Complex Pythagorean fuzzy einstein aggregation operators in selecting the best breed of Horsegram

Janani, K.; Veerakumari, K. Pradeepa; Vasanth, Krishnan; Rakkiyappan, R.

doi:10.1016/j.eswa.2021.115990

Cited by 32 publications

(18 citation statements)

References 63 publications

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“…To study the efficacy of the schemed aggregation operators, this section compares several preexisting aggregation operators such as complex q-rung orthopair fuzzy weighted averaging (Cq-ROFWA) [56], complex qrung orthopair fuzzy weighted geometric (Cq-ROFWG) [56], complex pythagorean fuzzy einstein ordered weighted averaging (CPFEOWA) [57], complex pythagorean fuzzy einstein ordered weighted geometric (CPFEOWG) [57], complex pythagorean fuzzy einstein hybrid averaging (CPFEHA) [57], complex pythagorean fuzzy einstein hybrid geometric (CPFEHG) [57], intuitionistic fuzzy Aczel-alsina hybrid averaging (IFAAHA) [44], intuitionistic fuzzy Aczel-alsina hybrid geometric (IFAAHG) [44], q-rung orthopair fuzzy weighted averaging (q-ROFWA) [54], q-rung orthopair fuzzy weighted geometric (q-ROFWG) [54], q-rung orthopair fuzzy frank ordered weighted averaging (q-ROFFOWA) [39], q-rung orthopair fuzzy frank ordered weighted geometric (q-ROFFOWG) [39], q-rung orthopair fuzzy frank hybrid averaging (q-ROFFHA) [39], q-rung orthopair fuzzy frank hybrid geometric (q-ROFFHG) [39], and the proposed aggregation operators Cq-ROFAAHA and Cq-ROFAAHG in the same working environment. We discovered some interesting findings, which are detailed as follows: • i).…”

Section: Comparative Analysismentioning

confidence: 99%

“…iii). The operators given by Janani et al [57] are based on complex pythagorean data. For the considered problem these operators work and the results obtained from these operators under investigation are almost same, except CPFEHA and CPFEHG operators which is due to the structural difference of these operators.…”

Section: Comparative Analysismentioning

confidence: 99%

“…4). Criteria weights: The existing approaches [39], [44], [54], [56], [57] are incapable of dealing with MCDM situations with unknown weight information. Whilst the presented approach is effective in such situations due to the proposed entropy measure.…”

Section: Figure 5: Comparative Analysis With Existing Aggregation Ope...mentioning

confidence: 99%

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Complex q-Rung Orthopair Fuzzy Aczel–Alsina Aggregation Operators and Its Application to Multiple Criteria Decision-Making With Unknown Weight Information

Ali

Naeem

2022

IEEE Access

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In decision making problems, complex q-rung orthopair fuzzy set is regarded as a more practical tool than complex intuitionistic fuzzy set and q-rung orthopair fuzzy set. This paper proposes several aggregation operators based on the Aczel-Alsina t-norm and t-conorm for aggregating complex q-rung orthopair fuzzy data. The suggested operators are then used to establish a multiple criteria decisionmaking (MCDM) method. The Aczel-Alsina operations t-norm and t-conorm can have the advantage of good flexibility with the operational parameter. In this regard, we expand the notions of the Aczel-Alsina t-norm and t-conorm to the complex q-rung orthopair fuzzy environment and provide certain aggregation operators in this study. Furthermore, we show the compatible features of the suggested operators. To overcome the defects of the existing entropy measures, a novel complex q-rung orthopair fuzzy entropy approach is put forward for acquiring the unknown criteria weights objectively. Following that, we describe an MCDM technique with unknown criterion weights in a complex q-rung orthopair fuzzy environment grounded on the originated operators. Then, to demonstrate the model's flexibility and validity, we analyze and solve a problem concerning with the selection of the sector that had the most impact on the Pakistan Stock Exchange. Subsequently, we demonstrate how the parameter's inclusion in our proposed model influences decision-making outcomes. At last, the generated outcomes are compared to the past approaches to demonstrate our suggested technique's superiority.INDEX TERMS Aczel-Alsina t-norms. Complex q-rung orthopair fuzzy set. Complex q-rung orthopair fuzzy Aczel-Alsina aggregation operators. Entropy measure. MCDM.

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Section: Comparative Analysismentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

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Complex q-Rung Orthopair Fuzzy Aczel–Alsina Aggregation Operators and Its Application to Multiple Criteria Decision-Making With Unknown Weight Information

Ali

Naeem

2022

IEEE Access

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“…However, in several procedures of decision making, it is possible and , but and . Alternatively, the main mathematical form of complex PF (CPF) set, pioneered by Ullah et al [ 31 ] with a modified rule: , , and due to these modifications, certain people have utilized it in the region of different fields, for instance, Einstein geometric operators [ 32 ], Dombi operators [ 33 ], Einstein operators [ 34 ], and prioritized weighted operators [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Archimedean Aggregation Operators Based on Complex Pythagorean Fuzzy Sets Using Confidence Levels and Their Application in Decision Making

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Ali

Mahmood

2022

Int. J. Fuzzy Syst.

