Generalized OWA Aggregation Operators

By Ronald R. Yager. Fuzzy Optimization and Decision Making, 2004

608 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/86e8dfea6e86a4083f4cdc33f7c823437349e422

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In Control and Systems Engineering

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Some geometric aggregation operators based on intuitionistic fuzzy sets (2006, methodology): “…and Da 2003): Another geometric aggregation operator called the OWG operator based on the OWA operator (Yager 1988, Yager 1993, Yager and Kacprzyk 1997, Yager 2004) and the geometric mean, was deﬁned as follows (Xu and Da 2003, Yager 2004, Herrera et al. 2001, Chiclana et al. 2001, Xu and Da…”
Power-Geometric Operators and Their Use in Group Decision Making (2010, background): “hybrid aggregation operator [15], fuzzy-weighted-average operator [16], generalized OWA aggregation operator [ 17 ], linguistic aggregation operators [18]–[22], etc.”
Using the OWA Operator in the Minkowski Distance (2008, methodology): “If we analyze the parameter λ, we can also obtain another group of special cases such as the usual OWA operat or [1], the ordered weighted geometric (OWG) operator [28]–[30] , the ordered weighted harmonic averaging (OWHA) operator [17] and the ordered weighted quadratic averaging (OWQA) operator [1”
Pythagorean fuzzy set: state of the art and future directions (2017, methodology): “Peng and Yuan (2016) developed an ordered weighted operator for aggregating the Pythagorean fuzzy information based on the generalized orderedweighted averaging operator (Yager 2004).”
The Induced Generalized OWA Operator (2009, methodology): “In order to solve this problem, we recommend to follow the policy developed by Yager and Filev [29] where they replace each argument of the tied IOWA pairs by their average.”
Some issues on intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm (2012, methodology): “Based on the generalized ordered weighted aggregation operator proposed by Yager [35], Zhao et al.”
Aggregation Functions: A Guide for Practitioners (2007, background): “A large family of generalized OWA functions is based on power functions, similar to weighted power means [271].”
Induced generalized intuitionistic fuzzy operators (2011, background): “From another point of research, Yager [42] extended the Choquet integral to a more general form by adding a parameter controlling the power to which the argument values are raised.”
Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making (2014, methodology): “Motivated by the generalized idea by Yager [49], we can unify the Hamming distance and the Euclidean distance by the following generalized distance measure: Please cation dgd H 1 S ðxiÞ;H 2 S ðxiÞ ¼ 1 L XL l¼1 d1l d 2 l 2sþ 1 !k0@ 1 A 1=k ð8Þ where k > 0.”
Hesitant fuzzy information aggregation in decision making (2011, background): “Yager [22] defined a generalized ordered weighted averaging (GOWA) operator, Zhao et al.”
Distance and similarity measures for hesitant fuzzy sets (2011, background): “Yager [50] generalized Xu and Chen’ distance measures and provided a variety of ordered weighted averaging norms, based on which he proposed several similarity measures.”
Fundamental Properties of Interval‐Valued Pythagorean Fuzzy Aggregation Operators (2016, background): “Later, Xia and Xu 14 gave some point aggregation operators for IFNs, which can reduce the indeterminacy degree of the aggregated arguments, and then motivated by the generalized aggregation operators, 9,15 they proposed a series of generalized intuitionistic fuzzy point aggregation operators.”
Intuitionistic Fuzzy Information Aggregation Using Einstein Operations (2012, methodology): “[71] R. R. Yager, “Generalized OWA aggregation operators,” Fuzzy Optim.”