Abstract
Pythagorean fuzzy sets outperform fuzzy sets and intuitionistic fuzzy sets for solving situations involving uncertainty. Numerous uses for fuzzy similarity measures exist, including clustering analysis, classifying problems, and even medical diagnosis. In a multi-criteria decision-making situation, fuzzy entropy measures are crucial for computing the criteria weights. We use t-conorms to propose various similarity measures for Pythagorean fuzzy sets in this study. We have also spoken about their different desired characteristics. We have developed some new entropy measures for Pythagorean fuzzy sets using recommended similarity measures. Using numerical comparison and linguistic hedging, we have proven the value of the suggested similarity and entropy metrics over the current measures in the Pythagorean fuzzy sets. The usefulness of the proposed Pythagorean fuzzy similarity metrics is shown by pattern analysis. Finally, in a Pythagorean fuzzy environment, a novel multi-attribute decision-making method is developed that solves a significant limitation of the famous decision-making methodology, namely, the technique for order preference by similarity to the ideal solution.