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The diagnosed complex Pythagorean fuzzy (CPF) set is a more valuable and dominant tool than the Pythagorean and intuitionistic fuzzy sets to describe awkward and unreliable information more effectively. Further, Archimedean t-norm and t-conorm have a significant influence to depict the relation among aggregated values. To take advantage of the CPF set and Archimedean t-norm and t-conorm, and assume the relation among Archimedean norms and algebraic, Einstein, Hamacher, and frank norms at the same time, in this analysis, first, we proposed the fundamental Archimedean operational laws. Second, based on these laws, we developed confidence CPF Archimedean-weighted averaging (CCPFSAWA), confidence CPF Archimedean-ordered weighted averaging (CCPFSAOWA), confidence CPF Archimedean-weighted geometric (CCPFSAWG), confidence CPF Archimedean-ordered weighted geometric (CCPFSAOWG) operators and implemented their valuable results and properties. We know that Archimedean t-norm and t-conorm are the general form of the all-aggregation operators, so by using different values of t-norm and t-conorm, we explored the confidence CPF-weighted averaging (CCPFWA), confidence CPF-ordered weighted averaging (CCPFOWA), confidence CPF Einstein-weighted averaging (CCPFEWA), confidence CPF Einstein-ordered weighted averaging (CCPFEOWA), confidence CPF Hamacher-weighted averaging (CCPFHWA), confidence CPF Hamacher-ordered weighted averaging (CCPFHOWA), confidence CPF frank-weighted averaging (CCPFFWA), confidence CPF frank-ordered weighted averaging (CCPFFOWA), confidence CPF-weighted geometric (CCPFWG), confidence CPF-ordered weighted geometric (CCPFOWG), confidence CPF Einstein-weighted geometric (CCPFEWG), confidence CPF Einstein-ordered weighted geometric (CCPFEOWG), confidence CPF Hamacher-weighted geometric (CCPFHWG), confidence CPF Hamacher-ordered weighted geometric (CCPFHOWG), confidence CPF frank-weighted geometric (CCPFFWG), and confidence CPF frank-ordered weighted geometric (CCPFFOWG) operators. Then, we developed a multi-attribute decision-making (MADM) method based on the proposed operators. Finally, many examples are used to do comparative analysis among proposed and existing methods to show the validation of the new approaches.

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“…To enlarge the representing domain, Ullah et al [36] proposed the concept of complex Pythagorean fuzzy sets (CPyFSs), and Liu et al [20] introduced the concept of complex qrung orthopair fuzzy sets (Cq-ROFSs). Since then, CIFSs, CPyFSs, and Cq-ROFSs have been widely applied to various fields, such as MCDM/MADM [1][2][3][4][5][6]12,15,[20][21][22]24,25,32,38,40], medical diagnosis [13,[27][28][29]32], pattern recognition [12,13,27,36], cluster analysis [12,47], and image processing [16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Hamming and Hausdorff 3D distance measures for complex pythagorean fuzzy sets and their applications in pattern recognition and medical diagnosis

Zhu

Ullah

et al. 2022

Complex Intell. Syst.

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Similarity measures are very effective and meaningful tool used for evaluating the closeness between any two attributes which are very important and valuable to manage awkward and complex information in real-life problems. Therefore, for better handing of fuzzy information in real life, Ullah et al. (Complex Intell Syst 6(1): 15–27, 2020) recently introduced the concept of complex Pythagorean fuzzy set (CPyFS) and also described valuable and dominant measures, called various types of distance measures (DisMs) based on CPyFSs. The theory of CPyFS is the essential modification of Pythagorean fuzzy set to handle awkward and complicated in real-life problems. Keeping the advantages of the CPyFS, in this paper, we first construct an example to illustrate that a DisM proposed by Ullah et al. does not satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Then, combining the 3D Hamming distance with the Hausdorff distance, we propose a new DisM for CPyFSs, which is proved to satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Moreover, similarly to some DisMs for intuitionistic fuzzy sets, we present some other new complex Pythagorean fuzzy DisMs. Finally, we apply our proposed DisMs to a building material recognition problem and a medical diagnosis problem to illustrate the effectiveness of our DisMs. Finally, we aim to compare the proposed work with some existing measures is to enhance the worth of the derived measures.

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Complex Pythagorean fuzzy einstein aggregation operators in selecting the best breed of Horsegram

Cited by 32 publications

References 63 publications

Complex q-Rung Orthopair Fuzzy Aczel–Alsina Aggregation Operators and Its Application to Multiple Criteria Decision-Making With Unknown Weight Information

Complex q-Rung Orthopair Fuzzy Aczel–Alsina Aggregation Operators and Its Application to Multiple Criteria Decision-Making With Unknown Weight Information

Archimedean Aggregation Operators Based on Complex Pythagorean Fuzzy Sets Using Confidence Levels and Their Application in Decision Making

Analysis of Hamming and Hausdorff 3D distance measures for complex pythagorean fuzzy sets and their applications in pattern recognition and medical diagnosis

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