Skip to main content
Log in

New prioritized aggregation operators with priority degrees among priority orders for complex intuitionistic fuzzy information

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

This paper aims to present some novel prioritized aggregation operators for aggregating complex intuitionistic fuzzy values (CIFVs). The aggregation operators are developed by assigning non-negative real numbers called as priority degrees among strict priority levels. The presented work is divided into three folds. The first fold is that uncertainties in the data are represented using CIFVs which have the characteristic of portraying membership and non-membership degrees over the unit disc of the complex plane. The second fold is to present prioritized averaging and geometric operators without priority degrees, prioritized averaging and geometric operators with priority degrees, prioritized ordered weighted averaging and geometric operators with priority degrees based on basic unit interval monotonic (BUM) function for aggregating dependent CIFVs. A number of propositions related to proposed operators are proved. The third fold is that a group decision-making approach based on proposed operators is developed and is applied on a decision-making problem. The results of proposed method are compared with several existing studies. The comparative study results reveal that the proposed approach is valid and gives fair results. The characteristics of the developed method are also compared with several existing approaches which highlight the superiority of the presented work over prevailing techniques. Besides this, the role of the priority degrees on aggregation result is also discussed in detail.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ali M, Smarandache F (2017) Complex neutrosophic set. Neural Comput Appl 28(7):1817–1834

    Google Scholar 

  • Ali M, Dat LQ, Son LH, Smarandache F (2018) Interval complex neutrosophic set: formulation and applications in decision-making. Int J Fuzzy Syst 20:986–999

    Google Scholar 

  • Alkouri AMJS, Salleh AR (2012) Complex intuitionistic fuzzy sets. In: AIP conference proceedings, vol 1482. American Institute of Physics, pp 464–470

  • Alkouri AM, Salleh AR (2013) Complex Atanassov’s intuitionistic fuzzy relation. Abstr Appl Anal 2013:287382

    MathSciNet  MATH  Google Scholar 

  • Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96

    MATH  Google Scholar 

  • Bakbak D, Ulucay V (2019) Multicriteria decision-making method using the cosine vector similarity measure under intuitionistic trapezoidal fuzzy multi-numbers in architecture. In: 6th International Multidisciplinary Studies Congress (Multicongress’ 19) Gaziantep, TĂĽrkiye

  • Bakbak D, Ulucay V, Sahin M (2019) Intuitionistic trapezoidal fuzzy multi-numbers and some arithmetic averaging operators with their application in architecture. In: 6th international multidisciplinary studies congress (Multicongress’ 19) Gaziantep, TĂĽrkiye

  • Bi L, Dai S, Hu B (2018) Complex fuzzy geometric aggregation operators. Symmetry 10(7):251

    Google Scholar 

  • Dai S, Bi L, Hu B (2019) Distance measures between the interval-valued complex fuzzy sets. Mathematics 7(6):549

    Google Scholar 

  • Dick S, Yager RR, Yazdanbakhsh O (2016) On pythagorean and complex fuzzy set operations. IEEE Trans Fuzzy Syst 24(5):1009–1021

    Google Scholar 

  • Gao H (2018) Pythagorean fuzzy hamacher prioritized aggregation operators in multiple attribute decision making. J Intell Fuzzy Syst 35:2229–2245

    Google Scholar 

  • Garg H (2016a) Generalized intuitionistic fuzzy interactive geometric interaction operators using Einstein t-norm and t-conorm and their application to decision making. Comput Ind Eng 101:53–69

    Google Scholar 

  • Garg H (2016b) Some series of intuitionistic fuzzy interactive averaging aggregation operators. Springer Plus 5(1):1–27

    Google Scholar 

  • Garg H (2016c) A new generalized pythagorean fuzzy information aggregation using Einstein operations and its application to decision making. Int J Intell Syst 31:886–920

    Google Scholar 

  • Garg H (2017) Generalized Pythagorean fuzzy geometric aggregation operators using Einstein t-norm and t-conorm for multicriteria decision-making process. Int J Intell Syst 32(6):597–630

    Google Scholar 

  • Garg H, Rani D (2019a) Some generalized complex intuitionistic fuzzy aggregation operators and their application to multicriteria decision-making process. Arab J Sci Eng 44(3):2679–2698

    Google Scholar 

  • Garg H, Rani D (2019b) A robust correlation coefficient measure of complex intuitionistic fuzzy sets and their applications in decision-making. Appl Intell 49(2):496–512

    Google Scholar 

  • Garg H, Rani D (2020a) Generalized geometric aggregation operators based on t-norm operations for complex intuitionistic fuzzy sets and their application to decision-making. Cogn Comput 12(3):679–698

    Google Scholar 

  • Garg H, Rani D (2020b) Robust averaging-geometric aggregation operators for complex intuitionistic fuzzy sets and their applications to MCDM process. Arab J Sci Eng 45:2017–2033

    Google Scholar 

  • Garg H, Rani D (2020c) Novel aggregation operators and ranking method for complex intuitionistic fuzzy sets and their applications to decision-making process. Artif Intell Rev 53:3595–3620

