ALGORITHM FOR THE CONSTRUCTION AND OPTIMIZATION OF THE TERRITORIAL LOCATION OF SOUND SOURCES OF EMERGENCY NOTIFICATION SYSTEMS OF THE POPULATION
Abstract
The article is devoted to the development of an algorithm for solving the problem of optimizing the territorial placement of elements of the public emergency notification system according to the criterion of maximizing the coverage of the territory of the settlement with the permissible power level of sound signal sources based on the application of genetic algorithms. The relevance of the article is due to the need to improve modern systems of emergency notification of the population, especially in cases where it is necessary to take into account the characteristics of certain territories. This problem is quite important in today’s world of permanent emergency situations, and the high-quality distribution of emergency notification sources can become one of the important elements of the system of warning and timely response to disasters. One of the most effective technologies for building such systems is the use of genetic algorithms, which ensure the search for the most effective options in a dynamic mode with the possibility of their further optimization. Therefore, the development and improvement of methods of rational placement of sources and optimization of the technical characteristics of the system of emergency notification of the population is an urgent problem for the safety and preservation of the health of the population. The purpose of this article is to develop an algorithm for structural optimization of the territorial placement of sound signal sources of the public emergency notification system according to the criterion of maximizing the coverage of the territory of the settlement with the permissible power level of sound signal sources based on the modification of the genetic algorithm. In accordance with the proposed approach, the function of maximum coverage of the territory of the settlement with a minimum number of sound signal sources of permissible power is used as an optimization criterion. In the process of searching for the optimal structure of the territorial arrangement of elements of the notification system, a modified genetic algorithm of analysis and determination of the most effective options is used. As a condition for building an optimal solution to the problem, the condition of complete coverage of the territory of the population with sound warning signals is defined. According to the results of the conducted research, it was found that the developed algorithm allows determining the optimal structure of the territorial placement of sound signal sources of the emergency public notification system based on the criterion of maximizing the coverage of the territory of the settlement with the permissible power level of the sound signal sources.
References
2. Abukhalaf A. H. I. & J. Von Meding. Communication challenges in campus emergency planning: The case of hurricane dorian in Florida. Natural Hazards. 2020. No.104. P. 1535-1565. https://doi.org/10.1007/s11069-020-04231-1.
3. Sutton J., Spiro E., S. Johnson B., Fitzhugh S., Gibson B., & Butts C. T. Warning tweets: Serial transmission of messages during the warning phase of a disaster event. Information, Communication & Society. 2014. No.6. P.765-787. https://doi.org/10.1080/1369118X.2013.862561.
4. Sivanandam S. N., Deepa S. N. Genetic Algorithms. Introduction to Genetic Algorithms. Springer, Berlin, Heidelberg. 2008. No.1. P.15-37. https://doi.org/10.1007/978-3-540-73190-0_2
5. Пиріг Я., Климаш М., Пиріг Ю., Лаврів О. Генетичний алгоритм як засіб розв’язання оптимізаційних задач. Інфокомунікаційні технології та електронна інженерія. 2023. C.95 – 103.
6. Porta, J., Parapar, R., Doallo, F., Rivera, F., Santé, I., & Crecente, R. (High performance genetic algorithm for land use planning. Computers, Environment and Urban Systems. 2013. No.37. P.45-58. https://doi.org/10.1016/j.compenvurbsys. 2012.05.003.
7. Zhang, R., Ong, S.K., & Nee, A.Y.C. A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Applied Soft Computing. 2015. No.37. P.521-532. https://doi.org/10.1016/j.asoc.2015.08.051
8. Pengfei G., Wang X., Han Y. The enhanced genetic algorithms for the optimization design. 3rd International Conference on Biomedical Engineering and Informatics. 2010. No.1. P.2990-2994. 10.1109/BMEI.2010.5639829.
ISSN 
