Swarm Intelligence

, Volume 6, Issue 1, pp 1–21 | Cite as

Theoretical analysis of two ACO approaches for the traveling salesman problem

  • Timo KötzingEmail author
  • Frank Neumann
  • Heiko Röglin
  • Carsten Witt


Bioinspired algorithms, such as evolutionary algorithms and ant colony optimization, are widely used for different combinatorial optimization problems. These algorithms rely heavily on the use of randomness and are hard to understand from a theoretical point of view. This paper contributes to the theoretical analysis of ant colony optimization and studies this type of algorithm on one of the most prominent combinatorial optimization problems, namely the traveling salesperson problem (TSP). We present a new construction graph and show that it has a stronger local property than one commonly used for constructing solutions of the TSP. The rigorous runtime analysis for two ant colony optimization algorithms, based on these two construction procedures, shows that they lead to good approximation in expected polynomial time on random instances. Furthermore, we point out in which situations our algorithms get trapped in local optima and show where the use of the right amount of heuristic information is provably beneficial.


Ant colony optimization Traveling salesperson problem Run time analysis Approximation 


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Copyright information

© Springer Science + Business Media, LLC 2011

Authors and Affiliations

  • Timo Kötzing
    • 1
    Email author
  • Frank Neumann
    • 2
  • Heiko Röglin
    • 3
  • Carsten Witt
    • 4
  1. 1.Algorithms and Complexity, Max-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.School of Computer ScienceUniversity of AdelaideAdelaideAustralia
  3. 3.Department of Computer ScienceUniversity of BonnBonnGermany
  4. 4.DTU InformaticsTechnical University of DenmarkKgs. LyngbyDenmark

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