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Robust Consensus: A New Measure for Multicriteria Robust Group Decision Making Problems Using Evolutionary Approach

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

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Abstract

In fuzzy group decision making problems, we often use multiobjective evolutionary optimization. The optimizers search through the whole search space and provide a set of nondominated solutions. But, sometimes the decision makers express their prior preferences using fuzzy numbers. In this case, the optimizers search in the preferred soft region and provide solutions with higher consensus. If perturbation in the decision variable space is unavoidable, we also need to search for robust solutions. Again, this perturbation affects the degree of consensus of the solutions. This leads to search for solutions those are robust to their degree of consensus. In this work, we address these issues by redefining consensus and proposing a new measure called robust consensus. We also provide a reformulation mechanism for multiobjective optimization problems. Our experimental results show that the proposed method is capable of finding robust solutions having robust consensus in the specified soft region.

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Author information

Authors and Affiliations

  1. Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India

    Kaustuv Nag

  2. Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India

    Tandra Pal

  3. Electronics and Communication Sciences Unit (ECSU), Indian Statistical Institute, Calcutta, India

    Nikhil R. Pal

Authors
  1. Kaustuv Nag
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  2. Tandra Pal
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  3. Nikhil R. Pal
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2014 Springer International Publishing Switzerland

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Nag, K., Pal, T., Pal, N.R. (2014). Robust Consensus: A New Measure for Multicriteria Robust Group Decision Making Problems Using Evolutionary Approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-07173-2_33

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07172-5

  • Online ISBN: 978-3-319-07173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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