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Issue:Computational complexity and influence of numerical precision on the results of intercriteria analysis in the decision making process: Difference between revisions

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Latest revision as of 17:24, 12 December 2023

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http://ifigenia.org/wiki/issue:nifs/24/3/53-63
Title of paper: Computational complexity and influence of numerical precision on the results of intercriteria analysis in the decision making process
Author(s):
Vassia Atanassova
Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. Georgi Bonchev Str., Sofia 1113, Bulgaria
vassia.atanassova@gmail.com
Olympia Roeva
Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. Georgi Bonchev Str., Sofia 1113, Bulgaria
olympia@biomed.bas.bg
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 24 (2018), Number 3, pages 53–63
DOI: https://doi.org/10.7546/nifs.2018.24.3.53-63
Download:  PDF (212 Kb  Kb, File info)
Abstract: The present step from the research on InterCriteria Analysis (ICrA) discusses the issues of the computational complexity of the algorithm developed, and the influence which the numerical precision has on the results of its work. These questions are important from both theoretical, and practical point of view, especially in the context of the application of the method in support of the decision making process.
Keywords: Intercriteria analysis, Computational complexity, Numerical precision, Decision making.
AMS Classification: 03E72, 03D15.
References:
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  2. Atanassov, K., Atanassova, V., Chountas, P., Mitkova, M., Sotirova, E., Sotirov, S., & Stratiev, D. (2016) Intercriteria analysis over normalized data. Proc. of 2016 IEEE 8th International Conference on Intelligent Systems, IEEE, 564–566.
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  4. Atanassov, K., Mavrov, D., & Atanassova, V. (204) Intercriteria Decision Making: A New Approach for Multicriteria Decision Making, Based on Index Matrices and Intuitionistic Fuzzy Sets. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 11, 1–8.
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  16. Pencheva, T., Roeva, O., & Angelova, M. (2018) Investigation of genetic algorithm performance based on different algorithms for intercriteria relations calculation. Lecture Notes in Computer Science, 10665, 390–398.
  17. Pencheva, T., Roeva, O., & Hristozov, I. (2006) Functional State Approach to Ferment-ation, Processes Modelling, Prof. Marin Drinov Academic Publishing House, Sofia.
  18. Roeva, O. (2011) Sensitivity analysis of E. coli fed-batch cultivation local models. Mathematica Balkanica, New Series, 25(4), 395–411.
  19. Roeva, O., & Fidanova, S. (2017) Intercriteria analysis of relations between model parameters estimates and ACO performance. Studies in Computational Intelligence, 681, 175–186.
  20. Roeva, O., & Fidanova, S. (2018) Comparison of different metaheuristic algorithms based on Intercriteria analysis. Journal of Computational and Applied Mathematics, 340, 615–628.
  21. Roeva O., S. Fidanova, M. Paprzycki, Comparison of Different ACO Start Strategies Based on InterCriteria Analysis, Recent Advances in Computational Optimization, Studies in Computational Intelligence, Vol. 717, 53-72.
  22. Roeva, O., Fidanova, S., Vassilev, P., & Gepner, P. (2015) Intercriteria analysis of a model parameters identification using genetic algorithm. IEEE Proceedings of the 2015 Feder-ated Conference on Computer Science and Information Systems (FedCSIS), 5, 501–506
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  24. Traneva, V., Tranev, S., Szmidt, E. & Atanassov, K. (2018) Three dimensional intercriteria analysis over intuitionistic fuzzy data. In: J. Kacprzyk et al. (eds.), Advances in Fuzzy Logic and Technology 2017, AISC, Vol. 641, Springer, 442–449.
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  26. Zoteva, D., & Roeva, O. (2018) InterCriteria Analysis results based on different number of objects. Notes on Intuitionistic Fuzzy Sets, 24(1), 110–119.
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