The problem of allocating a set of facilities in order to maximisethe sum of the demands of the covered clients is known as themaximal covering location problem. In this work we tackle this problemby means of iterated greedy algorithms. These algorithms iteratively refinea solution by partial destruction and reconstruction, using a greedyconstructive procedure. Iterated greedy algorithms have been appliedsuccessfully to solve a considerable number of problems. With the aim ofproviding additional results and insights along this line of research, thispaper proposes two new iterated greedy algorithms that incorporate twoinnovative components: a population of solutions optimised in parallelby the iterated greedy algorithm, and an improvement procedure thatexplores a large neighbourhood by means of an exact solver. The benefitsof the proposal in comparison to a recently proposed decompositionheuristic and a standalone exact solver are experimentally shown.