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Complex Interval-valued Intuitionistic Fuzzy Sets and their Aggregation Operators

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The objective of this manuscript is to present the concept of the complex interval-valued intuitionistic fuzzy (CIVIF) set, their algebraic operations and their corresponding aggregation operators, which can better represent the time-periodic problems and two-dimensional information in a single set. The proposed CIVIF set includes the characteristics of both complex intuitionistic fuzzy set, as well as the interval-valued intuitionistic fuzzy sets. Some of the basic operational laws and their properties have been investigated in details. Also, we have developed some new weighted and ordered weighted averaging and geometric aggregation operators with complex interval-valued intuitionistic fuzzy information. The proposed operations are the generalization of the operations of interval-valued intuitionistic fuzzy, complex fuzzy and complex intuitionistic fuzzy theories. Furthermore, a group decision-making method is established based on these operators. Finally, an illustrative example is used to illustrate the applicability and validity of the proposed approach and compare the results with the existing methods to show the effectiveness of it.
Wydawca
Rocznik
Strony
61--101
Opis fizyczny
Bibliogr. 43 poz., tab.
Twórcy
autor
  • School of Mathematics, Thapar Institute of Engineering & Technology (Deemed University), Patiala 147004, Punjab, India
autor
  • School of Mathematics, Thapar Institute of Engineering & Technology (Deemed University), Patiala 147004, Punjab, India
Bibliografia
  • [1] Garg H. Some arithmetic operations on the generalized sigmoidal fuzzy numbers and its application. Granular Computing, 2018.3(1):9-25. doi:10.1007/s41066-017-0052-7.
  • [2] Garg H, Ansha. Arithmetic operations on Generalized Parabolic fuzzy numbers and its Application. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2018.88(1):15-26. doi:10.1007/s40010-016-0278-9.
  • [3] Zadeh LA. Fuzzy sets. Information and Control, 1965. 8:338-353.
  • [4] Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 1986.20:87-96. URL https://doi.org/10.1016/S0165-0114(86)80034-3.
  • [5] Atanassov K, Gargov G. Interval-valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 1989.31:343-349. URL https://doi.org/10.1016/0165-0114(89)90205-4.
  • [6] Xu ZS, Yager RR. Some geometric aggregation operators based on intuitionistic fuzzy sets. International Journal of General Systems, 2006.35:417-433. URL https://doi.org/10.1080/03081070600574353.
  • [7] Xu ZS. Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control and Decision, 2007.22(2):215-219.
  • [8] Garg H. Generalized intuitionistic fuzzy interactive geometric interaction operators using Einstein t-norm and t-conorm and their application to decision making. Computers and Industrial Engineering, 2016.101:53-69. URL https://doi.org/10.1016/j.cie.2016.08.017.
  • [9] Garg H. Novel intuitionistic fuzzy decision making method based on an improved operation laws and its application. Engineering Applications of Artificial Intelligence, 2017.60:164-174. URL https://doi.org/10.1016/j.engappai.2017.02.008.
  • [10] Yager RR. On ordered weighted avergaing aggregation operators in multi-criteria decision making. IEEE Transactions on Systems, Man and Cybernetics, 1988. 18(1):183-190.
  • [11] Garg H. Some robust improved geometric aggregation operators under interval-valued intuitionistic fuzzy environment for multi-criteria decision -making process. Journal of Industrial & Management Optimization, 2018.14(1):283-308.
  • [12] Wang W, Liu X. The multi-attribute decision making method based on interval-valued intuitionistic fuzzy Einstein hybrid weighted geometric operator. Computers and Mathematics with Applications, 2013.66:1845-1856. URL https://doi.org/10.1016/j.camwa.2013.07.020.
  • [13] Kumar K, Garg H. TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment. Computational and Applied Mathematics, 2018.37(2):1319-1329. doi:10.1007/s40314-016-0402-0.
  • [14] Garg H. A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Applied Soft Computing, 2016.38:988-999. URL https://doi.org/10.1016/j.asoc.2015.10.040.
  • [15] Zhang X, Yue G, Teng Z. Possibility degree of interval - valued intuitionistic fuzzy numbers and its application. In: Proceedings of the International Symposium on Information Processing (ISIP09). 2009 pp. 33-36.
  • [16] Garg H, Kumar K. Improved possibility degree method for ranking intuitionistic fuzzy numbers and their application in multiattribute decision-making. Granular Computing, 2018. pp. 1-11. doi:10.1007/s41066-018-0092-7.
  • [17] Chen SM, Chang CH. A novel similarity measure between Atanassov’s intuitionistic fuzzy sets based on transformation techniques with applications to pattern recognition. Information Sciences, 2015. 291:96-114. URL https://doi.org/10.1016/j.ins.2014.07.033.
  • [18] Garg H, Kumar K. An advanced study on the similarity measures of intuitionistic fuzzy sets based on the set pair analysis theory and their application in decision making. Soft Computing, 2018.22(15):4959-4970. doi:10.1007/s00500-018-3202-1.
  • [19] Liu P. Some Hamacher Aggregation Operators Based on the Interval-valued Intuitionistic fuzzy numbers and their application to Group Decision making. IEEE Transactions on Fuzzy Systems, 2014.22(1):83-97. doi:10.1109/TFUZZ.2013.2248736.
  • [20] Chen SM, Cheng SH, Tsai WH. Multiple attribute group decision making based on interval-valued intuitionistic fuzzy aggregation operators and transformation techniques of interval - valued intuitionistic fuzzy values. Information Sciences, 2016.367-368(1):418-442. URL https://doi.org/10.1016/j.ins.2016.05.041.