    Google Scholar 

  • Garg H, Gwak J, Mahmood T, Ali Z (2020) Power aggregation operators and VIKOR methods for complex q-rung orthopair fuzzy sets and their applications. Mathematics 8(4):538

    Google Scholar 

  • Gou X, Xu Z, Lei Q (2016) New operational laws and aggregation method of intuitionistic fuzzy information. J Intell Fuzzy Syst 30:129–141

    MATH  Google Scholar 

  • Gulistan M, Khan S (2020) Extentions of neutrosophic cubic sets via complex fuzzy sets with application. Complex Intell Syst 6:309–320

    Google Scholar 

  • Khalaf MM, Alharbi SO, Chammam W (2019) Similarity measures between temporal complex intuitionistic fuzzy sets and application in pattern recognition and medical diagnosis. Discrete Dyn Nat Soc 2019:1–16

    MathSciNet  MATH  Google Scholar 

  • Khan MSA, Abdullah S, Ali A, Amin F (2019) Pythagorean fuzzy prioritized aggregation operators and their application to multi-attribute group decision making. Granul Comput 4:249–263

    Google Scholar 

  • Kumar T, Bajaj RK (2014) On complex intuitionistic fuzzy soft sets with distance measures and entropies. J Math 2014:972198

    MathSciNet  MATH  Google Scholar 

  • Li B, Xu Z (2019) Prioritized aggregation operators based on the priority degrees in multicriteria decision-making. Int J Intell Syst 34(9):1985–2018

    Google Scholar 

  • Liu P, Mahmood T, Ali Z (2020) Complex q-rung orthopair fuzzy aggregation operators and their applications in multi-attribute group decision making. Information 11(1):5

    Google Scholar 

  • Ngan RT, Son LH, Ali M, Tamir DE, Rishe ND, Kandel A (2020) Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making. Appl Soft Comput 87:105961

    Google Scholar 

  • Ramot D, Milo R, Friedman M, Kandel A (2002) Complex fuzzy sets. IEEE Trans Fuzzy Syst 10(2):171–186

    Google Scholar 

  • Ramot D, Friedman M, Langholz G, Kandel A (2003) Complex fuzzy logic. IEEE Trans Fuzzy Syst 11(4):450–461

    Google Scholar 

  • Rani D, Garg H (2017) Distance measures between the complex intuitionistic fuzzy sets and its applications to the decision-making process. Int J Uncertain Quantif 7(5):423–439

    MathSciNet  MATH  Google Scholar 

  • Rani D, Garg H (2018) Complex intuitionistic fuzzy power aggregation operators and their applications in multicriteria decision-making. Expert Syst 35(6):e12325

    Google Scholar 

  • Thirunavukarasu P, Suresh R, Ashokkumar V (2017) Theory of complex fuzzy soft set and its applications. Int J Innov Res Sci Technol 3(10):13–18

    Google Scholar 

  • Ulucay V, Deli I, Sahin M (2019) Intuitionistic trapezoidal fuzzy multi-numbers and its application to multi-criteria decision-making problems. Complex Intell Syst 5:65–78

    Google Scholar 

  • Verma R, Sharma B (2015) Intuitionistic fuzzy Einstein prioritized weighted average operators and their application to multiple attribute group decision making. Appl Math Inf Sci 9(6):3095–3107

    Google Scholar 

  • Wang W, Liu X (2011) Intuitionistic fuzzy geometric aggregation operators based on Einstein operations. Int J Intell Syst 26:1049–1075

    Google Scholar 

  • Wang W, Liu X (2012) Intuitionistic fuzzy information aggregation using Einstein operations. IEEE Trans Fuzzy Syst 20(5):923–938

    Google Scholar 

  • Xu Z (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15(6):1179–1187

    Google Scholar 

  • Xu Z, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35(4):417–433

    MathSciNet  MATH  Google Scholar 

  • Yager RR (2008) Prioritized aggregation operators. Int J Approx Reason 48:263–274

    MathSciNet  MATH  Google Scholar 

  • Yager RR (2009) Prioritized OWA aggregation. Fuzzy Optim Decis Mak 8:245–262

    MathSciNet  MATH  Google Scholar 

  • Yazdanbakhsh O, Dick S (2018) A systematic review of complex fuzzy sets and logic. Fuzzy Sets Syst 338:1–22

    MathSciNet  MATH  Google Scholar 

  • Yu D (2011) Intuitionistic fuzzy prioritized operators and their application in multi-criteria group decision making. Technol Econ Dev Econ 19(1):1–21

    MathSciNet  Google Scholar 

  • Yu D (2012) Group decision making based on generalized intuitionistic fuzzy prioritized geometric operator. Int J Intell Syst 27:635–661

    Google Scholar 

  • Yu W, Zhang Z, Zhong Q (2019) Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach. Ann Oper Res 1–24. https://doi.org/10.1007/s10479-019-03432-7

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    MATH  Google Scholar 

  • Zhang Z, Yu W, Martinez L, Gao Y (2019) Managing multigranular unbalanced hesitant fuzzy linguistic information in multiattribute large-scale group decision making: A linguistic distribution-based approach. IEEE Trans Fuzzy Syst 28:2875–2889

