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Maximally diverse grouping: an iterated tabu search approach

Author

Listed:
  • Gintaras Palubeckis

    (Kaunas University of Technology, Kaunas, Lithuania)

  • Armantas Ostreika

    (Kaunas University of Technology, Kaunas, Lithuania)

  • Dalius Rubliauskas

    (Kaunas University of Technology, Kaunas, Lithuania)

Abstract

The maximally diverse grouping problem (MDGP) consists of finding a partition of a set of elements into a given number of mutually disjoint groups, while respecting the requirements of group size constraints and diversity. In this paper, we propose an iterated tabu search (ITS) algorithm for solving this problem. We report computational results on three sets of benchmark MDGP instances of size up to 960 elements and provide comparisons of ITS to five state-of-the-art heuristic methods from the literature. The results demonstrate the superiority of the ITS algorithm over alternative approaches. The source code of the algorithm is available for free download via the internet.

Suggested Citation

  • Gintaras Palubeckis & Armantas Ostreika & Dalius Rubliauskas, 2015. "Maximally diverse grouping: an iterated tabu search approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(4), pages 579-592, April.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:4:p:579-592
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    Citations

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    Cited by:

    1. Lai, Xiangjing & Hao, Jin-Kao, 2016. "Iterated maxima search for the maximally diverse grouping problem," European Journal of Operational Research, Elsevier, vol. 254(3), pages 780-800.
    2. Zhou, Qing & Benlic, Una & Wu, Qinghua & Hao, Jin-Kao, 2019. "Heuristic search to the capacitated clustering problem," European Journal of Operational Research, Elsevier, vol. 273(2), pages 464-487.
    3. Yang, Xiao & Cai, Zonghui & Jin, Ting & Tang, Zheng & Gao, Shangce, 2022. "A three-phase search approach with dynamic population size for solving the maximally diverse grouping problem," European Journal of Operational Research, Elsevier, vol. 302(3), pages 925-953.
    4. Lai, Xiangjing & Hao, Jin-Kao & Fu, Zhang-Hua & Yue, Dong, 2021. "Neighborhood decomposition based variable neighborhood search and tabu search for maximally diverse grouping," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1067-1086.
    5. Arne Schulz, 2022. "A new mixed-integer programming formulation for the maximally diverse grouping problem with attribute values," Annals of Operations Research, Springer, vol. 318(1), pages 501-530, November.
    6. Arne Schulz, 2024. "Efficient neighborhood evaluation for the maximally diverse grouping problem," Annals of Operations Research, Springer, vol. 341(2), pages 1247-1265, October.
    7. Zhiqiang Zhang & Zhongwen Li & Xiaobing Qiao & Weijun Wang, 2019. "An Efficient Memetic Algorithm for the Minimum Load Coloring Problem," Mathematics, MDPI, vol. 7(5), pages 1-20, May.
    8. Schulz, Arne, 2021. "The balanced maximally diverse grouping problem with block constraints," European Journal of Operational Research, Elsevier, vol. 294(1), pages 42-53.

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