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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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