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Article Dans Une Revue European Journal of Operational Research Année : 2016

Iterated maxima search for the maximally diverse grouping problem

Résumé

The maximally diverse grouping problem (MDGP) is to partition the vertices of an edge-weighted and undirected complete graph into m groups such that the total weight of the groups is maximized subject to some group size constraints. MDGP is a NP-hard combinatorial problem with a number of relevant applications. In this paper, we present an innovative heuristic algorithm called iterated maxima search (IMS) algorithm for solving MDGP. The proposed approach employs a maxima search procedure that integrates organically an efficient local optimization method and a weak perturbation operator to reinforce the intensification of the search and a strong perturbation operator to diversify the search. Extensive experiments on five sets of 500 MDGP benchmark instances of the literature show that IMS competes favorably with the state-of-the-art algorithms. We provide additional experiments to shed light on the rationality of the proposed algorithm and investigate the role of the key ingredients.

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Dates et versions

hal-01412525 , version 1 (08-12-2016)

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Xiangjing Lai, Jin-Kao Hao. Iterated maxima search for the maximally diverse grouping problem. European Journal of Operational Research, 2016, 254 (3), pp.780-800. ⟨10.1016/j.ejor.2016.05.018⟩. ⟨hal-01412525⟩

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