Abstract

The purpose of this paper is to propose a new Pythagorean fuzzy entropy for Pythagorean fuzzy sets, which is a continuation of the Pythagorean fuzzy entropy of intuitionistic sets. The Pythagorean fuzzy set continues the intuitionistic fuzzy set with the additional advantage that it is well equipped to overcome its imperfections. Its entropy determines the quantity of information in the Pythagorean fuzzy set. Thus, the proposed entropy provides a new flexible tool that is particularly useful in complex multi-criteria problems where uncertain data and inaccurate information are considered. The performance of the introduced method is illustrated in a real-life case study, including a multi-criteria company selection problem. In this example, we provide a numerical illustration to distinguish the entropy measure proposed from some existing entropies used for Pythagorean fuzzy sets and intuitionistic fuzzy sets. Statistical illustrations show that the proposed entropy measures are reliable for demonstrating the degree of fuzziness of both Pythagorean fuzzy set (PFS) and intuitionistic fuzzy sets (IFS). In addition, a multi-criteria decision-making method complex proportional assessment (COPRAS) was also proposed with weights calculated based on the proposed new entropy measure. Finally, to validate the reliability of the results obtained using the proposed entropy, a comparative analysis was performed with a set of carefully selected reference methods containing other generally used entropy measurement methods. The illustrated numerical example proves that the calculation results of the proposed new method are similar to those of several other up-to-date methods.

Highlights

  • This paper introduces the concept of the fuzzy entropy of Pythagorean fuzzy sets, which extends the concept of the fuzzy entropy of the intuitionistic fuzzy sets

  • As indicated in the article, this extension is necessary. It exploited the concept of probability for determining the fuzzy entropy of Pythagorean fuzzy sets

  • We gave some numerical illustrations to analyze our proposed entropy measure compared to some current entropies of Pythagorean fuzzy sets

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Summary

Introduction

Entropy is a measure of uncertainty occurring in information in which a higher value implies more information in the process being analyzed. In multi-criteria decision analysis (MCDA) methods, entropy is used to determine the objective weights of the criteria based on the data in the decision matrix. The entropy of a Pythagorean fuzzy set is a novel approach that measures the uncertainty associated with a PFS that represents the properties of fuzzy sets well. The entropy designed for fuzzy sets measures fuzziness appearing among fuzzy sets [1]. Multi-criteria decision-making processes are often fraught with uncertainty due to constraints of knowledge regarding the choice of criteria for the decision model and its structure, inaccuracies present in the input data, and limited stakeholders’

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