A novel distance measure for intuitionistic fuzzy sets with diverse applications

Abstract

Distance function is a canonical quantitative tool to measure the similarity or difference between two intuitionistic fuzzy sets (IFSs). In spite of having a vast range of such tools, one fails to distinguish accurately the IFSs with higher hesitancy. In view of that, we introduce a distance function for IFSs and validate the axiomatic definition of it. Boundedness and nonlinear characteristics of the proposed distance measure are corroborated. Moreover, its efficacy is not restricted to highly uncertain IFSs only, but it is equally effective in other cases, which is testified via some numerical examples. We establish the veracity of the prescribed distance by comparing it with reported results through some illustrative examples. In light of recent COVID-19 pandemic, selection of a proper antivirus face mask has become a daunting task, which is addressed via the introduced distance measure as a multi-attribute decision-making problem. We further extend the applicability of the proposed distance in the areas of pattern recognition and medical diagnosis.