ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 5. Vol. 27. 2021

DOI: 10.17587/it.27.235-241

S. I. Kolesnikova, D. Sc., skolesnikova@yandex.ru, S. A. Karavanova, Master's Student, Saint-Petersburg State University of Aerospace Instrumentation, St. Petersburg, 190000, Russian Federation

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

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.
Keywords: big data, correct algorithm for ranking alternatives, optimization of the algorithm for ranking a large set of alternatives, performance indicators of the algorithm for pairwise comparisons; algorithmic complexity

P. 235–241

Acknowledgements: The reported study was funded by RFBR according to the research project ¹ 20-08-00747

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