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Iterated greedy algorithms for the maximal covering location problem. (English) Zbl 1292.90173
Hao, Jin-Kao (ed.) et al., Evolutionary computation in combinatorial optimization. 12th European conference, EvoCOP 2012, Málaga, Spain, April 11–13, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-29123-4/pbk). Lecture Notes in Computer Science 7245, 172-181 (2012).
Summary: 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.
For the entire collection see [Zbl 06026017].

MSC:
90B80 Discrete location and assignment
90C59 Approximation methods and heuristics in mathematical programming
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