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Correction to “Pythagorean fuzzy interaction aggregation operators and their application to multiple attribute decision making”

Abstract

In this erratum, we point out that two theorems and six equations in a previous paper by G. Wei (2017) are incorrect by the Pythagorean fuzzy operations in detail, and present the modified theorems and equations. The original paper appeared as Wei, G. (2017). Pythagorean fuzzy interaction aggregation operators and their application to multiple attribute decision making. Journal of Intelligent & Fuzzy Systems, 33(4), 2119–2132. https://dx.doi.org/10.3233/jifs-162030.

1Introduction

More recently, Pythagorean fuzzy set (PFS) [1, 2] has emerged as an effective tool for depicting uncertainty of the MADM problems. The PFS is also characterized by the membership degree and the non-membership degree, whose sum of squares is less than or equal to 1, the PFS is more general than the IFS. In some cases, the PFS can solve the problems that the IFS cannot, for example, if a DM gives the membership degree and the non-membership degree as 0.8 and 0.6, respectively, then it is only valid for the PFS. In other words, all the intuitionistic fuzzy degrees are a part of the Pythagorean fuzzy degrees, which indicates that the PFS is more powerful to handle the uncertain problems. Zhang and Xu [3] provided the detailed mathematical expression for PFS and introduced the concept of Pythagorean fuzzy number(PFN). Meanwhile, they also developed a Pythagorean fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for handling the MCDM problem within PFNs. Wei [4] utilized arithmetic and geometric operations to develop some Pythagorean fuzzy interaction aggregation operators: Pythagorean fuzzy interaction weighted average (PFIWA) operator, Pythagorean fuzzy interaction weighted geometric (PFIWG) operator, Pythagorean fuzzy interaction ordered weighted average (PFIOWA) operator, Pythagorean fuzzy interaction ordered weighted geometric (PFIOWG) operator, Pythagorean fuzzy interaction hybrid average (PFIHA) operator and Pythagorean fuzzy interaction hybrid geometric (PFIHG) operator and studied the prominent characteristics of these proposed operators.

The aim of this paper is to point out some errors to the Theorems 1, Theorem 10 and some equations in Wei [4] and we propose the revised theorems, equations and their proof.

2Preliminaries

The basic concepts of PFNs [1, 2] are briefly reviewed in this section.

Definition 1. [1, 2] Let X be a fix set. A PFS is an object having the form

(1)
P={x,(μP(x),νP(x))|xX}
where the function μP : X → [0, 1] defines the degree of membership and the function νP : X → [0, 1] defines the degree of non-membership of the element x ∈ X to P, respectively, and, for every x ∈ X, it holds that
(2)
(μp(x))2+(νp(x))21.

For convenience, Zhang and Xu [3] called a˜=(μ,ν) a Pythagorean fuzzy number (PFN).

Definition 2. [4] Let a˜=(μ,ν) be a Pythagorean fuzzy number, a score function S of a Pythagorean fuzzy number can be represented as follows:

(3)
S(a˜)=12(1+μ2-ν2),S(a˜)[0,1].

Definition 3. [5] Let a˜=(μ,ν) be a Pythagorean fuzzy number, an accuracy function H of a Pythagorean fuzzy number can be represented as follows:

(4)
H(a˜)=μ2+ν2,H(a˜)[0,1].
to evaluate the degree of accuracy of the Pythagorean fuzzy number a˜=(μ,ν) , where H(a˜)[0,1] . The larger the value of H(a˜) , the more the degree of accuracy of the Pythagorean fuzzy number a˜ .

Based on the score function S and the accuracy function H, Wei [4] gave an order relation between two Pythagorean fuzzy numbers, which is defined as follows:

Definition 4. [4] Let a˜1=(μ1,ν1) and a˜2=(μ2,ν2) be two Pythagorean fuzzy number, s(a˜1) and s(a˜2) be the scores of a˜ and b˜ , respectively, and let H(a˜1) and H(a˜2) be the accuracy degrees of a˜ and b˜ , respectively, then if S(a˜)<S(b˜) , then a˜ is smaller than b˜ , denoted by a˜<b˜ ; if S(a˜)=S(b˜) , then

  • (1) if H(a˜)=H(b˜) , then a˜ and b˜ represent the same information, denoted by a˜=b˜ ; (2) if H(a˜)<H(b˜) , a˜ is smaller than b˜ , denoted by a˜<b˜ .

