A New Similarity Measure of Intuitionistic Fuzzy Set and Application in MADM Problem

Similarity measure is an important tool to measure the degree of resemblance between two intuitionistic fuzzy sets. In this paper, in order to overcome the counter-intuitive in some cases, a new similarity measure of intuitionistic fuzzy sets is constructed and successively applied in pattern recognition and medical diagnosis. Based on the proposed similarity measure, a new decision making method is put forward for the multi-attribute decision making (MADM) problem with attribute values expressed by intuitionistic fuzzy set. When the attribute weights information is completely unknown, maximizing deviation method is developed and used to determine the weights. When the attribute weights information is partly known, an optimization model is established for solving the attribute weights. Two MADM examples are given to illustrate the feasibility and practicability of the proposed decision making method.