On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Generalizing the modeling of fuzzy logic controllers by parameterized aggregation operators
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Direct approach processes in group decision making using linguistic OWA operators
Fuzzy Sets and Systems
An efficient algorithm for fuzzy weighted average
Fuzzy Sets and Systems
Linguistic decision analysis: steps for solving decision problems under linguistic information
Fuzzy Sets and Systems - Special issue on soft decision analysis
Fuzzy weighted averages revisited
Fuzzy Sets and Systems - Information processing
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Fuzzy Systems
Construction of Aggregation Operators With Noble Reinforcement
IEEE Transactions on Fuzzy Systems
Aggregation Using the Fuzzy Weighted Average as Computed by the Karnik–Mendel Algorithms
IEEE Transactions on Fuzzy Systems
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Type-1 ordered weighted average (OWA) operator is important in decision making and data mining because it is able to aggregate linguistic terms represented as fuzzy sets via the OWA mechanism. Currently, the most efficient type-1 OWA operator is that proposed by Zhou et al. [10], namely, the @a-level type-1 OWA. The calculation complexity of the operator is between O(n) and O(n^2). Since the calculation of @a-level type-1 OWA is very similar to that of fuzzy weighted average (FWA) and the calculation complexity of several most efficient FWA algorithms is O(n), the recent FWA approaches may provide important reference for improving the efficiency of the @a-level type-1 OWA operator. In this paper, an opposite direction searching algorithm for calculating type-1 OWA (ODSOWA) is proposed based on ODSFWA, one of the most efficient FWA algorithms at present. Procedures of the proposed ODSOWA algorithm for calculating the type-1 OWA are explained, and the calculation complexity of the algorithm is proved to be O(n). Simulation was performed to compare the ODSFWA with the @a-level type-1 OWA in terms of computational costs and CPU time costs. The results indicate that the ODSOWA approach can save arithmetical operations significantly.