Swarm Intelligence Algorithms for Coordinated Control of Multiple UAVs: Performance Analysis Through Simulation

Abstract

Swarm intelligence, derived from collective behaviors observed in social insects, birds, and fish, provides robust decentralized coordination mechanisms for multi-UAV systems operating in complex, dynamic environments. This paper presents a comprehensive performance analysis of prominent swarm intelligence algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Firefly Algorithm (FA)—applied to coordinated UAV control tasks. We develop a unified simulation framework implementing these algorithms for representative multi-UAV missions including area coverage, target search and tracking, formation maintenance, and collaborative payload transport. The study systematically evaluates algorithm performance across multiple dimensions: convergence speed to optimal configurations, scalability with swarm size (10 to 100 UAVs), robustness to individual agent failures, communication efficiency, and adaptability to dynamic obstacles and changing mission objectives. Our simulation environment incorporates realistic constraints including limited communication range, packet loss, computational delays, and UAV kinematic limitations. Comparative analysis reveals that PSO variants demonstrate superior convergence for formation control tasks, while ACO excels in path planning scenarios with complex obstacle fields. ABC algorithms show remarkable robustness to communication disruptions, and FA provides effective solutions for dynamic target tracking with minimal coordination overhead. We investigate hybrid approaches combining multiple swarm intelligence paradigms, demonstrating that adaptive algorithm selection based on mission phase yields significant performance improvements. The paper examines parameter sensitivity analysis, identifying critical tuning requirements for inertia weights, pheromone evaporation rates, and neighborhood topologies. Advanced variants incorporating opposition-based learning, chaotic maps, and Lévy flight patterns are evaluated for enhanced exploration-exploitation balance. Simulation results quantify trade-offs between solution optimality, computational complexity, and communication bandwidth consumption. The study concludes with practical implementation guidelines and identifies research gaps in formal stability guarantees and integration with learning-based approaches.