Maximizing single attribute diversity in group selection

Sergey Kovalev (), Isabelle Chalamon () and Fabio J. Petani ()
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Sergey Kovalev: 25 rue de l’Université
Isabelle Chalamon: 25 rue de l’Université
Fabio J. Petani: 25 rue de l’Université

Annals of Operations Research, 2023, vol. 320, issue 1, No 21, 535-540

Abstract: Abstract The studied problem consists in selecting a group of k entities out of n entities such that their diversity is maximized. Each entity is assumed to be characterized by a single numerical attribute. The diversity is measured by the total pairwise Euclidean or squared Euclidean distance. The problem appears in the formation of social or working groups. Under certain conditions, diversity is perceived as a positive factor influencing the group’s effectiveness. We propose simple $$O(n+k\log k)$$ O ( n + k log k ) time algorithms to solve this problem for both the total Euclidean and squared Euclidean distances.

Keywords: Combinatorial optimization; Group selection; Maximum diversity; Euclidean distance; Polynomial algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-04764-7

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