8-9 October 2020 • Burgas, Bulgaria

Submission: 15 May 2020 • Notification: 31 May 2020 • Final Version: 15 June 2020

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

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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.atanassovaAt sign.pnggmail.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
olympiaAt sign.pngbiomed.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
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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:
  1. Atanassov, K. (2014) Index Matrices. Springer, Cham.
  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.
  3. Atanassov, K., Atanassova, V., & Gluhchev, G. (2015) InterCriteria Analysis: Ideas and problems. Notes on Intuitionistic Fuzzy Sets, 21(1), 81–88.
  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.
  5. Atanassov, K., Szmidt, E., & Kacprzyk, J. (2013) On intuitionistic fuzzy pairs. Notes on Intuitionistic Fuzzy Sets, 19(3), 1–13.
  6. Atanassova, V., Interpretation in the Intuitionistic Fuzzy Triangle of the Results, Obtained by the InterCriteria Analysis, 16th World Congress of the International Fuzzy Systems Association (IFSA), 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), 30. 06-03. 07. 2015, Gijon, Spain, 2015, 1369–1374.
  7. Atanassova, V., Vardeva, I, Sotirova, E., & Doukovska, L. (2016) Traversing and ranking of elements of an intuitionistic fuzzy set in the intuitionistic fuzzy interpretation triangle, Chapter, Novel Developments in Uncertainty Representation and Processing, Vol. 401, Advances in Intelligent Systems and Computing, 161–174.
  8. Bastin, G., & Dochain, D. (1991) On-line Estimation and Adaptive Control of Bioreactors. Elsevier Science Publications.
  9. Bureva, V., Sotirova, E., Atanassova, V., Angelova, N., & Atanassov, K. (2018) Intercriteria Analysis over Intuitionistic Fuzzy Data, Springer LNCS, Vol. 10655, 2018, 333–340.
  10. Doukovska L., Atanassova V., Sotirova E., Vardeva I., Radeva I. (2019) Defining Consonance Thresholds in InterCriteria Analysis: An Overview. In: Intuitionistic Fuzziness and Other Intelligent Theories and Their Applications. SCI, Vol 757. Springer, Cham.
  11. Goldberg, D. E. (2006). Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, London.
  12. Ikonomov, N., Vassilev, P., Roeva, O. (2018) ICrAData – Software for InterCriteria Analysis, Int J Bioautomation, 22(1), 1–10.
  13. Intercriteria.net. Available online: http://intercriteria.net/
  14. Mavrov, D. (2015) Software for InterCriteria Analysis: Implementation of the main algorithm, Notes on Intuitionistic Fuzzy Sets, 21(2), 77–86.
  15. Mavrov, D. (2015-2016) Software for intercriteria analysis:working with the results. Annual of “Informatics” Section, Union of Scientists in Bulgaria, 8, 37–44.
  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
  23. Sekulić, M., Pejic, V., Brezocnik, M., Gostimirović, M., & Hadžistević M. (2018) Prediction of surface roughness in the ball-end milling process using response surface methodology, genetic algorithms, and grey Wolf optimizer algorithm. Advances in Production Engineering & Management, 13, 18–30.
  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.
  25. Zhang, Y., He, F., Lu, G., & Xiong, H. (2018) An imporosity message scheduling based on modified genetic algorithm for time-triggered Ethernet. Science China, Information Sciences, 61(1), 019102.
  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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