    Google Scholar 

  • Zhang Z, Gao Y, Li Z (2020a) Consensus reaching for social network group decision making by considering leadership and bounded confidence. Knowl Based Syst 204:106240

    Google Scholar 

  • Zhang Z, Kou X, Yu W, Gao Y (2020b) Consistency improvement for fuzzy preference relations with self-confidence: an application in two-sided matching decision making. J Oper Res Soc. https://doi.org/10.1080/01605682.2020.1748529

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Harish Garg.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Proof of Proposition 1

For \((d_1,d_2, \ldots , d_{n-1})\rightarrow (1,1, \ldots , 1)\) we obtain that \(T_v^{(d)}=\prod \nolimits _{k=1}^{v-1} \big (S({\mathcal {G}}_k)\big )^{d_k}\rightarrow \prod \nolimits _{k=1}^{v-1} S({\mathcal {G}}_k)=T_v\), which gives that \(\xi _v^{(d)}\rightarrow \xi _v\). Hence,

$$\begin{aligned}&\lim _{(d_1,d_2, \ldots , d_{n-1})\rightarrow (1,1, \ldots , 1)} \text {CIFPA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big )\\&\quad = \lim _{(d_1,d_2, \ldots , d_{n-1})\rightarrow (1,1, \ldots , 1)}\left( \xi _1^{(d)} {\mathcal {G}}_1 \oplus \xi _2^{(d)} {\mathcal {G}}_2 \right. \\&\qquad \left. \oplus \cdots , \oplus \xi _n^{(d)} {\mathcal {G}}_n \right) \\&\quad = \xi _1 {\mathcal {G}}_1 \oplus \xi _2 {\mathcal {G}}_2 \oplus \cdots , \oplus \xi _n {\mathcal {G}}_n \\&\quad = \text {CIFPA}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big ) \end{aligned}$$

\(\square \)

Proof of Proposition 2

For \((d_1,d_2, \ldots , d_{n-1})\rightarrow (0,0, \ldots , 0)\), we have \(T_v^{(d)}=\prod \nolimits _{k=1}^{v-1} \big (S({\mathcal {G}}_k)\big )^{d_k}=1\). It gives that \(\xi _v^{(d)}=\frac{T_v^{(d)}}{\sum \nolimits _{v=1}^n T_v^{(d)}}=\frac{1}{n}\). Hence, \(\lim _{(d_1,d_2, \ldots , d_{n-1})\rightarrow (0,0, \ldots , 0)} \text {CIFPA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big )=\frac{1}{n} \big ({\mathcal {G}}_1 \oplus {\mathcal {G}}_2 \oplus \cdots \oplus {\mathcal {G}}_n\big ).\) \(\square \)

Proof of Proposition 3

Since, \(d_1 \rightarrow +\infty \). Now, for each \(v=2,3, \ldots , n\), we have \(T_v^{(d)}=\prod \nolimits _{k=1}^{v-1} \big (S({\mathcal {G}}_k)\big )^{d_k}=\big (S({\mathcal {G}}_1)\big )^{+\infty }\big (S({\mathcal {G}}_2)\big )^{d_2} \ldots \) \(\big (S({\mathcal {G}}_{v-1})\big )^{d_{v-1}}=0\) because \(0< S({\mathcal {G}}_1) < 1\). It gives that \(\sum \nolimits _{v=1}^n T_v^{(d)}=T_1^{(d)}=1\) which implies that \(\xi _1^{(d)}=\frac{T_1^{(d)}}{\sum \nolimits _{v=1}^n T_1^{(d)}}=1\) and \(\xi _v^{(d)}=\frac{T_v^{(d)}}{\sum \nolimits _{v=1}^n T_v^{(d)}}=0\) for each \(v=2,3, \ldots , n\). Hence, \( \lim _{d_1\rightarrow +\infty } \text {CIFPA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big )={\mathcal {G}}_1\). \(\square \)

Proof of Proposition 4

Since \((d_1,d_2, \ldots , d_k, d_{k+1}) \rightarrow (0,0 \ldots , 0, +\infty )\) therefore,

$$\begin{aligned} T_v^{(d)}= & {} \prod \limits _{k=1}^{v-1} \big (S({\mathcal {G}}_k)\big )^{d_k} =\big (S({\mathcal {G}}_1)\big )^{d_1}\big (S({\mathcal {G}}_2)\big )^{d_2} \ldots \big (S({\mathcal {G}}_{v-1})\big )^{d_{v-1}}\\&\rightarrow \big (S({\mathcal {G}}_1)\big )^{0}\big (S({\mathcal {G}}_2)\big )^{0} \ldots \big (S({\mathcal {G}}_{v-1})\big )^{0}\\= & {} 1 \, \text {for each}\, v=2,3, \ldots , k+1 \\ T_v^{(d)}= & {} \prod \limits _{k=1}^{v-1} \big (S({\mathcal {G}}_k)\big )^{d_k}=\big (S({\mathcal {G}}_1)\big )^{d_1}\big (S({\mathcal {G}}_2)\big )^{d_2} \ldots \big (S({\mathcal {G}}_{v-1})\big )^{d_{v-1}}\\&\rightarrow \big (S({\mathcal {G}}_1)\big )^{0}\big (S({\mathcal {G}}_2)\big )^{0} \ldots \big (S({\mathcal {G}}_{k})\big )^{0}\big (S({\mathcal {G}}_{k+1})\big )^{+\infty }\\&\cdots \big (S({\mathcal {G}}_{v-1})\big )^{d_{v-1}}\\= & {} 0~ \forall ~ v=k+2,k+3, \ldots , n \end{aligned}$$

