The circular intuitionistic fuzzy set (CIFS) is an extension of the intuitionistic fuzzy set (IFS), where each element is represented as a circle in the IFS interpretation triangle (IFIT) instead of a point. The center of the circle corresponds to the coordinate formed by membership ($ \mathcal{M} $) and non-membership ($ \mathcal{N} $) degrees, while the radius, $ r $, represents the imprecise area around the coordinate. However, despite enhancing the representation of IFS, CIFS remains limited to the rigid $ IFIT $ space, where the sum of $ \mathcal{M} $ and $ \mathcal{N} $ cannot exceed one. In contrast, the generalized IFS (GIFS) allows for a more flexible IFIT space based on the relationship between $ \mathcal{M} $ and $ \mathcal{N} $ degrees. To address this limitation, we propose a generalized circular intuitionistic fuzzy set (GCIFS) that enables the expansion or narrowing of the IFIT area while retaining the characteristics of CIFS. Specifically, we utilize the generalized form introduced by Jamkhaneh and Nadarajah. First, we provide the formal definitions of GCIFS along with its relations and operations. Second, we introduce arithmetic and geometric means as basic operators for GCIFS and then extend them to the generalized arithmetic and geometric means. We thoroughly analyze their properties, including idempotency, inclusion, commutativity, absorption and distributivity. Third, we define and investigate some modal operators of GCIFS and examine their properties. To demonstrate their practical applicability, we provide some examples. In conclusion, we primarily contribute to the expansion of CIFS theory by providing generality concerning the relationship of imprecise membership and non-membership degrees.
Citation: Dian Pratama, Binyamin Yusoff, Lazim Abdullah, Adem Kilicman. The generalized circular intuitionistic fuzzy set and its operations[J]. AIMS Mathematics, 2023, 8(11): 26758-26781. doi: 10.3934/math.20231370
The circular intuitionistic fuzzy set (CIFS) is an extension of the intuitionistic fuzzy set (IFS), where each element is represented as a circle in the IFS interpretation triangle (IFIT) instead of a point. The center of the circle corresponds to the coordinate formed by membership ($ \mathcal{M} $) and non-membership ($ \mathcal{N} $) degrees, while the radius, $ r $, represents the imprecise area around the coordinate. However, despite enhancing the representation of IFS, CIFS remains limited to the rigid $ IFIT $ space, where the sum of $ \mathcal{M} $ and $ \mathcal{N} $ cannot exceed one. In contrast, the generalized IFS (GIFS) allows for a more flexible IFIT space based on the relationship between $ \mathcal{M} $ and $ \mathcal{N} $ degrees. To address this limitation, we propose a generalized circular intuitionistic fuzzy set (GCIFS) that enables the expansion or narrowing of the IFIT area while retaining the characteristics of CIFS. Specifically, we utilize the generalized form introduced by Jamkhaneh and Nadarajah. First, we provide the formal definitions of GCIFS along with its relations and operations. Second, we introduce arithmetic and geometric means as basic operators for GCIFS and then extend them to the generalized arithmetic and geometric means. We thoroughly analyze their properties, including idempotency, inclusion, commutativity, absorption and distributivity. Third, we define and investigate some modal operators of GCIFS and examine their properties. To demonstrate their practical applicability, we provide some examples. In conclusion, we primarily contribute to the expansion of CIFS theory by providing generality concerning the relationship of imprecise membership and non-membership degrees.
