A hybrid genetic algorithmic approach to the maximally diverse grouping problem

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

22 Scopus Citations
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  • Z. P. Fan
  • Y. Chen
  • J. Ma
  • S. Zeng

Related Research Unit(s)


Original languageEnglish
Pages (from-to)92-99
Journal / PublicationJournal of the Operational Research Society
Issue number1
Publication statusPublished - Jan 2011


The maximally diverse grouping problem (MDGP) is a NP-complete problem. For such NP-complete problems, heuristics play a major role in searching for solutions. Most of the heuristics for MDGP focus on the equal group-size situation. In this paper, we develop a genetic algorithm (GA)-based hybrid heuristic to solve this problem considering not only the equal group-size situation but also the different group-size situation. The performance of the algorithm is compared with the established Lotfi-Cerveny-Weitz algorithm and the non-hybrid GA. Computational experience indicates that the proposed GA-based hybrid algorithm is a good tool for solving MDGP. Moreover, it can be easily modified to solve other equivalent problems. © 2011 Operational Research Society Ltd. All rights reserved.

Research Area(s)

  • genetic algorithm, local neighbourhood search, maximally diverse grouping problem