Thus, \(\sum \nolimits _{v=1}^n T_v^{(d)}=k+1 \) and \(\xi _v^{(d)}=\frac{T_v^{(d)}}{\sum \nolimits _{v=1}^n T_v^{(d)}} \rightarrow \frac{1}{k+1}\) for each \(v=1,2, \ldots , k+1\) and \(\xi _v^{(d)}=\frac{T_v^{(d)}}{\sum \nolimits _{v=1}^n T_v^{(d)}} \rightarrow \frac{0}{k+1}=0\) \(\forall \) \(v=k+2,k+3, \ldots , n\). Hence, \(\lim _{(d_1,d_2, \ldots , d_k, d_{k+1}) \rightarrow (0,0 \ldots , 0, +\infty )} \text {CIFPA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big )=\frac{1}{k+1}\big ({\mathcal {G}}_1 \oplus {\mathcal {G}}_2 \oplus \cdots \oplus {\mathcal {G}}_{k+1}\big ).\) \(\square \)

Proof of Proposition 5

Since \((d_1,d_2, \ldots , d_k, d_{k+1}) \rightarrow (1,1 \ldots , 1, +\infty )\) therefore,

$$\begin{aligned} T_v^{(d)}= & {} \prod \limits _{k=1}^{v-1} \big (S({\mathcal {G}}_k)\big )^{d_k} =\big (S({\mathcal {G}}_1)\big )^{d_1}\big (S({\mathcal {G}}_2)\big )^{d_2} \ldots \big (S({\mathcal {G}}_{v-1})\big )^{d_{v-1}}\\&\rightarrow S({\mathcal {G}}_1)S({\mathcal {G}}_2) \ldots S({\mathcal {G}}_{v-1})=T_v \, \text {for each}\, v=2,3, \ldots , k+1 \\ T_v^{(d)}= & {} \prod \limits _{k=1}^{v-1} \big (S({\mathcal {G}}_k)\big )^{d_k} =\big (S({\mathcal {G}}_1)\big )^{d_1}\big (S({\mathcal {G}}_2)\big )^{d_2} \ldots \big (S({\mathcal {G}}_{v-1})\big )^{d_{v-1}}\\&\rightarrow S({\mathcal {G}}_1)S({\mathcal {G}}_2) \ldots S({\mathcal {G}}_{k})\big (S({\mathcal {G}}_{k+1})\big )^{+\infty } \ldots \big (S({\mathcal {G}}_{v-1})\big )^{d_{v-1}}\\= & {} 0 ~ \text {for each}~ v=k+2, k+3, \ldots , n \end{aligned}$$

Thus, \(\sum \nolimits _{v=1}^n T_v^{(d)}\rightarrow \sum \nolimits _{v=1}^{k+1} T_v \) and \(\xi _v^{(d)}=\frac{T_v^{(d)}}{\sum \nolimits _{v=1}^n T_v^{(d)}} \rightarrow \frac{T_v}{\sum \nolimits _{v=1}^{k+1} T_v}\) for each \(v=1,2, \ldots , k+1\) and \(\xi _v^{(d)}=\frac{T_v^{(d)}}{\sum \nolimits _{v=1}^n T_v^{(d)}} \rightarrow \frac{0}{\sum \nolimits _{v=1}^{k+1} T_v}=0\) for each \(v=k+2,k+3, \ldots , n\). Hence, \(\lim _{(d_1,d_2, \ldots , d_k, d_{k+1}) \rightarrow (0,0 \ldots , 0, +\infty )} \text {CIFPA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big )=\text {CIFPA}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_{k+1}\big ).\) \(\square \)

Proof of the Proposition 6

Since \(d_k < d^{\prime }_k\)

  1. (a)

    When \(v \le k\). For convenience, let \(S({\mathcal {G}}_v)=\rho _v \). Now,

    Similarly, we can obtain that

    For simplicity, let \(\mathcal {A}=1+\rho ^{d_1}_1+\rho ^{d_1}_1\rho ^{d_2}_2 + \cdots +\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\) and \(\mathcal {B}=\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}+\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d_{k+1}}_{k+1}+ \cdots + \rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{n-1}}_{n-1}\). Then, we obtain that

    $$\begin{aligned} \xi ^{(d)}_v = \frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A}+\rho ^{d_{k}}_{k}\mathcal {B}} ~~~~ \text {and} ~~~~ \xi ^{(d^\prime )}_v = \frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A}+\rho ^{d^\prime _{k}}_{k}\mathcal {B}} \end{aligned}$$

