Aplicación del algoritmo de optimización por colonia de hormigas (aco) en la optimización de rutas urbanas: un estudio de caso in silico
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Date
2026, 25/12/2025
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Abstract
This study examined how the Ant Colony Optimization (ACO) algorithm could be used to improve urban route planning, using a simulation based on the classic Traveling Salesman Problem (TSP). Four city locations were represented using Cartesian coordinates, and the distances between them were calculated with the Euclidean formula. The experiment was conducted in Python 3.13.0, applying NetworkX and Matplotlib for modeling and visualization. The ACO algorithm was configured with four ants and ten iterations, using standard parameters such as pheromone importance, evaporation rate, and heuristic influence. The results showed that the ants generated distinct routes. The Argel Velez Villagomez Escuela de Hábitat, Infraestructura y Creatividad Pontificia Universidad Católica del Ecuador, sede Santo Domingo Santo Domingo, Ecuador aavelezv@pucesd.edu.ec Andy Alava Gomez Escuela de Hábitat, Infraestructura y Creatividad Pontificia Universidad Católica del Ecuador, sede Santo Domingo Santo Domingo, Ecuador agalava@pucesd.edu.ec Optimization, Traveling Salesman Problem (TSP) and Urban Transportation Planning. I. INTRODUCTION most efficient path—C4, C3, C1, C2, C4—was identified early in the process, with a total length of 241.45 km. The variability in the solutions highlighted ACO’s ability to explore multiple possibilities within the search space. Compared to other heuristic techniques like Genetic Algorithms, Tabu Search, or Simulated Annealing, ACO showed a more flexible and stable performance, particularly in dynamic urban environments. These qualities suggest that ACO could be a valuable approach for tackling routing problems in cities, especially when conditions such as traffic or delivery constraints are in constant change. While results were promising, future research is encouraged to evaluate ACO in more complex urban scenarios and explore how tuning its parameters might enhance its performance in real-world applications.
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Keywords
Colony, Optimization, Metaheuristic
Citation
Vera, F. M., Vizuete, A. M., Villagomez, A. V., Araque, S. T., Cedeño, K. C., Gomez, A. A., & Posligua, A. R. (2025). Application of the Ant Colony Optimization algorithm (ACO) in urban route optimization: An in silico case study. In ETCM 2025 - 9th Ecuador Technical Chapters Meeting (ETCM 2025 - 9th Ecuador Technical Chapters Meeting). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ETCM67548.2025.11304304