IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v191y2011i1p1-2210.1007-s10479-011-0983-3.html
   My bibliography  Save this article

An efficient memetic algorithm for the graph partitioning problem

Author

Listed:
  • Philippe Galinier
  • Zied Boujbel
  • Michael Coutinho Fernandes

Abstract

Given a graph and an integer k, the goal of the graph partitioning problem is to find a partition of the vertex set in k classes, while minimizing the number of cut edges, and respecting a balance constraint between the classes. In this paper, we present a new memetic algorithm for the solution of the problem which uses both a tabu operator and a specialized crossover operator. The algorithm is tested by using the benchmarks of the graph partitioning archive. Our experiments show our memetic algorithm to outperform state-of-the-art algorithms proposed so far for the problem and to reach new records for a majority of the tested benchmarks instances. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:191:y:2011:i:1:p:1-22:10.1007/s10479-011-0983-3
    DOI: 10.1007/s10479-011-0983-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-011-0983-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-011-0983-3?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. David S. Johnson & Cecilia R. Aragon & Lyle A. McGeoch & Catherine Schevon, 1991. "Optimization by Simulated Annealing: An Experimental Evaluation; Part II, Graph Coloring and Number Partitioning," Operations Research, INFORMS, vol. 39(3), pages 378-406, June.
    2. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    3. Chris Walshaw, 2004. "Multilevel Refinement for Combinatorial Optimisation Problems," Annals of Operations Research, Springer, vol. 131(1), pages 325-372, October.
    4. Philippe Galinier & Jin-Kao Hao, 1999. "Hybrid Evolutionary Algorithms for Graph Coloring," Journal of Combinatorial Optimization, Springer, vol. 3(4), pages 379-397, December.
    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. Lin, Geng & Guan, Jian & Feng, Huibin, 2018. "An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 199-209.
    2. 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.
    3. 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.
    4. Raka Jovanovic & Abdelkader Bousselham & Stefan Voß, 2015. "A heuristic method for solving the problem of partitioning graphs with supply and demand," Annals of Operations Research, Springer, vol. 235(1), pages 371-393, December.
    5. 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.
    6. 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.

    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. Wen Sun & Jin-Kao Hao & Alexandre Caminada, 2019. "Iterated backtrack removal search for finding k-vertex-critical subgraphs," Journal of Heuristics, Springer, vol. 25(4), pages 565-590, October.
    2. Drexl, Andreas & Juretzka, Jan & Salewski, Frank, 1993. "Academic course scheduling under workload and changeover constraints," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 337, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    3. Drexl, Andreas & Haase, Knut, 1993. "Sequential-analysis-based randomized-regret-methods for lotsizing and scheduling," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 323, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    4. Chiara Gruden & Irena Ištoka Otković & Matjaž Šraml, 2020. "Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    5. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    6. Anurag Agarwal, 2009. "Theoretical insights into the augmented-neural-network approach for combinatorial optimization," Annals of Operations Research, Springer, vol. 168(1), pages 101-117, April.
    7. Nicolas Zufferey & Olivier Labarthe & David Schindl, 2012. "Heuristics for a project management problem with incompatibility and assignment costs," Computational Optimization and Applications, Springer, vol. 51(3), pages 1231-1252, April.
    8. Mohammad Javad Feizollahi & Igor Averbakh, 2014. "The Robust (Minmax Regret) Quadratic Assignment Problem with Interval Flows," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 321-335, May.
    9. Nha Vo‐Thanh & Hans‐Peter Piepho, 2023. "Generating designs for comparative experiments with two blocking factors," Biometrics, The International Biometric Society, vol. 79(4), pages 3574-3585, December.
    10. Сластников С.А., 2014. "Применение Метаэвристических Алгоритмов Для Задачи Маршрутизации Транспорта," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 50(1), pages 117-126, январь.
    11. Xiao-Feng Xie & Jiming Liu, 2009. "Graph coloring by multiagent fusion search," Journal of Combinatorial Optimization, Springer, vol. 18(2), pages 99-123, August.
    12. H. A. J. Crauwels & C. N. Potts & L. N. Van Wassenhove, 1998. "Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 341-350, August.
    13. Pirlot, Marc, 1996. "General local search methods," European Journal of Operational Research, Elsevier, vol. 92(3), pages 493-511, August.
    14. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    15. Nair, D.J. & Grzybowska, H. & Fu, Y. & Dixit, V.V., 2018. "Scheduling and routing models for food rescue and delivery operations," Socio-Economic Planning Sciences, Elsevier, vol. 63(C), pages 18-32.
    16. Cazzaro, Davide & Fischetti, Martina & Fischetti, Matteo, 2020. "Heuristic algorithms for the Wind Farm Cable Routing problem," Applied Energy, Elsevier, vol. 278(C).
    17. İbrahim Muter & Ş. İlker Birbil & Güvenç Şahin, 2010. "Combination of Metaheuristic and Exact Algorithms for Solving Set Covering-Type Optimization Problems," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 603-619, November.
    18. Kadri Sylejmani & Jürgen Dorn & Nysret Musliu, 2017. "Planning the trip itinerary for tourist groups," Information Technology & Tourism, Springer, vol. 17(3), pages 275-314, September.
    19. Huang, Yeran & Yang, Lixing & Tang, Tao & Gao, Ziyou & Cao, Fang, 2017. "Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks," Energy, Elsevier, vol. 138(C), pages 1124-1147.
    20. B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "Optimization of manufacturing systems using a neural network metamodel with a new training approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1191-1197, September.

    More about this item

    Statistics

    Access and download statistics

    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:spr:annopr:v:191:y:2011:i:1:p:1-22:10.1007/s10479-011-0983-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.