    Now, since \(d_k < d^{\prime }_k\). It implies that \(\rho ^{d_{k}}_{k} \ge \rho ^{d^\prime _{k}}_{k}\) which leads to \(\mathcal {A}+\rho ^{d_{k}}_{k}\mathcal {B}\ge \mathcal {A}+\rho ^{d^\prime _{k}}_{k}\mathcal {B}\). It gives that \(\frac{1}{\mathcal {A}+\rho ^{d_{k}}_{k}\mathcal {B}}\le \frac{1}{ \mathcal {A}+\rho ^{d^\prime _{k}}_{k}\mathcal {B}}\) which implies that, \(\frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A}+\rho ^{d_{k}}_{k}\mathcal {B}} \le \frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A}+\rho ^{d^\prime _{k}}_{k}\mathcal {B}}\). Hence, \(\xi ^{(d)}_v \le \xi ^{\prime (d^\prime )}_v\) for \(v \le k\).

  2. (b)

    When \(v > k\), we have

    $$\begin{aligned} \xi ^{(d)}_v= & {} \frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d_{k}}_{k}\rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A}+\rho ^{d_{k}}_{k}\mathcal {B}} ~~~~ \text {and} \\ \xi ^{(d^\prime )}_v= & {} \frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d^\prime _{k}}_{k}\rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A}+\rho ^{d^\prime _{k}}_{k}\mathcal {B}} \end{aligned}$$

    Since \(d_k < d^{\prime }_k\). It implies that \(\rho ^{d_{k}}_{k} \ge \rho ^{d^\prime _{k}}_{k}\) which leads to \(\frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\le 1\). Now,

    $$\begin{aligned} \xi ^{(d)}_v= & {} \frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d_{k}}_{k}\rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A}+\rho ^{d_{k}}_{k}\mathcal {B}} \\= & {} \frac{\left( \rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d_{k}}_{k}\rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1}\right) \left( \frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\right) }{\left( \mathcal {A}+\rho ^{d_{k}}_{k}\mathcal {B}\right) \left( \frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\right) } \\= & {} \frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d^\prime _{k}}_{k}\rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A}\left( \frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\right) +\rho ^{d^\prime _{k}}_{k}\mathcal {B} } \\ \end{aligned}$$

    Further, since \(\frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\le 1\). It gives that \(\mathcal {A} \left( \frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\right) \le \mathcal {A}\) which leads to \(\mathcal {A} \left( \frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\right) + \rho ^{d^\prime _{k}}_{k}\mathcal {B}\le \mathcal {A} +\rho ^{d^\prime _{k}}_{k}\mathcal {B}\). It implies that

    $$\begin{aligned} \frac{1}{\mathcal {A} \left( \frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\right) + \rho ^{d^\prime _{k}}_{k}\mathcal {B}} \ge \frac{1}{\mathcal {A} +\rho ^{d^\prime _{k}}_{k}\mathcal {B}} \end{aligned}$$

    and therefore,

    $$\begin{aligned}&\frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d^\prime _{k}}_{k}\rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A} \left( \frac{\rho ^{d^\prime _{k}}_{k}}{\rho ^{d_{k}}_{k}}\right) + \rho ^{d^\prime _{k}}_{k}\mathcal {B}} \\&\quad \ge \frac{\rho ^{d_1}_1 \rho ^{d_2}_2 \ldots \rho ^{d_{k-1}}_{k-1}\rho ^{d^\prime _{k}}_{k}\rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1}}{\mathcal {A} +\rho ^{d^\prime _{k}}_{k}\mathcal {B}} \end{aligned}$$

    Hence, \(\xi ^{(d)}_v \ge \xi ^{\prime (d^\prime )}_v\) for \(v > k\).

\(\square \)

Proof of the Proposition 8

Since, \((d_1,d_2, \ldots , d_{n-1})\rightarrow (0,0, \ldots , 0)\). Therefore, for \(v=2,3, \ldots , n\), \(T_v^{(d)}=\prod \nolimits _{k=1}^{v-1} \rho _k^{d_k}=\rho _1^{d_1} \rho _2^{d_2} \ldots \rho _{v-1}^{d_{v-1}} \) \(\rightarrow \rho _1^0 \rho _2^0 \ldots \rho _{v-1}^0=1\) and \(T^{(d)}_1=1\). So, \(T^{(d)}= \sum \nolimits _{v=1}^n T^{(d)}_v \rightarrow n\). It gives that

$$\begin{aligned}&\frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathcal {H}_v\big \}} T^{(d)}_t}{T^{(d)}} \rightarrow \frac{v}{n}~~ \text {and}~~ \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathcal {H}_{v-1}\big \}} T^{(d)}_t}{T^{(d)}} \rightarrow \frac{v-1}{n} \\&\quad \Rightarrow ~ \xi ^{(d)}_v \rightarrow g\left( \frac{v}{n}\right) -g\left( \frac{v-1}{n}\right) \\ \end{aligned}$$