  • [21] Garg H. Generalized intuitionistic fuzzy entropy-based approach for solving multi-attribute decision-making problems with unknown attribute weights. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2017. pp. 1-11. doi:10.1007/s40010-017-0395-0.
  • [22] Wei GW, Wang HJ, Lin R. Application of correlation coefficient to interval-valued intuitionistic fuzzy multiple attribute decision-making with incomplete weight information. Knowledge and Information Systems, 2011.26(2):337-349. doi:10.1007/s10115-009-0276-1.
  • [23] Garg H, Kumar K. Some aggregation operators for linguistic intuitionistic fuzzy set and its application to group decision-making process using the set pair analysis. Arabian Journal for Science and Engineering, 2018.43(6):3213-3227. doi:10.1007/s13369-017-2986-0.
  • [24] Singh S, Garg H. Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process. Applied Intelligence, 2017.46(4):788-799. doi:10.1007/s10489-016-0869-9.
  • [25] Garg H, Singh S. A novel triangular interval type-2 intuitionistic fuzzy sets and their aggregation operators. Iranian Journal of Fuzzy Systems, 2018. doi:10.22111/IJFS.2018.3559.
  • [26] Chen SM, Cheng SH, Tsai WH. A novel multiple attribute decision making method based on interval-valued intuitionistic fuzzy geometric averaging operators. In: 8th International Conference on Advanced Computational Intelligence Chiang Mai, Thailand. 2016 pp. 79-83. doi:10.1109/ICACI.2016.7449807.
  • [27] Wang W, Liu X. Interval-valued intuitionistic fuzzy hybrid weighted averaging operator based on Einstein operation and its application to decision making. Journal of Intelligent and Fuzzy Systems, 2013.25(2):279-290. doi:10.3233/IFS-120635.
  • [28] Arora R, Garg H. A robust correlation coefficient measure of dual hesistant fuzzy soft sets and their application in decision making. Engineering Applications of Artificial Intelligence, 2018. 72:80-92. URL https://doi.org/10.1016/j.engappai.2018.03.019.
  • [29] Garg H, Arora R. Generalized and Group-based Generalized intuitionistic fuzzy soft sets with applications in decision-making. Applied Intelligence, 2018.48(2):343-356. doi:10.1007/s10489-017-0981-5.
  • [30] Xu ZS. Intuitionistic fuzzy aggregation operators. IEEE Transactions of Fuzzy Systems, 2007.15:1179-1187. doi:10.1109/TFUZZ.2006.890678.
  • [31] Garg H, Arora R. Novel scaled prioritized intuitionistic fuzzy soft interaction averaging aggregation operators and their application to multi criteria decision making. Engineering Applications of Artificial Intelligence, 2018.71C:100-112. URL https://doi.org/10.1016/j.engappai.2018.02.005.
  • [32] Xu ZS. An overview of methods for determining OWA weights. International Journal of Intelligent Systems, 2005.20:843-865. doi:10.1002/int.v20:8.
  • [33] Kaur G, Garg H. Multi - Attribute Decision - Making Based on Bonferroni Mean Operators under Cubic Intuitionistic Fuzzy Set Environment. Entropy, 2018.20(1):65. doi:10.3390/e20010065.
  • [34] Kaur G, Garg H. Cubic Intuitionistic fuzzy aggregation operators. International Journal for Uncertainty Quantification, 2018. 8(5):405-428.
  • [35] Ramot D, Milo R, Fiedman M, Kandel A. Complex Fuzzy sets. IEEE Transactions on Fuzzy Systems, 2002.10(2):171-186. doi:10.1109/91.995119.
  • [36] Ramot D, Friedman M, Langholz G, Kandel A. Complex fuzzy logic. IEEE Transactions on Fuzzy Systems, 2003.11(4):450-461. doi:10.1109/TFUZZ.2003.814832.
  • [37] Alkouri A, Salleh A. Complex intuitionistic fuzzy sets, volume 1482, chapter 2nd International Conference on Fundamental and Applied Sciences 2012, pp. 464-470. 2012. doi:10.1063/1.4757515.
  • [38] Alkouri AUM, Salleh AR. Complex Atanassov’s Intuitionistic Fuzzy Relation. Abstract and Applied Analysis, 2013.2013:Article ID 287382, 18 pages. URL http://dx.doi.org/10.1155/2013/287382.
  • [39] Kumar T, Bajaj RK. On complex Intuitionistic Fuzzy Soft Sets with Distance measures and Entropies. Journal of Mathematics, 2014.2014:Article ID 972198, 12 pages. URL http://dx.doi.org/10.1155/2014/972198.
  • [40] Rani D, Garg H. Distance measures between the complex intuitionistic fuzzy sets and its applications to the decision - making process. International Journal for Uncertainty Quantification, 2017.7(5):423-439. doi:10.1615/Int.J.UncertaintyQuantification.2017020356.
  • [41] Rani D, Garg H. Complex intuitionistic fuzzy power aggregation operators and their applications in multi-criteria decision-making. Expert Systems., 2018. pp. e12325. doi:10.1111/exsy.12325.
  • [42] Garg H, Rani D. Some Generalized Complex Intuitionistic Fuzzy Aggregation Operators and Their Application to Multicriteria Decision-Making Process. Arabian Journal for Science and Engineering, 2018, pp. 1-20. doi:https://doi.org/10.1007/s13369-018-3413-x.
  • [43] Arora R, Garg H. Prioritized averaging/geometric aggregation operators under the intuitionistic fuzzy soft set environment. Scientia Iranica, 2018. 25(1):466-482.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-deb2e6a2-64f3-4f2d-be2b-79ab5c466145
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