Definition 5. [3] Let a˜1=(μ1,ν1) , a˜2=(μ2,ν2) , and a˜=(μ,ν) be three Pythagorean fuzzy numbers, and the basic operations on them are defined as follows:

(1)a˜1a˜2=((μ1)2+(μ2)2-(μ1)2(μ2)2,ν1ν2);(2)a˜1a˜2=(μ1μ2,(ν1)2+(ν2)2-(ν1)2(ν2)2);(3)λa˜=(1-(1-μ2)λ,νλ),λ>0;(4)(a˜)λ=(μλ,1-(1-ν2)λ),λ>0;(5)a˜c=(ν,μ).

3Pythagorean fuzzy interaction aggregation operators

In this section, we shall give the corrected Theorem 1 and its proof and the corrected Equations (6 and 7).

Theorem 1. Let a˜j=(μj,νj)(j=1,2,,n) be a collection of Pythagorean fuzzy numbers, then their aggregated value by using the PFIWA operator is also a PFN, and

(5)
PFIWAω(a˜1,a˜2,,a˜n)=j=1n(ωja˜j)=(1-j=1n(1-(μj)2)ωj,j=1n(1-(μj)2)ωj-j=1n(1-((μj)2+(νj)2))ωj)
where ω = (ω1,  ω2, ⋯ ,  ωnT be the weight vector of a˜j(j=1,2,,n) , and ωj > 0, j=1nωj=1 .

Proof. We prove Equation (5) by mathematical induction on n.

  • When n = 2, we have

    PFIWAω(α˜1,α˜2)=ω1α˜1ω2α˜2

By Theorem 1, we can see that both ω1α1 and ω2α2 are PFNs, and the value of ω1α1 ⊕ ω2α2 is also a PFN. From the operational laws of Pythagorean fuzzy number, we have

ω1α˜1=(1-(1-(μ1)2)ω1,(1-(μ1)2)ω1-(1-((μ1)2+(ν1)2))ω1);ω2α˜2=(1-(1-(μ2)2)ω2,(1-(μ2)2)ω2-(1-((μ2)2+(ν2)2))ω2)

Then

PFIWAω(α˜1,α˜2)=ω1α˜1ω2α˜2=(1-(1-(μ1)2)ω1,(1-(μ1)2)ω1-(1-((μ1)2+(ν1)2))ω1)(1-(1-(μ2)2)ω2,(1-(μ2)2)ω2-(1-((μ1)2+(ν1)2))ω2)=(1-(1-(μ1)2)ω1(1-(μ2)2)ω2,(1-(μ1)2)ω1(1-(μ2)2)ω2-[(1-((μ1)2+(ν1)2))]ω1[(1-((μ2)2+(ν2)2))]ω2)=(1-j=12(1-(μj)2)ωj,j=12(1-(μj)2)ωj-j=12(1-((μj)2+(νj)2))ωj)
  • Suppose that n = k, Equation (5) holds, i.e.,

    PFIWAω(α˜1,α˜2,,α˜k)=ω1α˜1ω2α˜2ωkα˜k=(1-j=1k(1-(μj)2)ωj,j=1k(1-(μj)2)ωj-j=1k(1-((μj)2+(νj)2))ωj)

And the aggregated value is a PFN, Then when n = k + 1, by the operational laws of Pythagorean fuzzy number, we have

PFIWAω(α˜1,α˜2,,α˜k+1)=ω1α˜1ω2α˜2ωkα˜kωk+1α˜k+1=(1-j=1k(1-(μj)2)ωj,j=1k(1-(μj)2)ωj-j=1k(1-((μj)2+(νj)2))ωj)(1-(1-(μk+1)2)ωk+1,(1-(μk+1)2)ωk+1-(1-((μk+1)2+(νk+1)2))ωk+1)=(1-j=1k+1(1-(μj)2)ωj,j=1k+1(1-(μj)2)ωj-j=1k+1(1-((μj)2+(νj)2))ωj)
by which aggregated value is also a PFN, Therefore, when n = k + 1, Equation (5) holds.

