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

Authors

  • Gintaras Palubeckis Kaunas University of Technology
  • Eimutis Karčiauskas Kaunas University of Technology
  • Aleksas Riškus Kaunas University of Technology

DOI:

https://doi.org/10.5755/j01.itc.40.4.977

Keywords:

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

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

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Published

2011-12-15

Issue

Vol. 40 No. 4 (2011)

Section

Articles

License

Copyright terms are indicated in the Republic of Lithuania Law on Copyright and Related Rights, Articles 4-37.