Abstract

Metaheuristic algorithms are widely used for solving optimization problems, and their performance critically depends on parameter settings. The irace tool has been proposed as an efficient method for automatic parameter tuning, identifying effective configurations while managing computational resources. However, irace can suffer from premature convergence, of-ten evaluating similar parameter configurations. This work introduces an approach to enhance irace’s exploration using clustering algorithms. The strategy groups elite configurations based on their location in the parameter space and selects parents from these groups to guide the sampling of new configurations, extending irace’s exploratory behavior. The method was evaluated by tuning the Ant Colony Optimization framework in three scenarios: a homogeneous setting (TSP instances with 2000 cities), a slightly less homogeneous one (TSP instances …