Some new types induced complex intuitionistic fuzzy Einstein geometric aggregation operators and their application to decision-making problem

被引:2
作者
Rahman, Khaista [1 ]
机构
[1] Department of Mathematics, Shaheed Benazir Bhutto University Sheringal, Sheringal
关键词
CIF-Sets; Decision-making; Einstein operators; Induced Einstein operators;
D O I
10.1007/s00521-024-10214-1
中图分类号
学科分类号
摘要
The objective of this research is to develop some novel operational laws based of T-norm and T-conorm and then using these operational laws to develop several Einstein operators for aggregating the different complex intuitionistic fuzzy numbers (CIFNs) by considering the dependency between the pairs of its membership degrees. In the existing studies of fuzzy and its extensions, the uncertainties present in the data are handled with the help of degrees of membership that are the subset of real numbers, which may also loss some valuable data and hence consequently affect the decision results. A modification to these, complex intuitionistic fuzzy set handles the uncertainties with the degree whose ranges are extended from real subset to the complex subset with unit disk and hence handle the two-dimensional information in a single set. Thus, motivated by this and this paper we present some novel methods such as complex intuitionistic fuzzy Einstein weighted geometric aggregation (CIFEWGA) operator, complex intuitionistic fuzzy Einstein ordered weighted geometric aggregation (CIFEOWGA) operator, complex intuitionistic fuzzy Einstein hybrid geometric aggregation (CIFEHGA) operator, induced complex intuitionistic fuzzy Einstein ordered weighted geometric aggregation (I-CIFEOWGA) operator and induced complex intuitionistic fuzzy Einstein hybrid geometric aggregation (I-CIFEHGA) operator. We present some of their desirable properties such as idempotency, boundedness and monotonicity. Furthermore, based on these methods a multi-attribute group decision-making problem developed under complex intuitionistic fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to show the effectiveness, importance and efficiency of the novel approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
引用
收藏
页码:21647 / 21669
页数:22
相关论文
共 54 条
[1]  
Zadeh L.A., Fuzzy sets, Inf Control, 8, pp. 338-353, (1965)
[2]  
Cao B., Zhao J., Lyu Z., Gu Y., Yang P., Halgamuge S.K., Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction, IEEE Trans Fuzzy Syst, 28, 5, pp. 939-952, (2020)
[3]  
Zheng W., Deng P., Gui K., Wu X., An abstract syntax tree based static fuzzing mutation for vulnerability evolution analysis, Inf Softw Technol, 158, (2023)
[4]  
Atanassov K.T., Intuitionistic fuzzy sets, Fuzzy Sets Syst, 20, 1, pp. 87-96, (1986)
[5]  
Wang W., Liu X., Intuitionistic fuzzy geometric aggregation operators based on Einstein operations, Int J Intell Syst, 26, 11, pp. 1049-1075, (2011)
[6]  
Wang W., Liu X., Intuitionistic fuzzy information aggregation using Einstein operations, IEEE Trans Fuzzy Syst, 20, 5, pp. 923-938, (2012)
[7]  
Hadzikadunic A., Stevic Z., Badi I., Roso V., Evaluating the logistics performance index of European union countries: an integrated multi-criteria decision-making approach utilizing the Bonferroni operator, Int J Knowl Innov Stud, 1, 1, pp. 44-59, (2023)
[8]  
Yu D., Shi S., Researching the development of Atanassov intuitionistic fuzzy set: using a citation network analysis, Appl Soft Comput, 32, pp. 189-198, (2015)
[9]  
Garg H., Agarwal N., Tripathi A., Entropy based multi-criteria decision making method under fuzzy environment and unknown attribute weights, Glob J Technol Optim, 6, 3, pp. 13-20, (2015)
[10]  
Kumar K., Garg H., TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment, Comput Appl Math, 37, 2, pp. 1319-1329, (2016)