Multipolar Interval-Valued Fuzzy Set with Application of Similarity Measures and multi-person TOPSIS technique

Abstract

A Similarity measure in the fuzzy structure plays a very considerable role in manipulating hurdles that apprehend vague data, but unable to deal with the ambiguous and variability of the problems having multipolar interval-valued data. In this research article, a certain distance between two multipolar interval-valued fuzzy sets (mIVF sets) has been defined. A new similarity measure (Sim.M) for mIVF based on distances has been introduced, also some of the basic operations on the structure has been defined such as union, intersection, and complement. MCDM is performed for mIVF information that measure the similarity measure based on distance measure for the best alternative. An application is given that the proposed Sim.M for mIVF set is capable of recognition the nature and structure of different entities which belongs to the same family. Furthermore, a multiperson TOPSIS technique is developed for the structure of mIVF with an algorithm for the selection of the best alternative.