Accelerated Promethee Algorithm Based on Dimensionality Reduction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11894)


This paper presents an accelerated Promethee (Preference Ranking Organization METHod for Enrichment Evaluations) multi-criteria algorithm based on dimensionality reduction in large scale environments. In our context, the Promethee algorithm is used to select from a large set of objects, one or a small set of objects with a good compromise between several qualitative and quantitative criteria. The exact solution can be used by applying the exact multi-criteria Promethee algorithm. However, the drawback, with this type of exact algorithm, is the long execution time due to the combinatorial aspect of the problem. The exact Promethee computing time is linked both to the dimension of the problem (number of qualitative and quantitative criteria) and the size of the problem (number of objects). To address the previous drawback, we propose to accelerate the Promethee algorithm in combining the exact Promethee algorithm with an algorithm inherited from the Machine Learning (ML) field. The experiments demonstrate the potential of our approach under different scenarios to accelerate the respond time.


Performance optimization Machine learning algorithms (K-Means) Multi-criteria algorithm 



We thank the Grid5000 team for their help to use the testbed. Grid’5000 is supported by a scientific interest group (GIS) hosted by Inria and including CNRS, RENATER and several universities as well as other organizations.


  1. 1.
    Jackson, D.A., Somers, K., Harvey, H.H.: Similarity coefficients: measures of co-occurrence and association or simply measures of occurrence? Am. Nat. 133(03), 436–453 (1989)CrossRefGoogle Scholar
  2. 2.
    Behzadian, M., Kazemzadeh, R., Albadvi, A., Aghdasi, M.: Promethee: a comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 200(1), 198–215 (2010)CrossRefGoogle Scholar
  3. 3.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  4. 4.
    Cérin, C., Menouer, T., Lebbah, M.: Accelerating the computation of multi-objectives scheduling solutions for cloud computing. In: 2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2), pp. 49–56, November 2018Google Scholar
  5. 5.
    Ding, L., Zeng, S., Kang, L.: A fast algorithm on finding the non-dominated set in multi-objective optimization. In: The 2003 Congress on Evolutionary Computation 2003, CEC 2003, vol. 4, pp. 2565–2571, December 2003Google Scholar
  6. 6.
  7. 7.
    Brans, J.P., Mareschal, B.: Promethee Methods. In: Figueira, J., Greco, S., Ehrogott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys. International Series in Operations Research & Management Science, vol. 78, pp. 163–186. Springer, New York (2005). Scholar
  8. 8.
    Kung, H.T., Luccio, F., Preparata, F.P.: On finding the maxima of a set of vectors. J. ACM 22(4), 469–476 (1975)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lai, Y.-J., Liu, T.-Y., Hwang, C.-L.: Topsis for MODM. Eur. J. Oper. Res. 76(3), 486–500 (1994). Facility Location Models for Distribution PlanningCrossRefGoogle Scholar
  10. 10.
    Menouer, T., Darmon, P.: New profile recommendation approach based on multi-criteria algorithm. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 4961–4966, December 2018Google Scholar
  11. 11.
    Menouer, T., Darmon, P.: New scheduling strategy based on multi-criteria decision algorithm. In: 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 101–107, February 2019Google Scholar
  12. 12.
    Opricovic, S., Tzeng, G.-H.: Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156(2), 445–455 (2004)CrossRefGoogle Scholar
  13. 13.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier, Amsterdam (2011)zbMATHGoogle Scholar
  14. 14.
    Deshmukh, S.C.: Preference ranking organization method of enrichment evaluation (promethee). Int. J. Eng. Sci. Invent. 2, 28–34 (2013)Google Scholar
  15. 15.
    Taillandier, P., Stinckwich, S.: Using the Promethee multi-criteria decision making method to define new exploration strategies for rescue robots. In: International Symposium on Safety, Security, and Rescue Robotics (2011)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.UMANISLevallois-PerretFrance
  2. 2.University of Paris 13, LIPN/CNRS UMR 7030VilletaneuseFrance

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