About arithmetic-geometric multidistances.

In a previous paper (see [7] ) we considered the family of multi-argument functions called multidistances, introduced in some recent papers (see [1]-[6]) by J.Martin and G.Mayor , which extend to n-dimensional ordered lists of elements the usual concept of distance between a couple of points in a metric space. In particular Martin and Mayor investigated three classes of multidistances, that is Fermat, sum-based and OWA- based multidistances. In this note we introduce a new family of multidistance functions, which are a generalization of the sum-based multidistances and we call them arithmetic- geometric multidistancesMultidistance, sum-based multidistances, arithmetic-geometric multidistances.

Multi-argument distances and regular sum-based multidistances.

In this paper we consider the family of multi-argument functions called multidistances, introduced in some recent papers by J.Martin and G.Mayor, which extend to n-dimensional ordered lists of elements the usual concept of distance between a couple of points in a metric space. In particular Martin and Mayor investigated three classes of multidistances, that is Fermat, sum-based and OWA- based multidistances.In this note we focus our attention on a specific property of multidistances, i.e. regularity, and we provide an alternative proof about the regularity of the sum-based multidistances.distance, multidistance, regularity, sum-based multidistances;

New closeness coefficients for fuzzy similarity based fuzzy TOPSIS: an approach combining fuzzy entropy and multidistance

This paper introduces new closeness coefficients for fuzzy similarity based TOPSIS. The new closeness coefficients are based on multidistance or fuzzy entropy, are able to take into consideration the level of similarity between analysed criteria, and can be used to account for the consistency or homogeneity of, for example, performance measuring criteria. The commonly known OWA operator is used in the aggregation process over the fuzzy similarity values. A range of orness values is considered in creating a fuzzy overall ranking for each object, after which the fuzzy rankings are ordered to find a final linear ranking. The presented method is numerically applied to a research and development project selection problem and the effect of using two new closeness coefficients based on multidistance and fuzzy entropy is numerically illustrated