[1] | K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Set. Syst., 20 (1986), 87–96. http://doi.org/10.1016/S0165-0114(86)80034-3 doi: 10.1016/S0165-0114(86)80034-3 |
[2] | L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X |
[3] | D. Dubois, H. Prade, Interval-valued fuzzy sets, possibility theory and imprecise probability, In: Proceedings of the joint 4th conference of the European society for fuzzy logic and technology and the 11th rencontres francophones surla logique floue et ses applications, 2005,314–319. |
[4] | J. M. Mendel, Uncertain rule-based fuzzy logic systems, Cham: Springer, 2017. |
[5] | V. Torra, Y. Narukawa, On hesitant fuzzy sets and decision, In: Proceedings of the IEEE international conference on fuzzy systems, 2009, 1378–1382. |
[6] | V. Torra, Hesitant fuzzy sets, Int. J. Intell. Syst., 25 (2010), 529–539. https://doi.org/10.1002/int.20418 doi: 10.1002/int.20418 |
[7] | K. T. Atanassov, G. Gargov, Interval valued intuitionistic fuzzy sets, Fuzzy Sets Syst., 31 (1989), 343–349. https://doi.org/10.1016/0165-0114(89)90205-4 doi: 10.1016/0165-0114(89)90205-4 |
[8] | S. Singh, H. Garg, Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process, Appl. Intell., 46 (2017), 788–799. https://doi.org/10.1007/s10489-016-0869-9 doi: 10.1007/s10489-016-0869-9 |
[9] | J. J. Peng, J. Q. Wang, X. H. Wu, H. Y. Zhang, X. Hong The fuzzy cross-entropy for intuitionistic hesitant fuzzy sets and their application in multi-criteria decision-making, Int. J. Syst. Sci., 46 (2015), 2335–2350. http://doi.org/10.1080/00207721.2014.993744 doi: 10.1080/00207721.2014.993744 |
[10] | L. Abdullah, N. A. Awang, Weight for TOPSIS method combined with intuitionistic fuzzy sets in multi-criteria decision making, Recent advances in soft computing and data mining, Cham: Springer, 2022. https://doi.org/10.1007/978-3-031-00828-3_20 |
[11] | H. Hashemi, S. M. Mousavi, E. K. Zavadskas, A. Chalekaee, Z. Turskis, A new group decision model based on grey-intuitionistic fuzzy-ELECTRE and VIKOR for contractor assessment problem, Sustainability, 10 (2018), 1635. http://doi.org/10.3390/su10051635 doi: 10.3390/su10051635 |
[12] | J. Jin, H. Garg, Intuitionistic fuzzy three-way ranking-based TOPSIS approach with a novel entropy measure and its application to medical treatment selection, Adv. Eng. Soft., 180 (2023), 103459. https://doi.org/10.1016/j.advengsoft.2023.103459 doi: 10.1016/j.advengsoft.2023.103459 |
[13] | F. Dammak, L. Baccour, A. M. Alimi, Intuitionistic fuzzy PROMETHEE Ⅱ technique for multi-criteria decision making problems based on distance and similarity measures, IEEE Int. Conf. Fuzzy Syst., 2020. https://doi.org/10.1109/FUZZ48607.2020.9177619 doi: 10.1109/FUZZ48607.2020.9177619 |
[14] | E. Szmidt, J. Kacprzyk, Intuitionistic fuzzy sets in some medical applications, In: Computational intelligence theory and applications, Berlin, Heidelberg: Springer, 2001. http://doi.org/10.1007/3-540-45493-4_19 |
[15] | U. Shuaib, H. Alolaiyan, A. Razaq, S. Dilbar, F. Tahir, On some algebraic aspects of $\eta$-intuitionistic fuzzy subgroups, J. Taibah Univ. Sci., 14 (2020), 463–469. http://doi.org/10.1080/16583655.2020.1745491 doi: 10.1080/16583655.2020.1745491 |
[16] | N. K. Akula, S. S Basha, Regression coefficient measure of intuitionistic fuzzy graphs with application to soil selection for the best paddy crop, AIMS Mathematics, 8 (2023), 17631–17649. https://doi.org/10.3934/math.2023900 doi: 10.3934/math.2023900 |
