Authors: Brimberg, Jack
Mladenović, Nenad 
Urošević, Dragan 
Title: Maximally diverse grouping and clique partitioning problems with skewed general variable neighborhood search
Journal: Springer Proceedings in Mathematics and Statistics
Volume: 156
First page: 3
Last page: 38
Issue Date: 1-Jan-2016
Rank: M14
ISBN: 978-3-319-29606-7
ISSN: 2194-1009
DOI: 10.1007/978-3-319-29608-1_1
Abstract: 
The maximally diverse grouping problem (MDGP) requires finding a partition of a given set of elements into a fixed number of mutually disjoint subsets (or groups) in order to maximize the overall diversity between elements of the same group. The clique partitioning problem (CPP) has a similar form as the MDGP, but is defined as the minimization of dissimilarity of elements in an unknown number of groups. In this paper a new variant of variable neighborhood search referred to as skewed general variable neighborhood search (SGVNS) is used to solve both problems. Extensive computational results show that the developed heuristic significantly outperforms its competitors. This demonstrates the usefulness of a combined approach of diversification afforded with skewed VNS and intensification afforded with the local search in general VNS.
Keywords: Clique partitioning | Diverse grouping | Variable neighbourhood search
Publisher: Springer Link
Project: RSF grant 14-41-00039

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