IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v60y2015i2p479-511.html
   My bibliography  Save this article

Multiobjective swarm intelligence for the traffic grooming problem

Author

Listed:
  • Álvaro Rubio-Largo
  • Miguel Vega-Rodríguez
  • David González-Álvarez

Abstract

The future of optical networks is focused on Wavelength Division Multiplexing (WDM) technology. WDM allows simultaneous transmissions of traffic on many non-overlapping channels (wavelengths). Since nowadays the majority of traffic requests only require a bandwidth of Mbps, there exists a waste of bandwidth in these non-overlapping channels because they support traffic in Gbps range. For exploiting the optical network resources effectively, several low-speed traffic requests can be groomed onto a wavelength channel, which is not a simple task. In fact, it is known as the Traffic Grooming problem, and is considered an optimization problem (NP-hard problem). In this work, we suggest the use of multiobjective evolutionary computation and swarm intelligence jointly for solving the Traffic Grooming problem. We have proposed the following swarm algorithms: Artificial Bee Colony, Gravitational Search Algorithm, and Firefly Algorithm; but adapted to multiobjective field: MO-ABC, MO-GSA, and MO-FA respectively. Furthermore, we have adapted the well-known Strength Pareto Evolutionary Algorithm 2, Fast Nondominated Sorting Genetic Algorithm, and Multiobjective Selection Based On Dominated Hypervolume to the Traffic Grooming problem with the aim of evaluating the quality of our swarm proposals. Finally, we present several comparisons with other heuristics and metaheuristics published in the literature by other authors. After comparing with them, we conclude that our approaches overcome the results obtained by other approaches published by other authors. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Álvaro Rubio-Largo & Miguel Vega-Rodríguez & David González-Álvarez, 2015. "Multiobjective swarm intelligence for the traffic grooming problem," Computational Optimization and Applications, Springer, vol. 60(2), pages 479-511, March.
    • Handle: RePEc:spr:coopap:v:60:y:2015:i:2:p:479-511
    DOI: 10.1007/s10589-014-9682-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-014-9682-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10589-014-9682-8?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. Beume, Nicola & Naujoks, Boris & Emmerich, Michael, 2007. "SMS-EMOA: Multiobjective selection based on dominated hypervolume," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1653-1669, September.
    Full references (including those not matched with items on IDEAS)

    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. Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
    2. Gong, Wenyin & Cai, Zhihua, 2009. "An improved multiobjective differential evolution based on Pareto-adaptive [epsilon]-dominance and orthogonal design," European Journal of Operational Research, Elsevier, vol. 198(2), pages 576-601, October.
    3. Andrea Ponti & Antonio Candelieri & Ilaria Giordani & Francesco Archetti, 2023. "Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms," Mathematics, MDPI, vol. 11(10), pages 1-14, May.
    4. David Quintana & Roman Denysiuk & Sandra García-Rodríguez & Antonio Gaspar-Cunha, 2017. "Portfolio implementation risk management using evolutionary multiobjective optimization," Post-Print hal-01881379, HAL.
    5. Yunsong Han & Hong Yu & Cheng Sun, 2017. "Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions," Sustainability, MDPI, vol. 9(12), pages 1-18, December.
    6. Yeudiel Lara Moreno & Carlos Ignacio Hernández Castellanos, 2024. "A Hierarchical Approach to a Tri-Objective Portfolio Optimization Problem Considering an ESG Index," Mathematics, MDPI, vol. 12(19), pages 1-16, October.
    7. Laumanns, Marco & Zenklusen, Rico, 2011. "Stochastic convergence of random search methods to fixed size Pareto front approximations," European Journal of Operational Research, Elsevier, vol. 213(2), pages 414-421, September.
    8. Ivo Couckuyt & Dirk Deschrijver & Tom Dhaene, 2014. "Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization," Journal of Global Optimization, Springer, vol. 60(3), pages 575-594, November.
    9. Derbel, Bilel & Humeau, Jérémie & Liefooghe, Arnaud & Verel, Sébastien, 2014. "Distributed localized bi-objective search," European Journal of Operational Research, Elsevier, vol. 239(3), pages 731-743.
    10. Hang Xu, 2024. "A Dynamic Tasking-Based Evolutionary Algorithm for Bi-Objective Feature Selection," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
    11. Sergio Cabello, 2023. "Faster distance-based representative skyline and k-center along pareto front in the plane," Journal of Global Optimization, Springer, vol. 86(2), pages 441-466, June.
    12. Sven Schulz & Udo Buscher & Liji Shen, 2020. "Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices," Journal of Business Economics, Springer, vol. 90(9), pages 1315-1343, November.
    13. Lourdes Uribe & Johan M Bogoya & Andrés Vargas & Adriana Lara & Günter Rudolph & Oliver Schütze, 2020. "A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-Objective Reference Set Problems," Mathematics, MDPI, vol. 8(10), pages 1-29, October.
    14. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    15. Yugong Dang & Hongen Ma & Jun Wang & Zhigang Zhou & Zhidong Xu, 2022. "An Improved Multi-Objective Optimization Decision Method Using NSGA-III for a Bivariate Precision Fertilizer Applicator," Agriculture, MDPI, vol. 12(9), pages 1-23, September.
    16. Taimoor Akhtar & Christine Shoemaker, 2016. "Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection," Journal of Global Optimization, Springer, vol. 64(1), pages 17-32, January.
    17. Prashant Singh & Ivo Couckuyt & Khairy Elsayed & Dirk Deschrijver & Tom Dhaene, 2017. "Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 172-193, October.
    18. Braun, Marlon & Shukla, Pradyumn, 2024. "On cone-based decompositions of proper Pareto-optimality in multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 592-602.
    19. Jesús Martínez-Frutos & David Herrero-Pérez, 2016. "Kriging-based infill sampling criterion for constraint handling in multi-objective optimization," Journal of Global Optimization, Springer, vol. 64(1), pages 97-115, January.
    20. Juergen Branke & Wen Zhang, 2019. "Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 831-865, September.

    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:coopap:v:60:y:2015:i:2:p:479-511. 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.