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An Extension of Ant Colony System to Continuous Optimization Problems

  • Seid H. Pourtakdoust
  • Hadi Nobahari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)

Abstract

A new method for global minimization of continuous functions has been proposed based on Ant Colony Optimization. In contrast with the previous researches on continuous ant-based methods, the proposed scheme is purely pheromone-based. The algorithm has been applied to several standard test functions and the results are compared with those of two other meta-heuristics. The overall results are compatible, in good agreement and in some cases even better than the two other methods. In addition the proposed algorithm is much simpler, which is mainly due to its simpler structure. Also it has fewer control parameters, which makes the parameter settings process easier than many other methods.

Keywords

Travel Salesman Problem Continuous Optimization Problem Admissible Range Parameter Setting Process State Transition Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bilchev, G., Parmee, I.C.: The ant colony metaphor for searching continuous design spaces. In: Fogarty, T.C. (ed.) AISB-WS 1995. LNCS, vol. 993, pp. 25–39. Springer, Heidelberg (1995)Google Scholar
  2. 2.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial Life, pp. 134–142. Elsevier Science Publisher, Amsterdam (1992)Google Scholar
  3. 3.
    Dorigo, M.: Optimization, learning and natural algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, IT (1992)Google Scholar
  4. 4.
    Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant algorithms and stigmergy. Future Generation Computer Systems 16, 851–871 (2000)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(3), 137–172 (1999)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Gambardella, L.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
  7. 7.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics-Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  8. 8.
    Dreo, J., Siarry, P.: A new ant colony algorithm using the heterarchical concept aimed at optimization of multi-minima continuous functions. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 216–221. Springer, Heidelberg (2002)CrossRefGoogle 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, ML 1995, Palo Alto, pp. 252–260 (1995)Google Scholar
  10. 10.
    Jun, L.Y., Jun, W.T.: An adaptive ant colony system algorithm for continuousspace optimization problems. Journal of Zhejiang University Science 4(1), 40–46 (2003)CrossRefGoogle Scholar
  11. 11.
    Ling, C., Jie, S., Ling, O., Hongjian, C.: A method for solving optimization problems in continuous space using ant colony algorithm. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 288–289. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Michalewicz, Z.: Genetic Algorithms+Data Structures=Evolution Programs, 3rd edn. Springer, Berlin (1996)zbMATHGoogle Scholar
  13. 13.
    Monmarche, N., Venturini, G., Slimane, M.: On how the ants Pachycondyla apicalis suggesting a new search algorithm. Internal Report No. 214, E3i, Downloadable from website (1999), http://www.antsearch.univ-tours.fr/webrtic
  14. 14.
    Monmarche, N., Venturini, G., Slimane, M.: On how Pachycondyla apicalis ants suggest a new search algorithm. Future Generation Computer Systems 16, 937–946 (2000)CrossRefGoogle Scholar
  15. 15.
    Stützle, T., Hoos, H.: MAX-MIN Ant System. Future Generation System 16(8), 889–914 (2000)CrossRefGoogle Scholar
  16. 16.
    Whitley, D., Mathias, K., Rana, S., Dzubera, J.: Building better test functions. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 239–246. Morgan Kaufmann, San Francisco (1995)Google Scholar
  17. 17.
    Wodrich, M., Bilchev, G.: Cooperative distributed search: the ant’s way. Control and Cybernetics 26, 413–445 (1997)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Seid H. Pourtakdoust
    • 1
  • Hadi Nobahari
    • 1
  1. 1.Sharif University of TechnologyTehranIran

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