Logarithmic aggregation operators and distance measures
- Publication Type:
- Journal Article
- Citation:
- International Journal of Intelligent Systems, 2018, 33 (7), pp. 1488 - 1506
- Issue Date:
- 2018-07-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Alfaro-Garc-a_et_al-2018-International_Journal_of_Intelligent_Systems.pdf | Published Version | 205.03 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2018 Wiley Periodicals, Inc. The Hamming distance is a well-known measure that is designed to provide insights into the similarity between two strings of information. In this study, we use the Hamming distance, the optimal deviation model, and the generalized ordered weighted logarithmic averaging (GOWLA) operator to develop the ordered weighted logarithmic averaging distance (OWLAD) operator and the generalized ordered weighted logarithmic averaging distance (GOWLAD) operator. The main advantage of these operators is the possibility of modeling a wider range of complex representations of problems under the assumption of an ideal possibility. We study the main properties, alternative formulations, and families of the proposed operators. We analyze multiple classical measures to characterize the weighting vector and propose alternatives to deal with the logarithmic properties of the operators. Furthermore, we present generalizations of the operators, which are obtained by studying their weighting vectors and the lambda parameter. Finally, an illustrative example regarding innovation project management measurement is proposed, in which a multi-expert analysis and several of the newly introduced operators are utilized.
Please use this identifier to cite or link to this item: