Political polarization has become a defining structural feature of contemporary democratic societies, driven by complex interactions between individual bias, localized social influence, and broader informational environments. Understanding the mechanisms through which heterogeneous opinions evolve into structured ideological divisions requires formal computational modelling capable of integrating spatial, behavioural, and temporal components.
This paper presents an approach to modeling the movement of a multi-agent system in a two-dimensional space using a modified Particle Swarm Optimization (PSO) algorithm, adapted to account for the physical properties of the agents. The standard PSO, originally designed for solving optimization problems through swarm behavior, has been extended to simulate the motion of physical objects with defined mass, velocity, and inter-agent interactions.
The presented study is dedicated to the dynamic pathfinding problem for UV. Since the automation of UV movement is an important area in many applied domains like robotics, the development of drones, autopilots, and self-learnable platforms, we propose and study a promising approach based on the algorithm of swarm AI. Given the 2D environment with multiple obstacles of rectangular shape, the task is to dynamically calculate a suboptimal path from the starting point to the target.
At this article proposed to use intelligent planning agents using ontological approach to automate the procedures of formation of many alternative solutions and the choice of rational decision in branch of GIS. It proposed to use the developed knowledge base in the field of methods of swarm intelligence based on adaptive ontology and a database of scientific publications in this field. All applied problems in the branch of geoinformation systems are divided into classes of problems: stationary, quasi-stationary, dynamic.
Presented the basic theories and algorithms with the help of which common coordinated actions of a group of objects are achieved. To research the joint work of a group of drones (UAVs) capable of self-organization, using the theory of swarm intelligence. The method of organizing the interaction of a group of UAVs in the environment, by dividing the group into local subgroups, is considered. The proposed algorithm to prevent possible collisions of neighboring UAVs by recalculating the flight trajectory.
A bee colony optimization algorithm and its modifications as a way to solve the complex combinatorial optimization problems are described. Biological basis, advantages, disadvantages and usage of this method are studied.