Hence,

$$\begin{aligned}&\lim _{(d_1,d_2, \ldots , d_{n-1})\rightarrow (0,0, \ldots , 0)} \text {CIFPOWA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big ) \\&\quad = \lim _{(d_1,d_2, \ldots , d_{n-1})\rightarrow (0,0, \ldots , 0)} \bigoplus \limits _{v=1}^n\xi ^{(d)}_v {\mathcal {G}}_{\sigma (v)}\\&\quad \rightarrow \bigoplus \limits _{v=1}^n \left( g\left( \frac{v}{n}\right) -g\left( \frac{v-1}{n}\right) \right) {\mathcal {G}}_{\sigma (v)} \\&\quad = \text {CIFOWA}_{g}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big ) \end{aligned}$$

\(\square \)

Proof of the Proposition 9

Since, \(d_1\rightarrow +\infty \). Therefore, for each \(v=2,3, \ldots , n\) we have, \(T^{(d)}_v=\prod \nolimits _{k=1}^{v-1} \rho ^{d_k}_k=\rho ^{d_1}_1\rho ^{d_2}_2 \ldots \rho ^{d_{v-1}}_{v-1} \rightarrow \rho ^{\infty }_1 \rho ^{d_2}_2 \ldots \rho ^{d_{v-1}}_{v-1}=0\) and \(T^{(d)}_1=1\). It implies that \(T^{(d)}=\sum \nolimits _{v=1}^n T^{(d)}_v=1\). It leads to that

$$\begin{aligned}&\text {if}~~ {\mathcal {G}}_1 \in \mathcal {H}_v ~~ \text {then} ~~ g\left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathcal {H}_v\big \}} T^{(d)}_t}{T^{(d)}}\right) =g(1)=1 ~~ \text {and}~ \\&\quad \text {if}~~{\mathcal {G}}_1 \notin \mathcal {H}_v ~~ \text {then} ~~ g\left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathcal {H}_v\big \}} T^{(d)}_t}{T^{(d)}}\right) =g(0)=0. \end{aligned}$$

Let \({\mathcal {G}}_1={\mathcal {G}}_{\sigma (k)}\) for some \(k=1,2, \ldots , n\). Then, using the Eq. (18), we obtain that \(\xi ^{(d)}_1 , \xi ^{(d)}_2 , \ldots , \xi ^{(d)}_{k-1} \rightarrow 0\), \(\xi ^{(d)}_{k} \rightarrow 1\), \(\xi ^{(d)}_{k+1} , \xi ^{(d)}_{k+2}, \ldots , \xi ^{(d)}_{n} \rightarrow 0\). Hence,

$$\begin{aligned}&\lim _{d_1\rightarrow +\infty } \text {CIFPOWA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big ) \\&\quad = \lim _{d_1\rightarrow +\infty } \bigoplus \limits _{v=1}^n \left( \xi ^{(d)}_v {\mathcal {G}}_{\sigma (v)} \right) \\&\quad \rightarrow \xi ^{(d)}_{k} {\mathcal {G}}_{\sigma (k)} \\&\quad = {\mathcal {G}}_1 \end{aligned}$$

\(\square \)

Proof of the Proposition 10

Since, \((d_1,d_2, \ldots , d_k, d_{k+1})\rightarrow (0,0, \ldots , 0, +\infty )\). Therefore, for each \(v=2,3, \ldots , k+1\), \(T^{(d)}_v=\prod \nolimits _{k=1}^{v-1} \rho ^{d_k}_k\) \(=\rho ^{d_1}_1\rho ^{d_2}_2 \ldots \rho ^{d_{v-1}}_{v-1} \rightarrow \rho ^{0}_1 \rho ^{0}_2 \ldots \rho ^{0}_{v-1}=1\) and when \(v=k+2, k+3, \ldots , n\), \(T^{(d)}_v=\prod \nolimits _{k=1}^{v-1} \rho ^{d_k}_k=\rho ^{d_1}_1\rho ^{d_2}_2 \ldots \rho ^{d_k}_k \rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1} \rightarrow \rho ^{0}_1 \rho ^{0}_2 \ldots \rho ^{0}_{k} \rho ^{+\infty }_{k+1}\rho ^{d_{k+2}}_{k+2} \ldots \rho ^{d_{v-1}}_{v-1}=0\). It gives that \(T^{(d)}=\sum \limits _{v=1}^n T_v^{(d)} \rightarrow k+1\).

Since, \(\big (\tau (1),\tau (2), \ldots , \tau (k+1)\big )\) is an arrangement of \((1,2, \ldots , k+1)\) such that \(S({\mathcal {G}}_{\tau (v-1)}) \ge S({\mathcal {G}}_{\tau (v)})\) for each \(v=2,3, \ldots , k+1\). Also, we have assumed that \(\big (\sigma (1), \sigma (2), \ldots , \sigma (n) \big )\) is an arrangement of \((1,2, \ldots , n)\) such that \(S({\mathcal {G}}_{\sigma (v-1)}) \ge S({\mathcal {G}}_{\sigma (v)})\) for each \(v=2,3, \ldots , n\). As, \(\big \{{\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_{k+1}\big \} \subseteq \big \{{\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big \}\) therefore, there must exist natural numbers \(1 \le m_1, m_2, \ldots , m_{k+1} \le n\) satisfying \({\mathcal {G}}_{\tau (1)}={\mathcal {G}}_{\sigma (m_1)}\), \({\mathcal {G}}_{\tau (2)}={\mathcal {G}}_{\sigma (m_2)}\), \(\ldots \), \({\mathcal {G}}_{\tau (k+1)}={\mathcal {G}}_{\sigma (m_{k+1})}\). Using the Eq. (18), we have

