Keywords
Unmanned Aerial Vehicles
Bio-Inspired Optimization
Ant Colony Optimization
Particle Swarm Optimization
Artificial Bee Colony
swarm intelligence
UAV Communication
Routing Optimization
Trajectory Planning
Network Connectivity
Multi-Terrain Simulation
MATLAB Simulation
Distributed Systems
Aerial Networking
Abstract
Flying Ad Hoc Networks (FANETs) composed of unmanned aerial vehicles (UAVs) provide flexible and infrastructure-free communication that is applicable to disaster management, environmental monitoring, and surveillance. Nevertheless, the node mobility is high, and topology change is frequent, hence reliable routing and coordination are difficult. The bio-inspired optimization algorithms have been extensively explored to solve these problems because of their flexibility and decentralized character [1], [2]. This paper offers a comparative performance study of the Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms in the FANET settings. To simulate the behaviour of a UAV swarm in several terrains, such as mountain terrain, urban terrain, desert terrain, and ocean terrain, a MATLAB simulation framework is created. The algorithms are compared according to the convergence rate, network connectivity, stability, and optimization of the trajectory. The findings of the simulations suggest that PSO can converge much faster, ACO can give very effective path optimization, and ABC can be more adaptable in highly dynamic environments. The paper identifies trade-offs between the chosen algorithms and offers recommendations on appropriate algorithms to choose when using various FANET applications. Results can be used to design intelligent and energy-efficient coordination schemes for the future UAV networks.
References
I. Bekmezci, O. K. Sahingoz, and Ş. Temel, “Flying ad-hoc networks (FANETs): A survey,” Ad Hoc Networks, vol. 11, no. 3, pp. 1254–1270, May 2013, doi: 10.1016/j.adhoc.2012.12.004.
E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems. New York, NY, USA: Oxford Univ. Press, 1999.
Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: Opportunities and challenges,” IEEE Communications Magazine, vol. 54, no. 5, pp. 36–42, May 2016, doi: 10.1109/MCOM.2016.7470933.
C. E. Perkins, E. M. Royer, and S. R. Das, “Ad hoc On-Demand Distance Vector (AODV) routing,” RFC 3561, IETF, Jul. 2003.
M. Dorigo and T. Stützle, Ant Colony Optimization. Cambridge, MA, USA: MIT Press, 2004.
M. Gunes, U. Sorges, and I. Bouazizi, “ARA—The ant-colony based routing algorithm for MANETs,” in Proc. Int. Workshop on Ad Hoc Networking (IWAHN), Vancouver, Canada, 2002, pp. 79–85.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, 1995, pp. 1942–1948, doi: 10.1109/ICNN.1995.488968.
D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, Nov. 2007, doi: 10.1007/s10898-007-9149-x.
Y. Zhang, Y. Wang, and S. Li, “UAV path planning based on particle swarm optimization,” in Proc. Int. Conf. Intelligent Computing (ICIC), Shanghai, China, 2011, pp. 1–8, doi: 10.1007/978-3-642-24553-4_1.
V. Sharma and R. Kumar, “An energy efficient routing protocol for wireless ad hoc networks using artificial bee colony optimization,” International Journal of Computer Networks & Communications, vol. 4, no. 3, pp. 121–133, May 2012, doi: 10.5121/ijcnc.2012.4310.
O. Oubbati, N. Chaib, A. Lakas, P. Lorenz, and M. Atiquzzaman, “UAV-assisted supporting services connectivity in urban VANETs,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3944–3951, Apr. 2019, doi: 10.1109/TVT.2019.2894025.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2026 Author