Adaptive Unmanned Surface Vehicles swarm optimization for target tracking in dynamic environments

Abstract This research investigates the performance and efficiency of Unmanned Surface Vehicles (USVs) in multi-target tracking scenarios using the Adaptive Particle Swarm Optimization with k-Nearest Neighbors (APSO–kNN) algorithm. This study explores various search patterns—Random Walk, Spiral, Lawnmower, and Cluster Search—to assess their effectiveness in dynamic environments. Through extensive simulations, this work evaluates the impact of different search strategies, varying the number of targets and USVs’ sensing capabilities, and integrating a Pursuit–Evasion model to test adaptability. The study findings demonstrate that systematic search patterns like Spiral and Lawnmower provide superior coverage and tracking accuracy, making them ideal for thorough area exploration. In contrast, the Random Walk pattern, while highly adaptable, shows lower accuracy due to its non-deterministic nature, and Cluster Search maintains group cohesion but is heavily dependent on target distribution. The mixed strategy, combining multiple patterns, offers robust performance across varied scenarios, while APSO–kNN effectively balances exploration and exploitation, making it a promising approach for real-world applications such as surveillance, search and rescue, and environmental monitoring. This study provides valuable insight into optimizing search strategies and sensing configurations for USV swarms, ultimately enhancing their operational efficiency and success in complex environments.