, Pistcataway, USA, pp. 309–314 (1997)Google Scholar
  18. 18.
    Stützle, T., Hoosb, H.: MAX–MIN Ant System. Future Generation Computer Systems 16(8), 927–935 (2000)CrossRefGoogle Scholar
  19. 19.
    Hu, X.M., Zhang, J., Xiao, J., Li, Y.: Protein Folding in Hydrophobic-Polar Lattice Model: A Flexible Ant-Colony Optimization Approach. Protein and Peptide Letters 15(5), 469–477 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ming Zhao
    • 1
  • Jeng-Shyang Pan
    • 1
  • Chun-Wei Lin
    • 1
  • Lijun Yan
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolHarbinChina

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