Myroslava Vovk

Work place: Lviv Polytechnic National University, Lviv, 79013, Ukraine



Research Interests: Solid Modeling, Mathematical Analysis


MyroslavaVovk received Master's degree in Mathematics from Lviv Ivan Franko University, Lviv, Ukraine in 1993. She received the PhD degree in Mathematics (Differential Equations) from the Faculty of Mathematics and Mechanics, KyivTaras Shevchenko University, Kyiv, Ukraine in 2001. Currently, she works in the Mathematics department of the Institute of Applied Mathematics and Basic Sciences at Lviv Polytechnic National University, Lviv, Ukraine. Research interests include applications of the computational and asymptotic methods in the mathematical modeling of the nonlineardynamical systems.

Author Articles
Information Technologies for Decision Support in Industry-Specific Geographic Information Systems based on Swarm Intelligence

By Vasyl Lytvyn Olga Lozynska Dmytro Uhryn Myroslava Vovk Yuriy Ushenko Zhengbing Hu

DOI:, Pub. Date: 8 Apr. 2023

A method of choosing swarm optimization algorithms and using swarm intelligence for solving a certain class of optimization tasks in industry-specific geographic information systems was developed considering the stationarity characteristic of such systems. The method consists of 8 stages. Classes of swarm algorithms were studied. It is shown which classes of swarm algorithms should be used depending on the stationarity, quasi-stationarity or dynamics of the task solved by an industry geographic information system. An information model of geodata that consists in a formalized combination of their spatial and attributive components, which allows considering the relational, semantic and frame models of knowledge representation of the attributive component, was developed. A method of choosing optimization methods designed to work as part of a decision support system within an industry-specific geographic information system was developed. It includes conceptual information modeling, optimization criteria selection, and objective function analysis and modeling. This method allows choosing the most suitable swarm optimization method (or a set of methods). 

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