Thus, by ① and ②, we know that Equation (5) holds for all n. The proof is completed.

Furthermore, Equations (6 and 7) can be corrected as follows:

(6)
PFIOWAw(a˜1,a˜2,,a˜n)=j=1n(wja˜σ(j))=(1-j=1n(1-(μσ(j))2)wj,j=1n(1-(μσ(j))2)wj-j=1n(1-(μσ(j)+νσ(j))2)wj)
(7)
PFIHAw,ω(α˜1,α˜2,,α˜n)=j=1n(wja˜̇σ(j))=(1-j=1n(1-(μ̇σ(j))2)wj,j=1n(1-(μ̇σ(j))2)wj-j=1n(1-((μ̇σ(j))2+(ν̇σ(j))2))wj)

4Pythagorean fuzzy interaction geometric aggregation operators

In this section, we shall give the corrected Theorem 10 and its proof and the corrected Equations (9 and 10).

Theorem 10. The aggregated value by using PFIWG operator is also a PFN, where

(8)
PFIWGω(α˜1,α˜2,,α˜n)=(j=1k(1-(νj)2)ωj-j=1k(1-(μj+νj)2)ωj,1-j=1k(1-(νj)2)ωj)
where ω = (ω1,  ω2, ⋯ ,  ωnT be the weight vector of αj (j = 1,  2, ⋯ ,  n), and ωj > 0, j=1nωj=1 .

Proof. We prove Equation (8) by mathematical induction on n.

  • When n = 2, we have

    PFIWGω(α˜1,α˜2)=(α˜1)ω1(α˜2)ω2

By Theorem1, we can see that both (α˜1)ω1 and (α˜2)ω2 are PFNs, and the value of (α˜1)ω1(α˜2)ω2 is also a PFN. From the operational laws of Pythagorean fuzzy number, we have

(α˜1)ω1=((1-(ν1)2)ω1-(1-(μ1+ν1)2)ω1,1-(1-(ν1)2)ω1);

(α˜2)ω2=((1-(ν2)2)ω2-(1-(μ2+ν2)2)ω2,1-(1-(ν2)2)ω2)

Then

PFIWGω(α˜1,α˜2)=(α˜1)ω1(α˜2)ω2=((1-(ν1)2)ω1-(1-(μ1+ν1)2)ω1,1-(1-(ν1)2)ω1)((1-(ν2)2)ω2-(1-(μ2+ν2)2)ω2,1-(1-(ν2)2)ω2)=((1-(ν1)2)ω1(1-(ν2)2)ω2-[(1-(μ1+ν1)2)]ω1[(1-(μ2+ν2)2)]ω2,1-(1-(ν1)2)ω1(1-(ν2)2)ω2)=(j=12(1-(νj)2)ωj-j=12(1-(μj+νj)2)ωj,1-j=12(1-(νj)2)ωj)

  • Suppose that n = k, Equation) holds, i.e.,

    PFIWGω(α˜1,α˜2,,α˜k)=(α˜1)ω1(α˜2)ω2(α˜k)ωk=(j=1k(1-(νj)2)ωj-j=1k(1-(μj+νj)2)ωj,1-j=1k(1-(νj)2)ωj)

And the aggregated value is a PFN, Then when n = k + 1, by the operational laws of Pythagorean fuzzy number, we have

PFIWGω(α˜1,α˜2,,α˜k+1)=(α˜1)ω1(α˜2)ω2(α˜k)ωk(α˜k+1)ωk+1=(j=1k(1-(νj)2)ωj-j=1k(1-(μj+νj)2)ωj,1-j=1k(1-(νj)2)ωj)((1-(νk+1)2)ωk+1-(1-(μk+1+νk+1)2)ωk+1,1-(1-(νk+1)2)ωk+1)=(j=1k+1(1-(νj)2)ωj-j=1k+1(1-(μj+νj)2)ωj,1-j=1k+1(1-(νj)2)ωj)
by which aggregated value is also a PFN, Therefore, when n = k + 1, Equation (8) holds.

