SYNERGISTIC CONFLICT-FREE UAV SWARM MOVEMENT MODELING USING METAHEURISTIC APPROACHES

The paper considers a new synergistic conflict-free movement model for unmanned aerial vehicle (UAV) swarm on the battlefield, based on the metaheuristic approach of particle swarm optimization (PSO). The paper presents an improved algorithm that ensures effective swarm coordination, traffic safety and efficient communication between UAVs. The model is designed to be used in various combat conditions and demonstrates the ability of the swarm to adapt to dynamic changes on the battlefield and perform tasks with high efficiency. Improvements to the PSO algorithm include the addition of collision avoidance force vectors, which allows each UAV take into account the position of its neighbors and avoid conflict situations. This ensures a more stable and smoother UAV swarm movement, reducing the risk of collisions and increasing the overall effectiveness of combat missions. The model also provides for the ability to adapt to changing conditions on the battlefield, which allows the UAV swarm to respond effectively to new threats and challenges.The simulation results show that the proposed metaheuristic approach based on the improved PSO algorithm is capable of calculating suboptimal trajectories for UAV swarm, minimizing the risk of collisions and improving the overall performance of combat missions. A comparative analysis with the classical PSO algorithm has revealed the advantages of the proposed model in the context of the efficiency of coordination and safety of UAV swarm movement. These results confirm the prospects of using the developed approach to control UAV swarms in combat conditions.The proposed algorithm allows each UAV in the swarm to take into account the other UAV position and speed, which allows maintaining the optimal distance between them, reducing collision probability. This is achieved by introducing an avoidance force vector that is directed away from other UAVs. This approach allows a swarm of UAVs to act as a single organized structure, which significantly increases the efficiency of performing tasks in complex and dynamic combat conditions. In addition, the model takes into account various combat scenarios, including obstacle avoidance, target acquisition, and retreat to a safe distance. This makes the algorithm a versatile tool for managing UAV swarms in real-world combat conditions, where the speed of reaction and accuracy of task execution are crucial.