Abstract
Ant Colony Optimization has proven to be an efficient optimization technique for solving difficult optimization problems. Nonetheless, the convergence of the ACO can still be prohibitively slow. We investigate how the recently proposed Restart Procedure (RP) can be used to improve convergence of the Ant Colony System (ACS) algorithm, which is among the most often applied algorithms from the ACO family. In particular, we present a series of computational experiments to answer the question about how the values of the RP-related parameters influence the convergence of the ACS combined with the RP (ACS-RP). We also show that the ACS-RP achieves significantly better results than the standard ACS within the same computational budget.
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This research was supported in part by PL-Grid Infrastructure.
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Skinderowicz, R. (2016). Ant Colony System with a Restart Procedure for TSP. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_9
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DOI: https://doi.org/10.1007/978-3-319-45246-3_9
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