IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v255y2015icp114-124.html
   My bibliography  Save this article

Parallelization of a non-linear multi-objective optimization algorithm: Application to a location problem

Author

Listed:
  • Arrondo, Aránzazu Gila
  • Redondo, Juana L.
  • Fernández, José
  • Ortigosa, Pilar M.

Abstract

Real-life problems usually include conflicting objectives. Solving multi-objective problems (i.e., obtaining the complete efficient set and the corresponding Pareto-front) via exact methods is in many cases nearly intractable. In order to cope with those problems, several (meta) heuristic procedures have been developed during the last decade whose aim is to obtain a good discrete approximation of the Pareto-front. In this vein, a new multi-objective evolutionary algorithm, called FEMOEA, which can be applied to many nonlinear multi-objective optimization problems, has recently been proposed. Through a comparison with an exact interval branch-and-bound algorithm, it has been shown that FEMOEA provides very good approximations of the Pareto-front. Furthermore, it has been compared to the reference algorithms NSGA-II, SPEA2 and MOEA/D. Comprehensive computational studies have shown that, among the studied algorithms, FEMOEA was the one providing, on average, the best results for all the quality indicators analyzed. However, when the set approximating the Pareto-front must have many points (because a high precision is required), the computational time needed by FEMOEA may not be negligible at all. Furthermore, the memory requirements needed by the algorithm when solving those instances may be so high that the available memory may not be enough. In those cases, parallelizing the algorithm and running it in a parallel architecture may be the best way forward. In this work, a parallelization of FEMOEA, called FEMOEA-Paral, is presented. To show its applicability, a bi-objective competitive facility location and design problem is solved. The results show that FEMOEA-Paral is able to maintain the effectiveness of the sequential version and this by reducing the computational costs. Furthermore, the parallel version shows good scalability. The efficiency results have been analyzed by means of a profiling and tracing toolkit for performance analysis.

Suggested Citation

  • Arrondo, Aránzazu Gila & Redondo, Juana L. & Fernández, José & Ortigosa, Pilar M., 2015. "Parallelization of a non-linear multi-objective optimization algorithm: Application to a location problem," Applied Mathematics and Computation, Elsevier, vol. 255(C), pages 114-124.
  • Handle: RePEc:eee:apmaco:v:255:y:2015:i:c:p:114-124
    DOI: 10.1016/j.amc.2014.08.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300314011308
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2014.08.036?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. Fernandez, Jose & Pelegri'n, Blas & Plastria, Frank & Toth, Boglarka, 2007. "Solving a Huff-like competitive location and design model for profit maximization in the plane," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1274-1287, June.
    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. J. L. Redondo & J. Fernández & P. M. Ortigosa, 2017. "FEMOEA: a fast and efficient multi-objective evolutionary algorithm," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 85(1), pages 113-135, February.
    2. G. Ortega & E. Filatovas & E. M. Garzón & L. G. Casado, 2017. "Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU," Journal of Global Optimization, Springer, vol. 69(3), pages 607-627, November.

    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. Fernández, José & Hendrix, Eligius M.T., 2013. "Recent insights in Huff-like competitive facility location and design," European Journal of Operational Research, Elsevier, vol. 227(3), pages 581-584.
    2. Sáiz, M. Elena & Hendrix, Eligius M.T. & Pelegrín, Blas, 2011. "On Nash equilibria of a competitive location-design problem," European Journal of Operational Research, Elsevier, vol. 210(3), pages 588-593, May.
    3. Blanquero, Rafael & Carrizosa, Emilio & G.-Tóth, Boglárka & Nogales-Gómez, Amaya, 2016. "p-facility Huff location problem on networks," European Journal of Operational Research, Elsevier, vol. 255(1), pages 34-42.
    4. Karatas, Mumtaz, 2017. "A multi-objective facility location problem in the presence of variable gradual coverage performance and cooperative cover," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1040-1051.
    5. Stradi-Granados, Benito A. & Haven, Emmanuel, 2010. "The use of interval arithmetic in solving a non-linear rational expectation based multiperiod output-inflation process model: The case of the IN/GB method," European Journal of Operational Research, Elsevier, vol. 203(1), pages 222-229, May.
    6. Farahani, Reza Zanjirani & Rezapour, Shabnam & Drezner, Tammy & Fallah, Samira, 2014. "Competitive supply chain network design: An overview of classifications, models, solution techniques and applications," Omega, Elsevier, vol. 45(C), pages 92-118.
    7. G.-Tóth, Boglárka & Anton-Sanchez, Laura & Fernández, José, 2024. "A Huff-like location model with quality adjustment and/or closing of existing facilities," European Journal of Operational Research, Elsevier, vol. 313(3), pages 937-953.
    8. Shamekhi Amiri, A. & Torabi, S. Ali & Ghodsi, R., 2018. "An iterative approach for a bi-level competitive supply chain network design problem under foresight competition and variable coverage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 99-114.
    9. Redondo, Juana L. & Fernández, José & Arrondo, Aránzazu G. & García, Inmaculada & Ortigosa, Pilar M., 2012. "Fixed or variable demand? Does it matter when locating a facility?," Omega, Elsevier, vol. 40(1), pages 9-20, January.
    10. Rezapour, Shabnam & Hassani, Ashkan & Farahani, Reza Zanjirani, 2015. "Concurrent design of product family and supply chain network considering quality and price," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 81(C), pages 18-35.
    11. Saidani, Nasreddine & Chu, Feng & Chen, Haoxun, 2012. "Competitive facility location and design with reactions of competitors already in the market," European Journal of Operational Research, Elsevier, vol. 219(1), pages 9-17.
    12. Asgari, Nasrin & Farahani, Reza Zanjirani & Goh, Mark, 2013. "Network design approach for hub ports-shipping companies competition and cooperation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 1-18.
    13. Rezapour, Shabnam & Farahani, Reza Zanjirani & Dullaert, Wout & De Borger, Bruno, 2014. "Designing a new supply chain for competition against an existing supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 124-140.
    14. Lin, Yun Hui & Tian, Qingyun, 2021. "Branch-and-cut approach based on generalized benders decomposition for facility location with limited choice rule," European Journal of Operational Research, Elsevier, vol. 293(1), pages 109-119.
    15. Berman, Oded & Drezner, Zvi & Krass, Dmitry & Wesolowsky, George O., 2009. "The variable radius covering problem," European Journal of Operational Research, Elsevier, vol. 196(2), pages 516-525, July.

    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:apmaco:v:255:y:2015:i:c:p:114-124. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.