The Influence of α and β Parameters on Ant Colony Optimization for the Traveling Salesman Problem

Zenodo metadata for ACO article Resource type:Publication Title:The Influence of α and β Parameters on Ant Colony Optimization for the Traveling Salesman Problem Publication date:2026-09-16 Creator:Family name: KotserubaGiven name: Ivan Description / Abstract:Ant Colony Optimization (ACO) is a population-based metaheuristic inspired by the collective behavior of ants searching for short paths. One of the main practical difficulties in applying ACO is the selection of appropriate parameter values. In particular, α controls the influence of accumulated pheromone information, while β controls the influence of distance. This study investigates how combinations of α and β affect the quality, stability, and convergence of ACO solutions for the Traveling Salesman Problem (TSP). Experiments were performed on uniformly generated and clustered graph instances. The results indicate that β generally has a stronger influence on route quality than α, although overly strong distance preference can reduce the effectiveness of the search on structured instances. The experiments support the broader conclusion that parameter tuning should take the structure of the optimization problem into account. Keywords:Ant Colony OptimizationTraveling Salesman Problemswarm intelligencecombinatorial optimizationparameter tuning Language:English License:Creative Commons Attribution 4.0 International (CC BY 4.0) Publisher:Zenodo Related identifier:https://github.com/kotsub/MVS_projectRelation: Is supplemented by / Is documented by (choose the closest available relation) Access:Open / Public DOI:No existing DOI. Let Zenodo assign one on publication.