Специфіка багатопотокового методу реалізації мурашиного алгоритму - Пилипюк, Тетяна; Щирба, Віктор (Kamianets-Podilskyi National Ivan Ohiienko University, 2025)
This work investigates the specifics and prospects of applying distributed systems principles and parallel programming to the processing of network graph models. The subject of experimental study for its characteristic features is a polynomial bio-inspired ant colony algorithm, based on a metaheuristic approach to modeling ant behavior. The research aims to obtain adequate results for constructing predicted movement trajectories that satisfy optimization conditions. The motivation for addressing this problem and achieving the work's goal stems from a wide range of applied tasks. These arise both at the regional level, within urban community economic and social development programs, and in promising areas of scientific research. Such areas include planning economic and social development, optimizing transportation, and exploring telecommunication networks and artificial intelligence systems. The research findings can also be used for planning military operations involving the group deployment of various armed forces units, especially when group targets are anticipated. Employing a heuristic ant colony algorithm to study problems represented as graph models, which are readily amenable to restructuring by multithreaded big data processing methods and tools, offers opportunities for effectively increasing computational speed. This is achieved through high-performance computing approaches, particularly when a large number of permissible routes need simultaneous analysis. This organic combination of the two technologies not only accelerates the computational process of finding the optimal route but also ensures better scalability of the investigated system. Within this research, the primary focus is on utilizing multithreaded computations. This enables the implementation of a scalable architecture capable of parallel processing a large number of agents in real time.