IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v231y2013i2p452-464.html
   My bibliography  Save this article

A hybrid metaheuristic method for the Maximum Diversity Problem

Author

Listed:
  • Wu, Qinghua
  • Hao, Jin-Kao

Abstract

The Maximum Diversity Problem (MDP) consists in selecting a subset of m elements from a given set of n elements (n>m) in such a way that the sum of the pairwise distances between the m chosen elements is maximized. We present a hybrid metaheuristic algorithm (denoted by MAMDP) for MDP. The algorithm uses a dedicated crossover operator to generate new solutions and a constrained neighborhood tabu search procedure for local optimization. MAMDP applies also a distance-and-quality based replacement strategy to maintain population diversity. Extensive evaluations on a large set of 120 benchmark instances show that the proposed approach competes very favorably with the current state-of-art methods for MDP. In particular, it consistently and easily attains all the best known lower bounds and yields improved lower bounds for 6 large MDP instances. The key components of MAMDP are analyzed to shed light on their influence on the performance of the algorithm.

Suggested Citation

  • Wu, Qinghua & Hao, Jin-Kao, 2013. "A hybrid metaheuristic method for the Maximum Diversity Problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 452-464.
  • Handle: RePEc:eee:ejores:v:231:y:2013:i:2:p:452-464
    DOI: 10.1016/j.ejor.2013.06.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713004682
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2013.06.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Duarte, Abraham & Marti, Rafael, 2007. "Tabu search and GRASP for the maximum diversity problem," European Journal of Operational Research, Elsevier, vol. 178(1), pages 71-84, April.
    2. Lü, Zhipeng & Glover, Fred & Hao, Jin-Kao, 2010. "A hybrid metaheuristic approach to solving the UBQP problem," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1254-1262, December.
    3. Macambira, Elder Magalhaes & de Souza, Cid Carvalho, 2000. "The edge-weighted clique problem: Valid inequalities, facets and polyhedral computations," European Journal of Operational Research, Elsevier, vol. 123(2), pages 346-371, June.
    4. Lozano, M. & Molina, D. & GarcI´a-MartI´nez, C., 2011. "Iterated greedy for the maximum diversity problem," European Journal of Operational Research, Elsevier, vol. 214(1), pages 31-38, October.
    5. Alidaee, Bahram & Glover, Fred & Kochenberger, Gary & Wang, Haibo, 2007. "Solving the maximum edge weight clique problem via unconstrained quadratic programming," European Journal of Operational Research, Elsevier, vol. 181(2), pages 592-597, September.
    6. Prokopyev, Oleg A. & Kong, Nan & Martinez-Torres, Dayna L., 2009. "The equitable dispersion problem," European Journal of Operational Research, Elsevier, vol. 197(1), pages 59-67, August.
    7. Micael Gallego & Abraham Duarte & Manuel Laguna & Rafael Martí, 2009. "Hybrid heuristics for the maximum diversity problem," Computational Optimization and Applications, Springer, vol. 44(3), pages 411-426, December.
    8. Martí, Rafael & Gallego, Micael & Duarte, Abraham, 2010. "A branch and bound algorithm for the maximum diversity problem," European Journal of Operational Research, Elsevier, vol. 200(1), pages 36-44, January.
    9. R Aringhieri & R Cordone, 2011. "Comparing local search metaheuristics for the maximum diversity problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 266-280, February.
    10. Elder Macambira, 2002. "An Application of Tabu Search Heuristic for the Maximum Edge-Weighted Subgraph Problem," Annals of Operations Research, Springer, vol. 117(1), pages 175-190, November.
    11. Feng, Bo & Jiang, Zhong-Zhong & Fan, Zhi-Ping & Fu, Na, 2010. "A method for member selection of cross-functional teams using the individual and collaborative performances," European Journal of Operational Research, Elsevier, vol. 203(3), pages 652-661, June.
    12. Philippe Galinier & Zied Boujbel & Michael Coutinho Fernandes, 2011. "An efficient memetic algorithm for the graph partitioning problem," Annals of Operations Research, Springer, vol. 191(1), pages 1-22, November.
    13. S. S. Ravi & D. J. Rosenkrantz & G. K. Tayi, 1994. "Heuristic and Special Case Algorithms for Dispersion Problems," Operations Research, INFORMS, vol. 42(2), pages 299-310, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wu, Qinghua & Hao, Jin-Kao, 2015. "A review on algorithms for maximum clique problems," European Journal of Operational Research, Elsevier, vol. 242(3), pages 693-709.
    2. Schulz, Arne, 2021. "The balanced maximally diverse grouping problem with block constraints," European Journal of Operational Research, Elsevier, vol. 294(1), pages 42-53.
    3. Zhicheng Liu & Longkun Guo & Donglei Du & Dachuan Xu & Xiaoyan Zhang, 2022. "Maximization problems of balancing submodular relevance and supermodular diversity," Journal of Global Optimization, Springer, vol. 82(1), pages 179-194, January.
    4. Martí, Rafael & Martínez-Gavara, Anna & Pérez-Peló, Sergio & Sánchez-Oro, Jesús, 2022. "A review on discrete diversity and dispersion maximization from an OR perspective," European Journal of Operational Research, Elsevier, vol. 299(3), pages 795-813.
