Computational Complexity of Ant Colony Optimization and Its Hybridization with Local Search

  • Frank Neumann
  • Dirk Sudholt
  • Carsten Witt
Part of the Studies in Computational Intelligence book series (SCI, volume 248)


The computational complexity of ant colony optimization (ACO) is a new and rapidly growing research area. The finite-time dynamics of ACO algorithms is assessed with mathematical rigor using bounds on the (expected) time until an ACO algorithm finds a global optimum. We review previous results in this area and introduce the reader into common analysis methods. These techniques are then applied to obtain bounds for different ACO algorithms on classes of pseudo-Boolean problems. The resulting runtime bounds are further used to clarify important design issues from a theoretical perspective. We deal with the question whether the current best-so-far solution should be replaced by new solutions with the same quality. Afterwards, we discuss the hybridization of ACO with local search and present examples where introducing local search leads to a tremendous speed-up and to a dramatic loss in performance, respectively.


Local Search Success Probability Memetic Algorithm Search Point Construction Graph 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Frank Neumann
    • 1
  • Dirk Sudholt
    • 2
  • Carsten Witt
    • 3
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.Informatik 2, Technische Universität DortmundDortmundGermany
  3. 3.DTU Informatics, Technical University of DenmarkLyngbyDenmark

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