IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v209y2011i2p104-112.html
   My bibliography  Save this article

A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO

Author

Listed:
  • Liefooghe, Arnaud
  • Jourdan, Laetitia
  • Talbi, El-Ghazali

Abstract

This paper presents a general-purpose software framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO. A concise overview of evolutionary algorithms for multiobjective optimization is given. A substantial number of methods has been proposed so far, and an attempt of conceptually unifying existing approaches is presented here. Based on a fine-grained decomposition and following the main issues of fitness assignment, diversity preservation and elitism, a conceptual model is proposed and is validated by regarding a number of state-of-the-art algorithms as simple variants of the same structure. This model is then incorporated into the ParadisEO-MOEO software framework. This framework has proven its validity and high flexibility by enabling the resolution of many academic, real-world and hard multiobjective optimization problems.

Suggested Citation

  • Liefooghe, Arnaud & Jourdan, Laetitia & Talbi, El-Ghazali, 2011. "A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO," European Journal of Operational Research, Elsevier, vol. 209(2), pages 104-112, March.
    • Handle: RePEc:eee:ejores:v:209:y:2011:i:2:p:104-112
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(10)00533-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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.
    2. Molina, Julin & Santana, Luis V. & Hernandez-Daz, Alfredo G. & Coello Coello, Carlos A. & Caballero, Rafael, 2009. "g-dominance: Reference point based dominance for multiobjective metaheuristics," European Journal of Operational Research, Elsevier, vol. 197(2), pages 685-692, September.
    3. Figueira, J.R. & Liefooghe, A. & Talbi, E.-G. & Wierzbicki, A.P., 2010. "A parallel multiple reference point approach for multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 205(2), pages 390-400, September.
    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. 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.
    2. Mohammadi, M. & Dehbari, S. & Vahdani, Behnam, 2014. "Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 15-41.

    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. 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.
    2. Ana Ruiz & Rubén Saborido & Mariano Luque, 2015. "A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm," Journal of Global Optimization, Springer, vol. 62(1), pages 101-129, May.
    3. E. Filatovas & O. Kurasova & J. L. Redondo & J. Fernández, 2020. "A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 402-423, July.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Cem P. Cetinkaya & Mert Can Gunacti, 2018. "Multi-Criteria Analysis of Water Allocation Scenarios in a Water Scarce Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2867-2884, June.
    9. 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.
    10. Rong Tang & Ke Li & Wei Ding & Yuntao Wang & Huicheng Zhou & Guangtao Fu, 2020. "Reference Point Based Multi-Objective Optimization of Reservoir Operation: a Comparison of Three Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1005-1020, February.
    11. Zio, E. & Bazzo, R., 2011. "Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 569-580.
    12. 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.
    13. Steuer, Ralph E. & Utz, Sebastian, 2023. "Non-contour efficient fronts for identifying most preferred portfolios in sustainability investing," European Journal of Operational Research, Elsevier, vol. 306(2), pages 742-753.
    14. Máximo Méndez & Mariano Frutos & Fabio Miguel & Ricardo Aguasca-Colomo, 2020. "TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    15. 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.
    16. Dubois-Lacoste, Jérémie & López-Ibáñez, Manuel & Stützle, Thomas, 2015. "Anytime Pareto local search," European Journal of Operational Research, Elsevier, vol. 243(2), pages 369-385.
    17. Carolina Almeida & Richard Gonçalves & Elizabeth Goldbarg & Marco Goldbarg & Myriam Delgado, 2012. "An experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem," Annals of Operations Research, Springer, vol. 199(1), pages 305-341, October.
    18. Hyoungjin Kim & Meng-Sing Liou, 2013. "New fitness sharing approach for multi-objective genetic algorithms," Journal of Global Optimization, Springer, vol. 55(3), pages 579-595, March.
    19. Taher Ahmadi & Bo van der Rhee, 2023. "Multiobjective Line Balancing Game: Collaboration and Peer Evaluation," INFORMS Transactions on Education, INFORMS, vol. 23(3), pages 179-195, May.
    20. Miettinen, Kaisa & Molina, Julián & González, Mercedes & Hernández-Díaz, Alfredo & Caballero, Rafael, 2009. "Using box indices in supporting comparison in multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 197(1), pages 17-24, August.

    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:209:y:2011:i:2:p:104-112. 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.