Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search

  • Frank Neumann
  • Dirk Sudholt
  • Carsten Witt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)


Ant colony optimization (ACO) is a metaheuristic that produces good results for a wide range of combinatorial optimization problems. Often such successful applications use a combination of ACO and local search procedures that improve the solutions constructed by the ants. In this paper, we study this combination from a theoretical point of view and point out situations where introducing local search into an ACO algorithm enhances the optimization process significantly. On the other hand, we illustrate the drawback that such a combination might have by showing that this may prevent an ACO algorithm from obtaining optimal solutions.


Local Search Global Optimum Local Optimum Rigorous Analysis Search Point 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  2. 2.
    Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations & Applications. Elsevier/Morgan Kaufmann (2004)Google Scholar
  3. 3.
    Levine, J., Ducatelle, F.: Ant colony optimisation and local search for bin packing and cutting stock problems. Journal of the Operational Research Society (2004)Google Scholar
  4. 4.
    Balaprakash, P., Birattari, M., Stützle, T., Dorigo, M.: Incremental local search in ant colony optimization: Why it fails for the quadratic assignment problem. In: Proc. of ANTS Workshop 2006, pp. 156–166 (2006)Google Scholar
  5. 5.
    Merkle, D., Middendorf, M.: Modeling the dynamics of ant colony optimization. Evolutionary Computation 10, 235–262 (2002)CrossRefGoogle Scholar
  6. 6.
    Gutjahr, W.J.: On the finite-time dynamics of ant colony optimization. Methodology and Computing in Applied Probability 8, 105–133 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Stützle, T., Hoos, H.H.: MAX-MIN ant system. Journal of Future Generation Computer Systems 16, 889–914 (2000)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Gutjahr, W.J., Sebastiani, G.: Runtime analysis of ant colony optimization with best-so-far reinforcement. Methodology and Computing in Applied Probability (to appear, 2008)Google Scholar
  10. 10.
    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
  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.
    Gutjahr, W.J.: First steps to the runtime complexity analysis of ant colony optimization. Computers and Operations Research 35(9), 2711–2727 (2008)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Frank Neumann
    • 1
  • Dirk Sudholt
    • 2
  • Carsten Witt
    • 2
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.Informatik 2Technische Universität DortmundDortmundGermany

Personalised recommendations