A Novel Hybrid Particle Swarm Optimization Algorithm for Path Planning of UAVs

Automatic path planning problem is essential for efficient mission execution by unmanned aerial vehicles (UAVs), which needs to access the optimal path rapidly in the complicated field. To address this problem, a novel hybrid particle swarm optimization (PSO) algorithm, namely, SDPSO, is proposed in this article. The proposed algorithm improves the update strategy of the global optimal solution in the PSO algorithm by merging the simulated annealing algorithm, which enhances the optimization ability and avoids falling into local convergence; each particle integrates the beneficial information of the optimal solution according to the dimensional learning strategy, which reduces the phenomenon of particles oscillation during the evolution process and increases the convergence speed of the SDPSO algorithm. The simulation results show that compared with PSO, dynamic-group-based cooperative optimization (DGBCO), gray wolf optimizer (GWO), RPSO, and two-swarm learning PSO (TSLPSO), the SDPSO algorithm can quickly plan higher quality paths for UAVs and has better robustness in complex 3-D environments.

Automated summary

This article proposes a novel hybrid particle swarm optimization (PSO) algorithm, SDPSO, for the automatic path planning problem of unmanned aerial vehicles (UAVs). The algorithm improves the update strategy of the global optimal solution in the PSO algorithm by merging the simulated annealing algorithm, and integrates the beneficial information of the optimal solution according to the dimensional learning strategy for each particle. Simulation results show that the SDPSO algorithm can quickly plan higher quality paths for UAVs and has better robustness in complex 3-D environments compared to other algorithms.