A max min ant system applied to the capacitated clustering problem

This work introduces a modified max min ant system (MMAS) designed to solve the capacitated clustering problem (CCP). Some improvements on the original MMAS algorithm are proposed, such as the use of a density model on the information heuristic and a local search adapted from the uncapacitated p-medians problem. Also the MMAS ability to deal with large scale instances is improved by means of a new proposal for the pheromone updating rule. Some simulations are performed using instances available from the literature, for benchmarking purposes. As a practical application, given a hypothetical demand proportional to the number of inhabitants of the 186 most populated Brazilian cities, the optimal allocation for a varied number of clustering centers is properly determined by the proposed algorithm, with a superior performance when compared with the original MMAS algorithm