Comparative analysis of swarm intelligence metaheuristic algorithms for group UAV control - Tiushkevych, D.A.; Huzii, M.M. (Problems of Informatization and Management, 2025)
This article is devoted to conducting a comparative analysis and evaluating the efficiency of modern metaheuristic algorithms introduced no later than 2021-namely, African Vultures Optimization Algorithm (AVOA), Harris Hawks Optimization (HHO), Dung Beetle Optimizer (DBO), and Snake Optimizer (SO) which can be applied to decentralized unmanned aerial vehicle (UAV) swarm control tasks. The research is timely and driven by the shift in UAV management from single-unit control to the coordination of autonomous swarms capable of executing labor-intensive tasks while minimizing risks inherent to centralized systems, specifically by reducing critical communication latency and enhancing resilience against control node failures. The research methodology was implemented using simulation modeling in a Python version 3.12.9 environment. The testing suite consisted of 23 classic benchmark functions, on which the four aforementioned algorithms were evaluated. Accuracy (Mean), iterative stability (Std), and convergence speed at a fixed dimensionality of D = 30 for each algorithm were selected as the primary comparison metrics. The results of the numerical experiments demonstrated a clear specialization for each algorithm: it was established that AVOA possesses the highest, near-benchmark localization accuracy and exceptional stability across most benchmarks compared to its competitors. This makes the algorithm the optimal choice for swarm targeting or spatial station-keeping tasks. DBO proved to be the superior algorithm for global exploration in complex terrains, demonstrating the highest efficiency in path planning through unknown areas with a high density of obstacles. The HHO algorithm showed sufficiently balanced indicators of accuracy and stability, particularly on curved landscapes. Generally competitive with AVOA, HHO is also excellently suited for target tracking tasks. The SO algorithm proved to be the least effective on average due to slow convergence, yet it maintains sufficient effectiveness for use in low-dimensional tasks.