Comparative Performance of Three Metaheuristic Approaches for the maximally Diverse Grouping Problem

Given a set of elements and a symmetric matrix representing dissimilarities between them, the maximally diverse grouping problem asks to find a partitioning of the elements into a fixed number of restricted size-groups such that the sum of pairwise dissimilarities between elements in the same group is maximized. We present multistart simulated annealing, hybrid genetic and variable neighborhood search algorithms for solving this problem. We report on computational experiments that compare the performance of these algorithms on benchmark instances of size up to 2000 elements.