Ant system for the set covering problem

Ant colony optimization (ACO) is a metaheuristic inspired by the biological behavior of Argentine ants, which cooperate with each other by means of pheromone deposit and evaporation. The ant system (AS), one of the instances of this general optimization approach, has been applied to different optimization problems. However, the feasibility of the AS has not been experimentally evaluated on an important category of the facility location problem, called the set covering problem (SCP). The unique application of ACO to SCP reported by literature (Hadji et al., 2000) used ant colonies combined with a local search. However its evaluation was not designed to provide more general conclusive results. For this reason, this paper not only worked with a non-hybrid ACO algorithm, but also adopted a more accurate experimental evaluation method, where a descriptive analysis comes before a comparative evaluation