Abstract
Ant colony optimization (ACO) is a metaheuristic that produces good results for a wide range of combinatorial optimization problems. Often such successful applications use a combination of ACO and local search procedures that improve the solutions constructed by the ants. In this paper, we study this combination from a theoretical point of view and point out situations where introducing local search into an ACO algorithm enhances the optimization process significantly. On the other hand, we illustrate the drawback that such a combination might have by showing that this may prevent an ACO algorithm from obtaining optimal solutions.
The work of D. Sudholt and of C. Witt was supported by the Deutsche Forschungsgemeinschaft (DFG) as a part of the Collaborative Research Center “Computational Intelligence” (SFB 531).
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Neumann, F., Sudholt, D., Witt, C. (2008). Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_12
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DOI: https://doi.org/10.1007/978-3-540-87527-7_12
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