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How Single Ant ACO Systems Optimize Pseudo-Boolean Functions

  • Benjamin Doerr
  • Daniel Johannsen
  • Ching Hoo Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

We undertake a rigorous experimental analysis of the optimization behavior of the two most studied single ant ACO systems on several pseudo-boolean functions. By tracking the behavior of the underlying random processes rather than just regarding the resulting optimization time, we gain additional insight into these systems. A main finding is that in those cases where the single ant ACO system performs well, it basically simulates the much simpler (1+1) evolutionary algorithm.

Keywords

Optimization Behavior Runtime Analysis Evaporation Factor Randomize Search Heuristic Expect Optimization Time 
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.
  2. 2.
    Doerr, B., Johannsen, D.: Refined runtime analysis of a basic ant colony optimization algorithm. In: Proc. of the CEC 2007, pp. 501–507. IEEE Press, Los Alamitos (2007)Google Scholar
  3. 3.
    Doerr, B., Neumann, F., Sudholt, D., Witt, C.: On the runtime analysis of the 1-ANT ACO algorithm. In: Proc. of GECCO 2007, pp. 33–40. ACM, New York (2007)Google Scholar
  4. 4.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: An autocatalytic optimizing process. Technical Report 91-016 Revised, Politecnico di Milano (1991)Google Scholar
  5. 5.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  6. 6.
    Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. TCS 276, 51–81 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Gutjahr, W.J.: First steps to the runtime complexity analysis of ant colony optimization. Comput. Oper. Res. 35, 2711–2727 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Gutjahr, W.J., Sebastiani, G.: Runtime analysis of ant colony optimization. Technical report, Mathematics department, Sapienza Univ. of Rome (2007)Google Scholar
  9. 9.
    Neumann, F., Sudholt, D., Witt, C.: Comparing variants of MMAS ACO algorithms on pseudo-boolean functions. In: Stützle, T., Birattari, M., H. Hoos, H. (eds.) SLS 2007. LNCS, vol. 4638, pp. 61–75. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Neumann, F., Sudholt, D., Witt, C.: Rigorous analyses for the combination of ant colony optimization and local search. In: van der Poorten, A.J., Stein, A. (eds.) ANTS-VIII 2008. LNCS, vol. 5011. Springer, Heidelberg (to appear, 2008)Google Scholar
  11. 11.
    Neumann, F., Witt, C.: Runtime analysis of a simple ant colony optimization algorithm. In: Asano, T. (ed.) ISAAC 2006. LNCS, vol. 4288, pp. 618–627. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Stützle, T., Hoos, H.: MAX–MIN ant system. Journal of Future Generation Computer Systems, 889–914 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Benjamin Doerr
    • 1
  • Daniel Johannsen
    • 1
  • Ching Hoo Tang
    • 1
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany

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