$$\begin{aligned}&\xi ^{(d)}_{m_1} \rightarrow g \left( \frac{1}{k+1}\right) -g \left( \frac{0}{k+1}\right) = \psi _1 \\&\xi ^{(d)}_{m_2} \rightarrow g \left( \frac{2}{k+1}\right) -g \left( \frac{1}{k+1}\right) = \psi _2 \\&\ldots \\&\xi ^{(d)}_{m_{k+1}} \rightarrow g \left( \frac{k+1}{k+1}\right) -g \left( \frac{k}{k+1}\right) = \psi _{k+1} \end{aligned}$$

and all other weights tend to 0. Hence,

$$\begin{aligned}&\lim _{(d_1,d_2, \ldots , d_k, d_{k+1})\rightarrow (0,0, \ldots , 0, +\infty )} \text {CIFPOWA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big ) \\&\quad = \lim _{(d_1,d_2, \ldots , d_k, d_{k+1})\rightarrow (0,0, \ldots , 0, +\infty )} \bigoplus \limits _{v=1}^n \left( \xi ^{(d)}_v {\mathcal {G}}_{\sigma (v)}\right) \\&\quad \rightarrow \psi _1 {\mathcal {G}}_{\sigma (m_1)} \oplus \psi _2 {\mathcal {G}}_{\sigma (m_2)} \oplus \cdots \psi _{k+1} {\mathcal {G}}_{\sigma (m_{k+1})} \\&\quad = \psi _1 {\mathcal {G}}_{\tau (1)} \oplus \psi _2 {\mathcal {G}}_{\tau (2)} \oplus \cdots \psi _{k+1} {\mathcal {G}}_{\tau (k+1)} \\&\quad = \text {CIFOWA}_{g}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_{k+1}\big ) \end{aligned}$$

\(\square \)

Proof of the Proposition 11

Since, \((d_1,d_2, \ldots , d_k, d_{k+1})\rightarrow (1,1, \ldots , 1, +\infty )\). Let \(\rho _1 \rho _2 \ldots \rho _{v-1}={\mathbb {T}}_v\) for \(v=2,3, \ldots , k+1\); \({\mathbb {T}}_1=1\) and \({\mathbb {T}}=\sum \nolimits _{v=1}^{k+1} {\mathbb {T}}_v\). Therefore, for each \(v=2,3, \ldots , k+1\), \(T^{(d)}_v=\prod \nolimits _{k=1}^{v-1} \rho ^{d_k}_k=\rho ^{d_1}_1\rho ^{d_2}_2 \ldots \rho ^{d_{v-1}}_{v-1} \rightarrow \rho ^{1}_1 \rho ^{1}_2 \ldots \rho ^{1}_{v-1}={\mathbb {T}}_v\). Further, for \(v=k+2, k+3, \ldots , n\), \(T^{(d)}_v=\prod \nolimits _{k=1}^{v-1} \rho ^{d_k}_k=\rho ^{d_1}_1\rho ^{d_2}_2 \ldots \rho ^{d_k}_k \rho ^{d_{k+1}}_{k+1} \ldots \rho ^{d_{v-1}}_{v-1} \rightarrow \rho ^{1}_1 \rho ^{1}_2 \ldots \rho ^{1}_{k} \rho ^{+\infty }_{k+1}\rho ^{d_{k+2}}_{k+2} \ldots \rho ^{d_{v-1}}_{v-1}=0\). It implies that \(T^{(d)}=\sum \nolimits _{v=1}^n T^{(d)}_v=T^{(d)}_1+T^{(d)}_2+ \cdots + T^{(d)}_{k+1}={\mathbb {T}}_1+{\mathbb {T}}_2+{\mathbb {T}}_3+ \cdots +{\mathbb {T}}_{k+1}=\sum \nolimits _{v=1}^{k+1} {\mathbb {T}}_v={\mathbb {T}}\).

Now, assume that \(\big (\tau (1),\tau (2), \ldots , \tau (k+1)\big )\) is an arrangement of \((1,2, \ldots , k+1)\) such that \(S({\mathcal {G}}_{\tau (v-1)}) \ge S({\mathcal {G}}_{\tau (v)})\) for each \(v=2,3, \ldots , k+1\). Further, let \(\mathbb {H}_0=\phi \) and \(\mathbb {H}_v=\big \{{\mathcal {G}}_{\tau (z)}|z=1,2, \ldots , v\big \}\) for \(v=1,2, \ldots , k+1\). Also, we have assumed that \(\big (\sigma (1), \sigma (2), \ldots , \sigma (n) \big )\) is an arrangement of \((1,2, \ldots , n)\) such that \(S({\mathcal {G}}_{\sigma (v-1)}) \ge S({\mathcal {G}}_{\sigma (v)})\) for each \(v=2,3, \ldots , n\). As, \(\big \{{\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_{k+1}\big \} \subseteq \big \{{\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big \}\) therefore, there must exist natural numbers \(1 \le m_1, m_2, \ldots , m_{k+1} \le n\) satisfying \({\mathcal {G}}_{\tau (1)}={\mathcal {G}}_{\sigma (m_1)}\), \({\mathcal {G}}_{\tau (2)}={\mathcal {G}}_{\sigma (m_2)}\), \(\ldots \), \({\mathcal {G}}_{\tau (k+1)}={\mathcal {G}}_{\sigma (m_{k+1})}\). Further, we have

