Ant colony optimisation, like all other meta-heuristic search processes, requires a set of parameters in order to solve combinatorial problems. These parameters are often tuned by hand by the researcher to a set that seems to work well for the problem under study or a standard set from the literature. However, it is possible to integrate a parameter search process within the running of the meta-heuristic without incurring an undue computational overhead. In this paper, ant colony optimisation is used to evolve suitable parameter values (using its own optimisation processes) while it is solving combinatorial problems. The results reveal for the travelling salesman and quadratic assignment problems that the use of the augmented solver generally performs well against one that uses a standard set of parameter values. This is attributed to the fact that parameter values suitable for the particular problem instance can be automatically derived and varied throughout the search process.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Burkard, R., Karisch, S., Rendl, F.: QAPLIB - A quadratic assignment problem library. Journal of Global Optimization 10, 391–403 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Colorni, A., Dorigo, M., Maniezzo, V.: An investigation of some properties of an “Ant algorithm”. In: Palallel Problem Solving from Nature Conference (PPSN 1992), Brussels, Belgium, pp. 509–520. Elsevier Publishing, Amsterdam (1992)Google Scholar
  3. 3.
    Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, London (1999)Google Scholar
  4. 4.
    Dorigo, M., Gambardella, L.: A study of some properties of Ant-Q. In: Proceedings of PPSN IV - Fourth International Conference on Parallel Problem Solving From Nature, Berlin, Germany, Springer, Heidelberg (1996)Google Scholar
  5. 5.
    Dorigo, M., Gambardella, L.: Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  6. 6.
    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), 1–13 (1996)Google Scholar
  7. 7.
    Hendtlass, T., Randall, M.: A survey of ant colony and particle swarm metaheuristics and their application to discrete optimisation problems. In: Proceedings of the Inaugual Workshop on Artificial Life, Adelaide, Australia, pp. 15–25 (2001)Google Scholar
  8. 8.
    Ingber, L.: Simulated annealing: Practice versus theory. Computer Modelling 18, 29–57 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Maniezzo, V., Colorni, A.: The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering 11(5), 769–778 (1999)CrossRefGoogle Scholar
  10. 10.
    Pilat, M., White, T.: Using genetic algorithms to optimize ACS-TSP. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 282–287. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Randall, M., Abramson, D.: A general meta-heuristic solver for combinatorial optimisation problems. Journal of Computational Optimization and Applications 20, 185–210 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Reinelt, G.: TSPLIB - A traveling salesman problem library. ORSA Journal on Computing 3, 376–384 (1991)zbMATHGoogle Scholar
  13. 13.
    Shmygelska, A., Aguirre-Hernandez, R., Hoos, H.: An ant colony optimization algorithm for the 2D HP protein folding problem. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 40–52. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Stützle, T., Dorigo, M.: ACO algorithms for the quadratic assignment problem. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 33–50. McGraw-Hill, London (1999)Google Scholar
  15. 15.
    Stützle, T., Hoos, H.: The MAX − MIN Ant System and local search for combinatorial optimization problems. In: Voss, S., Martello, S., Osman, I., Roucairol, C. (eds.) Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pp. 313–329. Kluwer Academic Publishers, Boston (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Marcus Randall
    • 1
  1. 1.Meta-heuristic Search GroupBond UniversityAustralia

Personalised recommendations