[17] | M. Akram, R. Akmal, Operations on intuitionistic fuzzy graph structures, Fuzzy Inf. Eng., 8 (2016), 389–410. https://doi.org/10.1016/j.fiae.2017.01.001 doi: 10.1016/j.fiae.2017.01.001 |
[18] | Z. W. Wei, L. Zhou, On intuitionistic fuzzy topologies based on intuitionistic fuzzy reflexive and transitive relations, Soft Comput., 15 (2011), 1183–1194. https://doi.org/10.1007/s00500-010-0576-0 doi: 10.1007/s00500-010-0576-0 |
[19] | Z. Xu, R. R. Yager, Some geometric aggregation operators based on intuitionistic fuzzy sets, Inter. J. General Syst., 35 (2006), 417–433. https://doi.org/10.1080/03081070600574353 doi: 10.1080/03081070600574353 |
[20] | H. Zhao, Z. Xu, M. Ni, S. Liu, Generalized aggregation operators for intuitionistic fuzzy sets, Inter. J. Intell. Syst., 25 (2010), 1–30. https://doi.org/10.1002/int.20386 doi: 10.1002/int.20386 |
[21] | W. Azeem, W. Mahmood, T. Mahmood, Z. Ali, M. Naeem, Analysis of Einstein aggregation operators based on complex intuitionistic fuzzy sets and their applications in multi-attribute decision-making, AIMS Mathematics, 8 (2023), 6036–6063. https://doi.org/10.3934/math.2023305 doi: 10.3934/math.2023305 |
[22] | G. Deschrijver, E. E. Kerre, A generalisation of operators on intuitionistic fuzzy sets using triangular norms and conorms, Notes Intuitionistic Fuzzy Sets, 8 (2002), 19–27. |
[23] | B. Yusoff, I. Taib, L. Abdullah, A. F. Wahab, A new similarity measure on intuitionistic fuzzy sets, Inter. J. Math. Comput. Sci., 5 (2011), 819–823. https://doi.org/10.5281/zenodo.1054905 doi: 10.5281/zenodo.1054905 |
[24] | R. R. Yager, A note on measuring fuzziness for intuitionistic and interval-valued fuzzy sets, Inter. J. Gen. Syst., 4 (2013), 889–901. https://doi.org/10.1080/03081079.2015.1029472 doi: 10.1080/03081079.2015.1029472 |
[25] | W. S. Du, Subtraction and division operations on intuitionistic fuzzy sets derived from the Hamming distance, Inform. Sci., 571 (2021), 206–224. https://doi.org/10.1016/j.ins.2021.04.068 doi: 10.1016/j.ins.2021.04.068 |
[26] | T. K. Mondal, S. K. Samanta, Generalized intuitionistic fuzzy sets, J. Fuzzy Math., 10 (2002), 839–862. https://doi.org/10.3390/math9172115 doi: 10.3390/math9172115 |
[27] | H. C. Liu, Liu's generalized intuitionistic fuzzy sets, J. Educ. Meas. Stat., 18 (2010), 1–14. |
[28] | I. Despi, D. Opris, E. Yalcin, Generalised Atanassov Intuitionistic Fuzzy Sets, In: Proceedings of the fifth international conference on information, process and knowledge management, 2013, 51–56. |
[29] | E. B. Jamkhaneh, S. Nadarajah, A new generalized intuitionistic fuzzy set, Hacettepe J. Math. Stat., 44 (2015), 111–122. http://doi.org/10.15672/HJMS.2014367557 doi: 10.15672/HJMS.2014367557 |
[30] | K. T. Atanassov, A second type of intuitionistic fuzzy sets, BUSEFAL, 56 (1993), 66–70. |
[31] | K. T. Atanassov, P. Vassilev, On the intuitionistic fuzzy sets of n-th type, In: Advances in data analysis with computational intelligence methods, Cham: Springer, 2018. http://doi.org/10.1007/978-3-319-67946-4_10 |
[32] | R. Srinivasan, N. Palaniappan, Some operations on intuitionistic fuzzy sets of root type, Ann. Fuzzy Math. Inform., 4 (2012), 377–383. |
[33] | R. R. Yager, Pythagorean fuzzy subsets, In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, 2013. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375 |
[34] | R. R. Yager, Generalized orthopair fuzzy sets, IEEE Trans. Fuzzy Syst., 25 (2017), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005 doi: 10.1109/TFUZZ.2016.2604005 |
