Logarithmic aggregation operators and distance measures. Issue 7 (25th March 2018)

• Record Type: Journal Article
• Authors: Alfaro‐García, Víctor G.; Merigó, José M.; Gil‐Lafuente, Anna M.; Kacprzyk, Janusz
• Is Part Of: International journal of intelligent systems. Volume 33:Issue 7(2018)
• Page Start: 1488
• Page End: 1506
• Publication Date: 2018-03-25
• Subjects: distance measures -- group decision making -- innovation project management -- logarithmic aggregation operators -- OWA operator; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.21988
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: 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.