Thus, by ① and ②, we know that Equation (8) holds for all n. The proof is completed.

Furthermore, Equations (9 and 10) can be corrected as follows:

(9)
PFIOWGω(a˜1,a˜2,,a˜n)=j=1n(α˜σ(j))wj=(j=1n(1-(νσ(j))2)wj-j=1n(1-((μσ(j))2+(νσ(j))2))wj,1-j=1n(1-(νσ(j))2)wj)

(10)
PFIHGw,ω(α˜1,α˜2,,α˜n)=j=1n(a˜̇σ(j))wj=(j=1n(1-(ν̇σ(j))2)wj-j=1n(1-((μ̇σ(j))2+(ν̇σ(j))2))wj,1-j=1n(1-(ν̇σ(j))2)wj)

5An approach to multiple attribute decision making with Pythagorean fuzzy information

In this section, we shall give the corrected Equations (11 and 12).

(11)
r˜i=(μi,νi)=PFIWAω(r˜i1,r˜i2,,r˜in)=j=1n(ωjr˜ij)=(1-j=1n(1-(μij)2)ωj,j=1n(1-(μij)2)ωj-j=1n(1-((μij)2+(νij)2))ωj),i=1,2,,m.
(12)
r˜i=(μi,νi)=PFIWGω(r˜i1,r˜i2,,r˜in)=j=1n(r˜ij)ωj=(j=1n(1-(νij)2)ωj-j=1n(1-((μij)2+(νij)2))ωj,1-j=1n(1-(νij)2)ωj),i=1,2,,m.

6Illustrative example and comparative analysis

In this section, with corrected Equations (11 and 12), the Tables 1– 3 are corrected as follows:

Table 1

The aggregating results of the ERP systems by the PFIWA (PFIWG) operators

PFIWAPFIWG
A1(0.727,0.324)(0.660,0.446)
A2(0.458,0.583)(0.355,0.651)
A3(0.407,0.538)(0.341,0.579)
A4(0.663,0.511)(0.495,0.674)
A5(0.667,0.474)(0.520,0.632)

Step 1. According to Table 1, aggregate all Pythagorean fuzzy numbers r˜ij(j=1,2,,n) by using the PFIWA (PFIWG) operator to derive the overall Pythagorean fuzzy numbers αi (i = 1, 2, 3, 4, 5) of the alternative Ai. The aggregating results are shown in Table 1.

Step 2. According to the aggregating results shown in Table 1 and the score functions of the ERP systems are shown in Table 2.

Table 2

The score functions of the ERP systems

PFIWAPFIWG
A10.7120.619
A20.4350.351
A30.4380.391
A40.5890.396
A50.6100.435

Step 3. According to the score functions shown in Table 2 and the comparison formula of score functions, the ordering of the ERP systems are shown in Table 3. Note that “>” means “preferred to”. As we can see, depending on the aggregation operators used, the ordering of the ERP systems is some and the best ERP system is A1.

Table 3

Ordering of the ERP systems

Ordering
PFIWAA1>A5>A4>A3>A2
PFIWGA1>A5>A4>A3>A2

7Conclusion

In this study we utilize arithmetic and geometric operations to investigate some Pythagorean fuzzy interaction aggregation operators in detail, and point out that Theorems 1 and 10 in Wei [4] are incorrect by the Pythagorean fuzzy operational laws. Finally we propose the modifications of these theorems and equations. In the future, we shall continue working in the extension and application of the developed operators to other domains and fuzzy setting, such as picture fuzzy sets, dual hesitant fuzzy sets, and so on [6–22].

References

[1] 

Yager R.R. , Proceeding of The Joint IFSA Wprld Congress and NAFIPS Annual Meeting (2013), 57–61 Pythagorean fuzzy subsets, Edmonton, Canada.

[2] 

Yager R.R. , Pythagorean membership grades in multicriteria decision making, IEEE Transactions on Fuzzy Systems 22 (2014), 958–965.

