Novel exponential divergence measure of complex intuitionistic fuzzy sets with an application to the decision-making process

As a generalization of Intuitionistic Fuzzy Sets (IFSs), Complex IFSs (CIFSs) are powerful and worthy tools for realizing the imprecise information using the complexvalued membership degrees with an extra term called phase term. Divergence measure is a valuable tool for determining the degree of discrimination between the two sets. Driven by these fundamental characteristics, it is fascinating to manifest some divergence measures under the CIFS theory. The present study proposed a method for solving the Multi-Criteria Decision-Making (MCDM) problem under CIFS environment. In this regard, rstly, the divergence measures were introduced between two CIFSs and their properties and relations were evaluated. Secondly, a novel algorithm was suggested based on the proposed measures to solve the problems in which the weights of the criteria were resolved by maximizing deviation method. Thirdly, a reasonable example was provided to verify the developed approach and exhibit its practicality and utility with a comparative analysis indicating its manageable and adaptable nature