[35] | T. Senapati, R. R. Yager, Fermatean fuzzy sets, J. Ambient Intell. Human. Comput., 11 (2020), 663–674. https://doi.org/10.1007/s12652-019-01377-0 doi: 10.1007/s12652-019-01377-0 |
[36] | E. B. Jamkhaneh, New operations over generalized interval valued intuitionistic fuzzy sets, Gazi Univ. J. Sci., 29 (2016), 667–674. |
[37] | E. B. Jamkhaneh, A. N. Ghara, Four new operators over the generalized intuitionistic fuzzy sets, J. New Theory, 18 (2017), 12–21. |
[38] | D. Sadhanaa, P. Prabakaran, Level operators on generalized intuitionistic fuzzy sets, Inter. J. Math. Trends Tech., 62 (2018), 152–157. |
[39] | Z. Roohanizadeh, E. B. Jamkhaneh, The reliability analysis based on the generalized intuitionistic fuzzy two-parameter Pareto distribution, Soft Comput., 27 (2022), 3095–3113. https://doi.org/10.1007/s00500-022-07494-x doi: 10.1007/s00500-022-07494-x |
[40] | K. T. Atanassov, Circular intuitionistic fuzzy sets, J. Intell. Fuzzy Syst., 39 (2020), 5981–5986. http://doi.org/10.3233/JIFS-189072 doi: 10.3233/JIFS-189072 |
[41] | K. T. Atanassov, E. Marinov, Four distances for circular intuitionistic fuzzy sets, Mathematics, 9 (2021), 1121. http://doi.org/10.3390/math9101121 doi: 10.3390/math9101121 |
[42] | T. Y. Chen, Evolved distance measures for circular intuitionistic fuzzy sets and their exploitation in the technique for order preference by similarity to ideal solutions, Artif. Intell. Rev., 56 (2022), 7347–7401. http://doi.org/10.1007/s10462-022-10318-x doi: 10.1007/s10462-022-10318-x |
[43] | M. J. Khan, W. Kumam, N. A. Alreshidi, Divergence measures for circular intuitionistic fuzzy sets and their applications, Eng. Appl. Artif. Intell., 116 (2022), 105455. https://doi.org/10.1016/j.engappai.2022.105455 doi: 10.1016/j.engappai.2022.105455 |
[44] | C. Kahraman, N. Alkan, Circular intuitionistic fuzzy TOPSIS method with vague membership functions: Supplier selection application context, Notes Intuitionistic Fuzzy Sets, 27 (2021), 24–52. http://doi.org/10.7546/nifs.2021.27.1.24-52 doi: 10.7546/nifs.2021.27.1.24-52 |
[45] | I. Otay, C. Kahraman, A novel circular intuitionistic fuzzy AHP and VIKOR methodology: An application to a multi-expert supplier evaluation problem, Pamukkale Univ. J. Eng. Sci., 28 (2021), 194–207. https://doi.org/10.5505/pajes.2021.90023 doi: 10.5505/pajes.2021.90023 |
[46] | N. Alkan, C. Kahraman, Circular intuitionistic fuzzy TOPSIS method: Pandemic hospital location selection, J. Intell. Fuzzy Syst., 42 (2022), 295–316. http://doi.org/10.3233/JIFS-219193 doi: 10.3233/JIFS-219193 |
[47] | E. Bolturk, C. Kahraman, Interval-valued and circular intuitionistic fuzzy present worth analyses, Informatica, 33 (2022), 693–711. http://doi.org/10.15388/22-INFOR478 doi: 10.15388/22-INFOR478 |
[48] | E. B. Jamkhaneh, H. Garg, Some new operations over the generalized intuitionistic fuzzy sets and their application to decision-making process, Granul. Comput., 3 (2018), 111–122. http://doi.org/10.1007/s41066-017-0059-0 doi: 10.1007/s41066-017-0059-0 |
[49] | P. Dutta, B. Saikia, Arithmetic operations on normal semi elliptic intuitionistic fuzzy numbers and their application in decision making, Granul. Comput., 6 (2021), 163–179. https://doi.org/10.1007/s41066-019-00175-5 doi: 10.1007/s41066-019-00175-5 |
[50] | K. T. Atanassov, On one type of intuitionistic fuzzy modal operators, Notes Intuitionistic Fuzzy Sets, 11 (2005), 24–28. |