### Algorithm for searching for Pareto-optimal solutions in problems of estimating coherent cognitiveness of resource networks

#### Abstract

Development of an algorithm for finding the optimal state of a resource network using the Pareto set, as well as a geometric interpretation of the problem using the deep analogy method. Purpose: creation and testing of an improved innovative model for searching for Pareto-optimal states of resource networks. Methods: optimizing locations using the Pareto set; methods of cognition, updated by the introduction of characteristic features of coherence in terms of entropy scattering and monochromatism of ontologies. Result: the core of the updated mathematical description of the search model for the Pareto-optimal states of resource networks was developed and presented. Conclusions: the productivity of the search model for Pareto-optimal states is shown with the need for its further modernization in parts that display dynamic features and properties. The obtained results can be used to create control systems for monitoring resource networks. And the use of cognitive methods can improve the efficiency of such systems, allowing you to take into account the parameters and features that affect performance and stability.

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S. L. Belyakov, Ya. A. Kolomiytsev, I. N. Rozenberg, and M. N. Savelyeva, Tech. A model for solving the routing problem in an intelligent geoinformation system. Izvestiya of the Southern Federal University. Technical science. – 2011. – No. 5 (118). – pp. 113-119. (in russian)

S. L. Belyakov, Ya. A. Kolomiytsev, I. N. Rozenberg, and M. N. Savelyeva, Tech. Optimization of flows in transport systems // Proceedings of the Southern Federal University. Technical science. – 2014. – No. 5 (154). – pp. 161-167. (in russian)

Dyshlenko S.G. Routing in transport networks // ITNOU: information technologies in science, education and management. – 2018. – No. 1. – pp. 15-20. (in russian)

Rud D.E. Technologies of topological optimization of traffic of information flows in telecommunication networks [Electronic resource] // Engineering Bulletin of the Don. – 2010. – No. 2. – pp. 95-107. – Access mode: http://www.ivdon.ru/uploads/article/doc/articles.193.big_image.doc. (in russian)

Bolbakov R.G., Mordvinov V.A., Berezkin P.V., Sivitsky I.I. Ontology of Functional Synergetics in Virtual Cognitive-Semiotic Design of Information Processes and Systems // Russian Technological Journal. – 2022. – No. 10 (1). – pp. 7-17. (in russian)

Lazarev E.A. Bicriteria model and algorithms for optimizing the data transmission network: Cand. cand. tech. Sciences: 05.13.01. - Nizhny Novgorod, 2013. – 120 p.

Perestoronin N.O. A new method of multiobjective optimization based on the local geometry of the Pareto set: WRC. master's degree. – 2013. – 37 p. (in russian)

Kaisa Miettinen, Francisco Ruiz, Andrzej P Wierzbicki Introduction to multiobjective optimization: interactive approaches. In Multiobjective Optimization, Springer, 2008. pp. 27–57.

Jorge Nocedal, Stephen J Wright Numerical optimization. Springer Science + Business Media, 2006.

Gabriele Eichfelder Adaptive scalarization methods in multiobjective optimization. Springer, 2008.

Perestoronin N.O., Yarotsky D.A. Testing of algorithms for multiobjective optimization // Proceedings of the 53rd Scientific Conference of the Moscow Institute of Physics and Technology. Moscow-Dolgoprudny, – 2010. – pp. 122-123. (in russian)

Zobnina O.V., Dyu A.I., Babaeva Yu.A. Multicriteria optimization // StudNet. – 2021. – No. 1. – pp. 87. (in russian)

Utyuzhnikov S.V., Maginot J, Guenov M.D. Local approximation of pareto surface. In Proceedings of the World Congress on Engineering, Citeseer, – 2007. V 2, pp. 898-903.

Joerg Fliege, Grana Drummond L.M., Benar F Svaiter Newton’s method for multiobjective optimization. SIAM Journal on Optimization, – 2009. No. 20(2), pp. 602-626.

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