Optimizing algorithms and decision making problems through novel distance techniques with complex fermatean fuzzy numbers

The concept of CFFSs offers a more comprehensive on CFSs, CIFSs, and CPYFSs, with greater potential for practical applications due to their flexible structure, larger space, adjustable parameters, and influential design compared to other generalization of FSs. The striking framework of CFFSs is keen to provide a larger preference range for the modeling of uncertain information. This article sets out a decision-making analysis for the interaction between complex-valued membership and complex-valued non-membership degrees with the help of CFFNs. Some of the unique operational laws and their fundamental properties for CFFNs are studied. Distance measures have been extensively used in many areas as an essential approach that may successfully disclose the difference between CFSs and their generalizations. Some of the well-known distance measures for CFFNs has been studied in the literature, so on the basis of these distances, some novel distance measures for CFFNs have been analyzed. The applications of the proposed CFFDMs are demonstrated in scenarios including telecommunication and medical diagnostic problems. A comparative analysis of the obtained novel CFFDMs with similar existing distances is conducted through numerical examples. All the observations and results are presented graphically. The present novel CFFDMs are fantastically designated for the classification of the most favorable alternatives by examining the closeness of all available choices from a particular ideal solution.