Algorithmica

, 54:243 | Cite as

Runtime Analysis of a Simple Ant Colony Optimization Algorithm

Open Access
Article

Abstract

Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical foundation of this randomized search heuristic is rather weak. Building up such a theory is demanded to understand how these heuristics work as well as to come up with better algorithms for certain problems. Up to now, only convergence results have been achieved showing that optimal solutions can be obtained in finite time. We present the first runtime analysis of an ACO algorithm, which transfers many rigorous results with respect to the runtime of a simple evolutionary algorithm to our algorithm. Moreover, we examine the choice of the evaporation factor, a crucial parameter in ACO algorithms, in detail. By deriving new lower bounds on the tails of sums of independent Poisson trials, we determine the effect of the evaporation factor almost completely and prove a phase transition from exponential to polynomial runtime.

Keywords

Randomized search heuristics Ant colony optimization Runtime analysis 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Algorithms and ComplexityMax-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.FB Informatik, LS 2Universität DortmundDortmundGermany

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