Logarithmic similarity measures on Pythagorean fuzzy sets in admission process

Hari Darshan Arora () and Anjali Naithani ()

Operations Research and Decisions, 2022, vol. 32, issue 1, 5-24

Abstract: The intuitionistic fuzzy sets (IFSs) have a more significant contribution to describing and dealing with uncertainty. The intuitionistic fuzzy measure is a significant consideration in the field of IFSs theory. However, Pythagorean fuzzy sets (PFSs) are an extension of the IFSs. PFSs are more capable of modelling uncertainties than IFSs in real-world decision-making scenarios. The majority of PFSs research has concentrated on establishing decision-making frameworks. A similarity measure is a key concept which measures the closeness of PFSs. IFSs-based similarity measures have been proposed in the literature. This type of similarity measure, however, has a drawback since it cannot satisfy the axiomatic definition of similarity by offering counter-intuitive examples. For this study, a similarity based on logarithmic function for Pythagorean fuzzy sets (PFSs) is proposed as a solution to the problem. A decision-making approach is presented to ascertain the suitability of careers for aspirants. Additionally, numerical illustration is applied to determine the strength and validity of the proposed similarity measures. The application of the proposed similarity measures is also presented in this article. A comparison of the suggested measures with the existing ones is also demonstrated to ensure the reliability of the measures. The results show that the proposed similarity measures are efficient and reasonable from both numerical and realistic assessments.

Keywords: intuitionistic fuzzy sets; Pythagorean fuzzy sets; similarity measures; weighted similarity measures; logarithmic function; decision making (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:wut:journl:v:32:y:2022:i:1:p:5-24:id:2642

DOI: 10.37190/ord220101

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