Ant Colony Optimization and the Minimum Cut Problem

T Kötzing, Per Lehre, F Neumann, Pietro Oliveto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)


Ant Colony Optimization (ACO) is a powerful metaheuristic for solving combinatorial optimization problems. With this paper we contribute to the theoretical understanding of this kind of algorithm by investigating the classical minimum cut problem. An ACO algorithm similar to the one that was proved successful for the minimum spanning tree problem is studied. Using rigorous runtime analyses we show how the ACO algorithm behaves similarly to Karger and Stein's algorithm for the minimum cut problem as long as the use of pheromone values is limited. Hence optimal solutions are obtained in expected polynomial time. On the other hand, we show that high use of pheromones has a negative effect, and the ACO algorithm may get trapped in local optima resulting in an exponential runtime to obtain an optimal solution. This result indicates that ACO algorithms may be inappropriate for finding minimum cuts.
Original languageEnglish
Title of host publicationProceedings of the 12th annual conference on Genetic and evolutionary computation
Number of pages8
Publication statusPublished - 11 Jul 2010
EventProceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO '10) - New York, United States
Duration: 11 Jul 2010 → …


ConferenceProceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO '10)
Country/TerritoryUnited States
CityNew York
Period11/07/10 → …


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