An Efficient Elimination of Input Data in the OWA Aggregation

Abstract

In the paper, we present an efficient method for pruning multiple alternatives in the OWA aggregation. The proposed method intends to identify inferior alternatives per se and diminish the number of alternatives without any efforts to exploit the OWA operator weights from decision maker. The efficacy of the proposed method is verified by simulation analysis in which different levels of alternatives and different levels of criteria are used.