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A parallel multiple reference point approach for multi-objective optimization

Author

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  • Figueira, J.R.
  • Liefooghe, A.
  • Talbi, E.-G.
  • Wierzbicki, A.P.

Abstract

This paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed within a region that covers the Pareto Frontier. An evolutionary algorithm is based on an achievement scalarizing function that does not impose any restrictions with respect to the location of the reference points in the objective space. Computational experiments are performed on a bi-objective flow-shop scheduling problem. Results, quality measures as well as a statistical analysis are reported in the paper.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:205:y:2010:i:2:p:390-400
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    References listed on IDEAS

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    1. Nagar, Amit & Haddock, Jorge & Heragu, Sunderesh, 1995. "Multiple and bicriteria scheduling: A literature survey," European Journal of Operational Research, Elsevier, vol. 81(1), pages 88-104, February.
    2. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    3. Kim, Yeong-Dae, 1995. "Minimizing total tardiness in permutation flowshops," European Journal of Operational Research, Elsevier, vol. 85(3), pages 541-555, September.
    4. Luque, Mariano & Miettinen, Kaisa & Eskelinen, Petri & Ruiz, Francisco, 2009. "Incorporating preference information in interactive reference point methods for multiobjective optimization," Omega, Elsevier, vol. 37(2), pages 450-462, April.
    5. 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.
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    Citations

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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. Pinto, F.S. & Figueira, J.R. & Marques, R.C., 2015. "A multi-objective approach with soft constraints for water supply and wastewater coverage improvements," European Journal of Operational Research, Elsevier, vol. 246(2), pages 609-618.
    3. 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.
    4. 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.
    5. Theodor J. Stewart, 2016. "Multiple objective project portfolio selection based on reference points," Journal of Business Economics, Springer, vol. 86(1), pages 23-33, January.
    6. Liang, Wen Yau & Huang, Chun-Che & Lin, Yin-Chen & Chang, Tsun Hsien & Shih, Meng Hao, 2013. "The multi-objective label correcting algorithm for supply chain modeling," International Journal of Production Economics, Elsevier, vol. 142(1), pages 172-178.

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