Abstract
The optimization of the Traveling Salesman Problem (TSP) is a widely studied combinatorial optimization problem with applications in transportation and logistics. This paper proposes an Ant Colony Optimization (ACO) algorithm for effectively solving the TSP. The approach combines a rank-based selection strategy that considers both the originality and fitness of solutions, along with a pheromone smoothing mechanism that diversifies the search, improving the algorithm’s performance. Additionally, the algorithm is coupled with a local search heuristic, which significantly enhances the obtained solutions. The experimental results indicate that the proposed algorithm outperforms well-known ACO variants, as well as recent hybridized ACO variants, across a range of benchmark TSP instances.
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Pérez-Carabaza, S., Gálvez, A., Iglesias, A. (2023). Extended Rank-Based Ant Colony Optimization Algorithm for Traveling Salesman Problem. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_2
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