Addressing Decision-Making Challenges: Similarity Measures for Interval-Valued Intuitionistic Fuzzy Hypersoft Sets

Abstract

A similarity measure tackles multiple difficulties, including managing ambiguous and indistinct information. Nevertheless, it encounters difficulties with general ambiguities and complexities in matters requiring heterogeneous data. This article examines essential ideas that establish a framework for the study, including soft sets, hypersoft sets, intuitionistic fuzzy hypersoft sets (IFHSS), and interval-valued IFHSS (IVIFHSS). The main aim of this research is to construct similarity metrics for IVIFHSS and examine their fundamental features. A decision-making methodology is suggested utilizing these similarity measures in conjunction with a strategy for addressing multi-tiered decision-making challenges. The merits of the algorithm, such as its efficiency, adaptability, and relative superiority to traditional methods, are outlined.