Abstract
Abstract
 OWA (Ordered Weighted Averaging) is a flexible aggregation operator which is come up with Yager to create a decision function in multi-criteria decision making. It is possible to determine how optimistic or pessimistic the decision maker's opinion with the value obtained from the weights of this operator. The determination of OWA weights cannot provide characterization by itself. If it is desired to aggregate various sized objects in terms of generalization and reusability of OWA weights, a more general form is needed. In this study, we propose the parameterized piecewise linear stress function and the approach to characterize OWA weights. The stress function is expressed by parameters which are obtained by artificial bee colony algorithm. Also the weights are approximately found by using parameters.
 Keywords – OWA operator, aggregation, artificial bee colony algorithm.
Highlights
The aggregation of the criterion functions to form decision functions is important in many disciplines
We propose the generalized mixed linear stress function and the approach to characterize OWA weights
The stress function is expressed by parameters which are obtained by artificial bee colony algorithm
Summary
The aggregation of the criterion functions to form decision functions is important in many disciplines. A number of methods are proposed to find the weights associated with the OWA operator (Nasiboglu & Tezel, 2016). We propose the generalized mixed linear stress function and the approach to characterize OWA weights. The stress function is expressed by parameters which are obtained by artificial bee colony algorithm.
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