A Population-Based Algorithm for Solving the Fuzzy Capacitated Maximal Covering Location Problem

In this paper, we investigate the use of a population-based algorithm for tackling the fuzzy capacitated maximal covering location problem. Such a problem is characterized by a set of customers with their distances and its goal is to determine a subset of locations positioned on customers such that a maximum coverage of customers, including the both fuzzy coverage degree of facilities and the distance between customers, should be optimized. The proposed method is based upon the grey wolf optimizer, which starts by generating an initial population using a greedy rule strategy that is able to achieve feasible solutions according to the current positions of wolves. In order to enhance the quality of solutions induced, a series of local searches are added for exploring the search space by exploiting some nice strategies. The behavior of the method is computationally analyzed on a set of instances of the literature. Encouraging results have been provided.