A Hybrid Particle Swarm Optimization Algorithm for Multi-Objective UAV Swarm Topology Shaping

UAV swarm need to form specific topology shapes based on the requirements of various missions. Existing topology shaping methods adopting fixed topology shaping positions fail to optimize the total traveled distance for formation switching of UAV swarm. Moreover, the use of simplified energy models often leads to inaccurate shaping decisions. To cope with these drawbacks, this paper proposes a multi-objective optimization approach for UAV swarm topology shaping based on the Particle Swarm Optimization algorithm, while a practical energy consumption model is adopted. To handle the continuous and discrete solution spaces of topology shaping position and matching relationships, a hybrid Particle Swarm Optimization (PSO) and Discrete Particle Swarm Optimization (DPSO) algorithm is introduced. This approach jointly optimizes the global energy consumption and standard deviation of flight distances, guiding the particle swarm updates to achieve multi-objective optimization. Our simulation results show that, in comparison to existing algorithms, the proposed method extends the working time of UAV swarm, reduces topology shaping time by 20%, and lowers the energy consumption of the most energy-demanding UAVs by 36.5%.