Swarm Intelligence for UAV

This paper explores the utilization of swarm intelligence (SI) in unmanned aerial vehicle (UAV) systems, focusing on the application of SI algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). These algorithms enable UAV swarms to perform complex tasks autonomously, including path planning, dynamic task allocation, and obstacle avoidance. The study examines practical applications across various domains, including military operations, environmental monitoring, disaster response, and infrastructure inspection. The advantages of SI, such as flexibility, robustness, and scalability, are highlighted alongside challenges related to communication, coordination, and regulatory compliance. The paper concludes with a discussion on future directions, emphasizing the potential of integrating SI with advanced technologies like artificial intelligence to enhance the capabilities and autonomy of UAV systems.