    5. Bahram Alidaee & Haibo Wang, 2017. "A note on heuristic approach based on UBQP formulation of the maximum diversity problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 102-110, January.
    6. Yi Zhou & Jin-Kao Hao & Adrien Goëffon, 2016. "A three-phased local search approach for the clique partitioning problem," Journal of Combinatorial Optimization, Springer, vol. 32(2), pages 469-491, August.
    7. Lu, Yongliang & Benlic, Una & Wu, Qinghua, 2018. "A memetic algorithm for the Orienteering Problem with Mandatory Visits and Exclusionary Constraints," European Journal of Operational Research, Elsevier, vol. 268(1), pages 54-69.
    8. 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.
    9. Aringhieri, Roberto & Cordone, Roberto & Grosso, Andrea, 2015. "Construction and improvement algorithms for dispersion problems," European Journal of Operational Research, Elsevier, vol. 242(1), pages 21-33.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martí, Rafael & Martínez-Gavara, Anna & Pérez-Peló, Sergio & Sánchez-Oro, Jesús, 2022. "A review on discrete diversity and dispersion maximization from an OR perspective," European Journal of Operational Research, Elsevier, vol. 299(3), pages 795-813.
    2. Lozano, M. & Molina, D. & GarcI´a-MartI´nez, C., 2011. "Iterated greedy for the maximum diversity problem," European Journal of Operational Research, Elsevier, vol. 214(1), pages 31-38, October.
    3. Wu, Qinghua & Hao, Jin-Kao, 2015. "A review on algorithms for maximum clique problems," European Journal of Operational Research, Elsevier, vol. 242(3), pages 693-709.
    4. Parreño, Francisco & Álvarez-Valdés, Ramón & Martí, Rafael, 2021. "Measuring diversity. A review and an empirical analysis," European Journal of Operational Research, Elsevier, vol. 289(2), pages 515-532.
    5. Aringhieri, Roberto & Cordone, Roberto & Grosso, Andrea, 2015. "Construction and improvement algorithms for dispersion problems," European Journal of Operational Research, Elsevier, vol. 242(1), pages 21-33.
    6. Anna Martínez-Gavara & Vicente Campos & Manuel Laguna & Rafael Martí, 2017. "Heuristic solution approaches for the maximum minsum dispersion problem," Journal of Global Optimization, Springer, vol. 67(3), pages 671-686, March.
    7. Seyedmohammadhossein Hosseinian & Dalila B. M. M. Fontes & Sergiy Butenko, 2020. "A Lagrangian Bound on the Clique Number and an Exact Algorithm for the Maximum Edge Weight Clique Problem," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 747-762, July.
    8. Felix Prause & Kai Hoppmann-Baum & Boris Defourny & Thorsten Koch, 2021. "The maximum diversity assortment selection problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 93(3), pages 521-554, June.
    9. Jiawei Song & Yang Wang & Haibo Wang & Qinghua Wu & Abraham P. Punnen, 2019. "An effective multi-wave algorithm for solving the max-mean dispersion problem," Journal of Heuristics, Springer, vol. 25(4), pages 731-752, October.
    10. Seyedmohammadhossein Hosseinian & Dalila B. M. M. Fontes & Sergiy Butenko, 2018. "A nonconvex quadratic optimization approach to the maximum edge weight clique problem," Journal of Global Optimization, Springer, vol. 72(2), pages 219-240, October.
    11. Juan F. Gomez & Javier Panadero & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan, 2022. "A Multi-Start Biased-Randomized Algorithm for the Capacitated Dispersion Problem," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    12. Bahram Alidaee & Haibo Wang, 2017. "A note on heuristic approach based on UBQP formulation of the maximum diversity problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 102-110, January.
    13. Daniel Porumbel & Jin-Kao Hao & Fred Glover, 2011. "A simple and effective algorithm for the MaxMin diversity problem," Annals of Operations Research, Springer, vol. 186(1), pages 275-293, June.
    14. 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.
    15. Michele Garraffa & Federico Della Croce & Fabio Salassa, 2017. "An exact semidefinite programming approach for the max-mean dispersion problem," Journal of Combinatorial Optimization, Springer, vol. 34(1), pages 71-93, July.
    16. R Aringhieri & R Cordone, 2011. "Comparing local search metaheuristics for the maximum diversity problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 266-280, February.
    17. Sergey Kovalev & Isabelle Chalamon & Fabio J. Petani, 2023. "Maximizing single attribute diversity in group selection," Annals of Operations Research, Springer, vol. 320(1), pages 535-540, January.
    18. Ricardo M. Lima & Ignacio E. Grossmann, 2017. "On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study," Computational Optimization and Applications, Springer, vol. 66(1), pages 1-37, January.
    19. San Segundo, Pablo & Coniglio, Stefano & Furini, Fabio & Ljubić, Ivana, 2019. "A new branch-and-bound algorithm for the maximum edge-weighted clique problem," European Journal of Operational Research, Elsevier, vol. 278(1), pages 76-90.
    20. Martí, Rafael & Gallego, Micael & Duarte, Abraham, 2010. "A branch and bound algorithm for the maximum diversity problem," European Journal of Operational Research, Elsevier, vol. 200(1), pages 36-44, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:231:y:2013:i:2:p:452-464. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.