Optimization of Multi-Criterial Selection Algorithm with a Dynamically Filled Large Set of Alternatives

Abstract

We consider the problem of the correct ranking of a dynamically replenished large set of alternatives in multicriteria choice problems that use in the solution the previously obtained modified method for analyzing hierarchies, based on the operation of additive convolution of local priorities not on the obtained set of characteristics of paired comparison matrices (as in the classical method), but on a set of pairs the relative weights of the coordinates of the eigenvectors being compared with each other for each criterion and the subsequent operation of additive convolution according to the criteria and alternatives in each pair. In this version, the algorithm ensures that previously achieved preferences are preserved when adding new alternatives and, thereby, makes it possible to optimize when processing large volumes of dynamically changing data, which significantly expands the applicability of the popular algorithm.