Generalized aggregation operators for intuitionistic fuzzy sets

  • Authors:

  • Hua Zhao;Zeshui Xu;Mingfang Ni;Shousheng Liu

  • Affiliations:

  • Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, People's Republic of China and Institute of Sciences, PLA University of Science and Technology, N ...;Institute of Sciences, PLA University of Science and Technology, Nanjing 210007, People's Republic of China;Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, People's Republic of China;Institute of Sciences, PLA University of Science and Technology, Nanjing 210007, People's Republic of China

  • Venue:
  • International Journal of Intelligent Systems
  • Year:
  • 2010

Quantified Score

Hi-index 0.01

Visualization

Abstract

The generalized ordered weighted averaging (GOWA) operators are a new class of operators, which were introduced by Yager (Fuzzy Optim Decision Making 2004;3:93–107). However, it seems that there is no investigation on these aggregation operators to deal with intuitionistic fuzzy or interval-valued intuitionistic fuzzy information. In this paper, we first develop some new generalized aggregation operators, such as generalized intuitionistic fuzzy weighted averaging operator, generalized intuitionistic fuzzy ordered weighted averaging operator, generalized intuitionistic fuzzy hybrid averaging operator, generalized interval-valued intuitionistic fuzzy weighted averaging operator, generalized interval-valued intuitionistic fuzzy ordered weighted averaging operator, generalized interval-valued intuitionistic fuzzy hybrid average operator, which extend the GOWA operators to accommodate the environment in which the given arguments are both intuitionistic fuzzy sets that are characterized by a membership function and a nonmembership function, and interval-valued intuitionistic fuzzy sets, whose fundamental characteristic is that the values of its membership function and nonmembership function are intervals rather than exact numbers, and study their properties. Then, we apply them to multiple attribute decision making with intuitionistic fuzzy or interval-valued intuitionistic fuzzy information. © 2009 Wiley Periodicals, Inc.