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.
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A preliminary version of this paper appeared in the Proceedings of the 17th International Symposium on Algorithms and Computation (ISAAC 2006), volume 4288 of LNCS, pp. 618–627, Springer.
Financial support for C. Witt by the Deutsche Forschungsgemeinschaft (SFB) in terms of the Collaborative Research Center “Computational Intelligence” (SFB 531) is gratefully acknowledged.
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Neumann, F., Witt, C. Runtime Analysis of a Simple Ant Colony Optimization Algorithm. Algorithmica 54, 243–255 (2009). https://doi.org/10.1007/s00453-007-9134-2
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DOI: https://doi.org/10.1007/s00453-007-9134-2
Keywords
- Randomized search heuristics
- Ant colony optimization
- Runtime analysis