A New Distance Measure in Intuitionistic Fuzzy Sets Environment and Its Diverse Applications

In this article, a novel tool of decision-making problems, namely distance measure between intuitionistic fuzzy sets (IFSs) is being forwarded. Classically, the previous distance measures did not consider the impact of hesitancy parameter, which produce counterintuitive outcomes when measuring distance. The proposed distance measure fully considers the influence of hesitation degree on distance measurement. Simultaneously, it not only consistent with the four properties between distance measure, but also has nonlinear characteristics. The incorporation of the logarithmic transformation introduces nonlinear characteristics in the distance computation, which allows the measure to capture subtle variations between intuitionistic fuzzy elements more effectively. This nonlinear behavior improves the discrimination capability of the distance measure when comparing intuitionistic fuzzy sets. Several examples were analyzed for demonstrating that the suggested distance measure produces preponderance outcomes. In addition, the applicability of the suggested distance measure was displayed by a series of applications in pattern recognition.