Solving the clique partitioning problem as a maximally diverse grouping problem

Jack Brimberg (1) , Stefana Janićijević (2) , Nenad Mladenovic (3) , Dragan Urošević (2)

1 Royal Military College of Canada
2 SANU - Mathematical Institute of the Serbian Academy of Sciences and Arts
3 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201

In this paper we show that the clique partitioning problem can be reformulated in an equivalent form as the maximally diverse grouping problem (MDGP). We then modify a skewed general variable neighborhood search (SGVNS) heuristic that was first developed to solve the MDGP. Similarly as with the MDGP, significant improvements over the state of the art are obtained when SGVNS is tested on large scale instances. This further confirms the usefulness of a combined approach of diversification afforded with skewed VNS and intensification afforded with the local search in general VNS.

Domaines: Informatique [cs]

Jack Brimberg, Stefana Janićijević, Nenad Mladenovic, Dragan Urošević. Solving the clique partitioning problem as a maximally diverse grouping problem. Optimization Letters, 2017, 11 (6), pp.1-13. ⟨10.1007/s11590-015-0869-4⟩. ⟨hal-03402185⟩