Evolutionary Computation in Combinatorial Optimization

Volume 7245 of the series Lecture Notes in Computer Science pp 172-181

Iterated Greedy Algorithms for the Maximal Covering Location Problem

  • Francisco J. RodriguezAffiliated withDepartment of Computer Science and Artificial Intelligence, University of Granada
  • , Christian BlumAffiliated withALBCOM Research Group, Technical University of Catalonia
  • , Manuel LozanoAffiliated withDepartment of Computer Science and Artificial Intelligence, University of Granada
  • , Carlos García-MartínezAffiliated withDepartment of Computing and Numerical Analysis, University of Córdoba

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The problem of allocating a set of facilities in order to maximise the sum of the demands of the covered clients is known as the maximal covering location problem. In this work we tackle this problem by means of iterated greedy algorithms. These algorithms iteratively refine a solution by partial destruction and reconstruction, using a greedy constructive procedure. Iterated greedy algorithms have been applied successfully to solve a considerable number of problems. With the aim of providing additional results and insights along this line of research, this paper proposes two new iterated greedy algorithms that incorporate two innovative components: a population of solutions optimised in parallel by the iterated greedy algorithm, and an improvement procedure that explores a large neighbourhood by means of an exact solver. The benefits of the proposal in comparison to a recently proposed decomposition heuristic and a standalone exact solver are experimentally shown.


iterated greedy algorithm large neighbourhood search maximal covering location problem