COMPARATIVE PERFORMANCE OF THREE METAHEURISTIC APPROACHES FOR THE MAXIMALLY DIVERSE GROUPING PROBLEM

Gintaras Palubeckis, Eimutis Karčiauskas, Aleksas Riškus

Abstract


Given a set of elements and a symmetric matrix representing dissimilarities between them, the maximallydiverse grouping problem asks to find a partitioning of the elements into a fixed number of restricted size-groups such that thesum 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 experimentsthat compare the performance of these algorithms on benchmark instances of size up to 2000 elements.

http://dx.doi.org/10.5755/j01.itc.40.4.977


Keywords


combinatorial optimization; maximally diverse grouping; metaheuristics; simulated annealing; genetic algorithm; variable neighborhood search

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