Deception in Ant Colony Optimization

  • Christian Blum
  • Marco Dorigo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)


The search process of a metaheuristic is sometimes misled. This may be caused by features of the tackled problem instance, by features of the algorithm, or by the chosen solution representation. In the field of evolutionary computation, the first case is called deception and the second case is referred to as bias. In this work we formalize the notions of deception and bias for ant colony optimization. We formally define first order deception in ant colony optimization, which corresponds to deception as being described in evolutionary computation. Furthermore, we formally define second order deception in ant colony optimization, which corresponds to the bias introduced by components of the algorithm in evolutionary computation. We show by means of an example that second order deception is a potential problem in ant colony optimization algorithms.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blum, C.: Theoretical and practical aspects of ant colony optimization. PhD thesis, IRIDIA, Université Libre de Bruxelles, Belgium (2004)Google Scholar
  2. 2.
    Blum, C., Dorigo, M.: The hyper-cube framework for ant colony optimization. IEEE Trans. on Systems, Man, and Cybernetics – Part B 34(2), 1161–1172 (2004)CrossRefGoogle Scholar
  3. 3.
    Blum, C., Sampels, M.: Ant Colony Optimization for FOP shop scheduling: A case study on different pheromone representations. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), vol. 2, pp. 1558–1563. IEEE Computer Society Press, Los Alamitos (2002)CrossRefGoogle Scholar
  4. 4.
    Blum, C., Sampels, M.: When model bias is stronger than selection pressure. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 893–902. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Blum, C., Sampels, M., Zlochin, M.: On a particularity in model-based search. In: Langdon, W.B., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 35–42. Morgan Kaufmann Publishers, San Francisco (2002)Google Scholar
  6. 6.
    Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M.: Ant System for job-shop scheduling. JORBEL – Belgian Journal of Operations Research, Statistics and Computer Science 34(1), 39–53 (1994)zbMATHGoogle Scholar
  7. 7.
    Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms, vol. 2, pp. 93–108. Morgan Kaufmann, San Mateo (1993)Google Scholar
  8. 8.
    Dorigo, M.: Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Dip. di Elettronica, Politecnico di Milano, Italy (1992)Google Scholar
  9. 9.
    Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Technical Report 91-016, Dip. di Elettronica, Politecnico di Milano, Italy (1991)Google Scholar
  10. 10.
    Dorigo, M., Maniezzo, V., Colorni, A.: 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
  11. 11.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHCrossRefGoogle Scholar
  12. 12.
    Goldberg, D.E.: Simple genetic algorithms and the minimal deceptive problem. In: Davis, L. (ed.) Genetic algorithms and simulated annealing, Pitman, London, UK, pp. 74–88 (1987)Google Scholar
  13. 13.
    Merkle, D., Middendorf, M.: Modelling ACO: Composed permutation problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 149–162. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Merkle, D., Middendorf, M.: Modelling the dynamics of ant colony optimization algorithms. Evolutionary Computation 10(3), 235–262 (2002)CrossRefGoogle Scholar
  15. 15.
    Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job-shop problem. Management Science 42(2), 797–813 (1996)zbMATHCrossRefGoogle Scholar
  16. 16.
    Rothlauf, F., Goldberg, D.E.: Prüfer numbers and genetic algorithms: A lesson on how the low locality of an encoding can harm the performance of GAs. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 395–404. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Christian Blum
    • 1
  • Marco Dorigo
    • 1
  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium

Personalised recommendations