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META HEURÍSTICA BASADA EN COLONIA DE HORMIGAS EN N ETAPAS PARA PROBLEMAS DE ALTA DIMENSIÓN.

Abstract:

Ant Colony Optimization is a population metaheuristic inspired by the behavior of natural ants, specifically their ability to find the shortest path between their nest and the food source. This search mechanism has been tested in discrete problems, establishing itself as a good option for this field of application. In previous works, it was shown that dividing the exploration process of these algorithms into 2 stages considerably improves their performance in terms of time and the quality of the results. In this context, we present, in this work, a generalization of the exploitation process by stages for instances of the Medium and High-Dimension of the Traveling Salesman Problem. For the tests, 5 instances of different sizes were selected and 4 variants of the algorithm were analyzed. The results corroborated that the process of division into stages is good for the performance of the algorithm, reaching the best results with 4 stages.