Fuente: PubMed "swarm" Sci Rep. 2026 May 27. doi: 10.1038/s41598-026-42372-8. Online ahead of print.ABSTRACTThree-dimensional flight-path planning for unmanned aerial vehicles (UAVs) inherently involves multiple, often conflicting objectives-minimizing route length and energy consumption, maximizing safety by avoiding no-fly zones and high-turbulence regions, and ensuring smooth maneuverability within kinematic limits. This study presents an enhanced adaptation of the Task Allocation and Archive-Guided Mutation Particle Swarm Optimization (TAMOPSO) algorithm to address these challenges. In the proposed framework, each candidate path is encoded as a sequence of discrete 3D waypoints, while dynamic task allocation partitions the swarm into role-specific subpopulations: global explorers for broad route discovery, local refiners for obstacle-proximal optimization, and altitude managers for vertical profile adjustment. An external archive of nondominated solutions, maintained through a uniform contribution index, preserves Pareto-front diversity and guides adaptive Lévy-flight mutations that balance global exploration with local refinement according to the swarm's convergence state. The algorithm's effectiveness is evaluated across benchmark terrains of varying complexity, including scenarios with seven and ten peaks, obstacle clusters, and restricted airspace constraints. Comparative results against fourteen baseline algorithms demonstrate that TAMOPSO achieves up to 60% reduction in total flight distance, and 47% faster convergence on average, while producing smoother trajectories and more uniformly distributed Pareto fronts. Extensive loop-based Monte Carlo experiments were implemented in the MATLAB environment, confirming the statistical robustness, computational efficiency, and scalability of the proposed approach. Overall, the integration of role-based task allocation and archive-driven adaptive mutation establishes TAMOPSO as a powerful and versatile framework for intelligent, real-time UAV path optimization in complex 3D mission environments.PMID:42203802 | DOI:10.1038/s41598-026-42372-8