Parametric Similarity Measurement of T-Spherical Fuzzy Sets for Enhanced Decision-Making

Abstract

The T-spherical fuzzy set (T-SFS), an advancement over the spherical fuzzy set (SFS), offers a refined approach for addressing contradictions and ambiguities in data. In this context, similarity measures (SMs) serve as critical tools for quantifying the resemblance between fuzzy values, traditionally relying on the calculation of distances between these values. Nevertheless, existing methodologies often encounter irrational outcomes due to certain characteristics and complex operations involved. To surmount these challenges, a novel parametric similarity measure is proposed, grounded in three adjustable parameters. This enables decision-makers to tailor the SM to suit diverse decision-making styles, thereby circumventing the aforementioned irrationalities. An analytical comparison with existing SM reveals the superiority of the proposed measure through mathematical validation. Furthermore, the utility of this measure is demonstrated in the resolution of multi-attribute decision-making (MADM) problems, highlighting its efficacy over several existing approaches within the domain of T-SFS. The implementation of the proposed SM not only enhances the precision of similarity assessment in fuzzy sets but also significantly contributes to the optimization of decision-making processes.

Optimization of Market Risk via Maclaurin Symmetric Mean Aggregation Operators: An Application of Interval-Valued Intuitionistic Fuzzy Sets in Multi-Attribute Group Decision-Making

“…Kostadinova et al [35] developed the theory of market chains for stock price and risk portfolio optimization. Sarfraz and Azeem [36] developed the theory of similarity measure. Chen et al [37] proposed the concept of market risk optimization with the application of real industry in China.…”