A Comparative Analysis of Bio-Inspired Algorithms in Flying Ad Hoc Networks

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. Keywords: Flying Ad Hoc Networks, 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.