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03-04-2023 | ORIGINAL PAPER

Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure

Authors: Reham A. Alahmadi, Abdul Haseeb Ganie, Yousef Al-Qudah, Mohammed M. Khalaf, Abdul Hamid Ganie

Published in: Granular Computing | Issue 6/2023

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Abstract

To deal with situations involving uncertainty, Fermatean fuzzy sets are more effective than Pythagorean fuzzy sets, intuitionistic fuzzy sets, and fuzzy sets. Applications for fuzzy similarity measures can be found in a wide range of fields, including clustering analysis, classification issues, medical diagnosis, etc. The computation of the weights of the criteria in a multi-criteria decision-making problem heavily relies on fuzzy entropy measurements. In this paper, we employ t-conorms to suggest various Fermatean fuzzy similarity measures. We have also discussed all of their interesting characteristics. Using the suggested similarity measurements, we have created some new entropy measures for Fermatean fuzzy sets. By using numerical comparison and linguistic hedging, we have established the superiority of the suggested similarity metrics and entropy measures over the existing measures in the Fermatean fuzzy environment. The usefulness of the proposed Fermatean fuzzy similarity measurements is shown by pattern analysis. Last but not least, a novel multi-attribute decision-making approach is described that tackles a significant flaw in the order preference by similarity to the ideal solution, a conventional approach to decision-making, in a Fermatean fuzzy environment.

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Title: Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure
Authors: Reham A. Alahmadi
Abdul Haseeb Ganie
Yousef Al-Qudah
Mohammed M. Khalaf
Abdul Hamid Ganie
Publication date: 03-04-2023
Publisher: Springer International Publishing
Published in: Granular Computing / Issue 6/2023
Print ISSN: 2364-4966
Electronic ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-023-00378-x

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