[3] 

Zhang X.L. and Xu Z.S. , Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets, International Journal of Intelligent Systems 29 (2014), 1061–1078.

[4] 

Wei G.W. , Pythagorean fuzzy interaction aggregation operators and their application to multiple attribute decision making, Journal of Intelligent and Fuzzy Systems 33(4) (2017), 2119–2132.

[5] 

Yager R.R. and Abbasov A.M. , Pythagorean membeship grades, complex numbers and decision making, International Journal of Intelligent Systems 28 (2013), 436–452.

[6] 

Wei G.W. and Lu M. , Pythagorean fuzzy maclaurin symmetric mean operators in multiple attribute decision making, International Journal of Intelligent Systems (2017). doi: 10.1002/int.21911

[7] 

Wei G.W. and Lu M. , Pythagorean fuzzy power aggregation operators in multiple attribute decision making, International Journal of Intelligent Systems 33(1) (2018), 169–186.

[8] 

Wu S.J. and Wei G.W. , Pythagorean fuzzy Hamacher aggregation operators and their application to multiple attribute decision making,} and Intelligent Engineering Systems}, International Journal of {{Knowledge-based 21(3) (2017), 189–201.

[9] 

Lu M. and Wei G.W. , Pythagorean uncertain linguistic aggregation operators for multiple attribute decision making, International Journal of Knowledge-based and Intelligent Engineering Systems 21(3) (2017), 165–179.

[10] 

Wei G.W. and Lu M. , Dual hesitant Pythagorean fuzzy Hamacher aggregation operators in multiple attribute decision making, Archives of Control Sciences 27(3) (2017), 365–395.

[11] 

Wei G.W. , Picture fuzzy Hamacher aggregation operators and their application to multiple attribute decision making, Fundamenta Informaticae 157(3) (2018), 271–320. doi:10.3233/FI-2018-1628

[12] 

Wei G.W. , Picture uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making, Kybernetes 46(10) (2017), 1777–1800.

[13] 

Wei G.W. , Interval-valued dual hesitant fuzzy uncertain linguistic aggregation operators in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems 33(3) (2017), 1881–1893.

[14] 

Wei G.W. , Picture 2-tuple linguistic Bonferroni mean operators and their application to multiple attribute decision making, International Journal of Fuzzy System 19(4) (2017), 997–1010.

[15] 

Wei G.W. , Lu M. , Alsaadi F.E. , Hayat T. and Alsaedi A. , Pythagorean 2-tuple linguistic aggregation operators in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems 33(2) (2017), 1129–1142.

[16] 

Wei G.W. , Alsaadi F.E. , Hayat T. and Alsaedi A. , Hesitant bipolar fuzzy aggregation operators in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems 33(2) (2017), 1119–1128.

[17] 

Wei G.W. , Interval valued hesitant fuzzy uncertain linguistic aggregation operators in multiple attribute decision making, International Journal of Machine Learning and Cybernetics 7(6) (2016), 1093–1114.

[18] 

Wei G.W. , Some cosine similarity measures for picture fuzzy sets and their applications to strategic decision making, Informatica 28(3) (2017), 547–564.

[19] 

Gao H. , Wei G.W. and Huang Y.H. , Dual hesitant bipolar fuzzy Hamacher prioritized aggregation operators in multiple attribute decision making, IEEE Access (2017)–doi:10.1109/ACCESS.2017.2784963

[20] 

Wei G.W. and Lu M. , Pythagorean hesitant fuzzy Hamacher aggregation operators in multiple attribute decision making, Journal of Intelligent Systems (2017)–doi:https://doi.org/10.1515/jisys-2017-0106--> .

[21] 

Wei G.W. , Alsaadi F.E. , Hayat T. and Alsaedi A. , Picture 2-tuple linguistic aggregation operators in multiple attribute decision making, Soft Computing (2016). doi:10.1007/s00500-016-2403-8

[22] 

Wei G.W. , Alsaadi F.E. , Hayat T. and Alsaedi A. , Projection models for multiple attribute decision making with picture fuzzy information, International Journal of Machine Learning and Cybernetics (2016). doi:10.1007/s13042-016-0604-1