Abstract
This paper deals with Ant Colony Optimization (ACO) applied to the Travelling Salesman Problem (TSP). TSP is a well-known combinatorial problem which aim is to find the shortest path between a designated set of nodes. ACO is an algorithm inspired by the natural behavior of ants. When travelling from the nest to a food source, ants leave pheromones behind. This algorithm can be applied to TSP in order to find the shortest path. In this paper, the variants of ACO are shortly explained and a new Improved Ant Colony Optimization (IACO) algorithm is proposed. The IACO is applied to small-sized TSP. It is shown that the proposed IACO performs better in some cases, especially when there are more cities in the TSP.
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Subaskaran, A., Krähemann, M., Hanne, T., Dornberger, R. (2022). Comparison of Ant Colony Optimization Algorithms for Small-Sized Travelling Salesman Problems. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_2
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