$$\begin{aligned}&\sum \limits _{t \in \big \{t| {\mathcal {G}}_t \in \mathcal {H}_m\big \}} T^{(d)}_t \rightarrow \sum \limits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_0\big \}} {\mathbb {T}}_t=0 ~~ \text {for} ~~ m\\&\quad \in \{0,1,2, \ldots m_1-1\}; \\&\sum \limits _{t \in \big \{t| {\mathcal {G}}_t \in \mathcal {H}_m\big \}} T^{(d)}_t \rightarrow \sum \limits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_1\big \}} {\mathbb {T}}_t ~~~ \text {for} ~~ m \\&\quad \in \{m_1,m_1+1,m_1+2, \ldots m_2-1\}; \\&\sum \limits _{t \in \big \{t| {\mathcal {G}}_t \in \mathcal {H}_m\big \}} T^{(d)}_t \rightarrow \sum \limits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_2\big \}} {\mathbb {T}}_t ~~~ \text {for} ~~ m \\&\quad \in \{m_2,m_2+1,m_2+2, \ldots m_3-1\}; \\&\ldots \\&\sum \limits _{t \in \big \{t| {\mathcal {G}}_t \in \mathcal {H}_m\big \}} T^{(d)}_t \rightarrow \sum \limits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_{k+1}\big \}} {\mathbb {T}}_t ~~~ \text {for} ~~ m \\&\quad \in \{m_{k+1},m_{k+1}+1,m_{k+1}+2, \ldots n\}. \\ \end{aligned}$$

Now, using the Eq. (18), we have

$$\begin{aligned}&\xi ^{(d)}_{m_1} \rightarrow g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_1\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) -g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_0\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) \\&\xi ^{(d)}_{m_2} \rightarrow g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_2\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) -g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_1\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) \\&\ldots \\&\xi ^{(d)}_{m_{k+1}} \rightarrow g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_{k+1}\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) -g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_k\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) \end{aligned}$$

and all other weights tend to 0. Finally, using the Definition 13, we obtain that

$$\begin{aligned}&\lim _{(d_1,d_2, \ldots , d_k, d_{k+1})\rightarrow (1,1, \ldots , 1, +\infty )}\text {CIFPOWA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big ) \\&\quad =\lim _{(d_1,d_2, \ldots , d_k, d_{k+1})\rightarrow (1,1, \ldots , 1, +\infty )}\left( \xi _1^{(d)} {\mathcal {G}}_{\sigma (1)} \oplus \xi _2^{(d)} {\mathcal {G}}_{\sigma (2)} \right. \\&\qquad \left. \oplus \cdots , \oplus \xi _n^{(d)} {\mathcal {G}}_{\sigma (n)}\right) \\&\quad = \xi ^{(d)}_{m_1} {\mathcal {G}}_{\sigma (m_1)} \oplus \xi ^{(d)}_{m_2} {\mathcal {G}}_{\sigma (m_2)} \oplus \cdots \oplus \xi ^{(d)}_{m_{k+1}} {\mathcal {G}}_{\sigma (m_{k+1})} \\&\quad \rightarrow \left( g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_1\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) -g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_0\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) \right) {\mathcal {G}}_{\tau (1)} \\&\quad \oplus \left( g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_2\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) -g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_1\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) \right) {\mathcal {G}}_{\tau (2)} \\&\qquad \oplus \cdots \oplus \left( g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_{k+1}\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) \right. \\&\qquad -\left. g \left( \frac{\sum \nolimits _{t \in \big \{t| {\mathcal {G}}_t \in \mathbb {H}_k\big \}} {\mathbb {T}}_t}{{\mathbb {T}}}\right) \right) {\mathcal {G}}_{\tau (k+1)} \\&\quad = \text {CIFPOWA}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_{k+1}\big ) \end{aligned}$$

Hence, \(\lim _{(d_1,d_2, \ldots , d_k, d_{k+1})\rightarrow (1,1, \ldots , 1, +\infty )} \text {CIFPOWA}_{d}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_n\big ) =\text {CIFPOWA}\big ({\mathcal {G}}_1,{\mathcal {G}}_2, \ldots , {\mathcal {G}}_{k+1}\big )\). \(\square \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garg, H., Rani, D. New prioritized aggregation operators with priority degrees among priority orders for complex intuitionistic fuzzy information. J Ambient Intell Human Comput 14, 1373–1399 (2023). https://doi.org/10.1007/s12652-021-03164-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03164-2

Keywords

Navigation