In applied mathematics, specifically in fuzzy logic, the ordered weighted averaging (OWA) operators provide a parameterized class of mean type aggregation operators.
They were introduced by Ronald R.
[1][2] Many notable mean operators such as the max, arithmetic average, median and min, are members of this class.
They have been widely used in computational intelligence because of their ability to model linguistically expressed aggregation instructions.
An OWA operator of dimension
that has an associated collection of weights
lying in the unit interval and summing to one and with where
By choosing different W one can implement different aggregation operators.
The OWA operator is a non-linear operator as a result of the process of determining the bj.
It is bounded, monotonic, symmetric, and idempotent, as defined below.
Two features have been used to characterize the OWA operators.
The first is the attitudinal character, also called orness.
In addition A − C(max) = 1, A − C(ave) = A − C(med) = 0.5 and A − C(min) = 0.
Thus the A − C goes from 1 to 0 as we go from Max to Min aggregation.
The attitudinal character characterizes the similarity of aggregation to OR operation(OR is defined as the Max).
This defined as An alternative definition is
The dispersion characterizes how uniformly the arguments are being used.
The above Yager's OWA operators are used to aggregate the crisp values.
Can we aggregate fuzzy sets in the OWA mechanism?
The Type-1 OWA operators have been proposed for this purpose.
[3] [4] So the type-1 OWA operators provides us with a new technique for directly aggregating uncertain information with uncertain weights via OWA mechanism in soft decision making and data mining, where these uncertain objects are modelled by fuzzy sets.
The type-1 OWA operator is defined according to the alpha-cuts of fuzzy sets as follows: Given the n linguistic weights
in the form of fuzzy sets defined on the domain of discourse
-cuts of fuzzy sets
is a permutation function such that
th largest element in the set
The computation of the type-1 OWA output is implemented by computing the left end-points and right end-points of the intervals
Then membership function of resulting aggregation fuzzy set is: For the left end-points, we need to solve the following programming problem: while for the right end-points, we need to solve the following programming problem: This paper[5] has presented a fast method to solve two programming problem so that the type-1 OWA aggregation operation can be performed efficiently.
Amanatidis, Barrot, Lang, Markakis and Ries[6] present voting rules for multi-issue voting, based on OWA and the Hamming distance.
Barrot, Lang and Yokoo[7] study the manipulability of these rules.