  This paper presents a new algorithm named spherical vector-based particle
swarm optimization (SPSO) to deal with the problem of path planning for
unmanned aerial vehicles (UAVs) in complicated environments subjected to
multiple threats. A cost function is first formulated to convert the path
planning into an optimization problem that incorporates requirements and
constraints for the feasible and safe operation of the UAV. SPSO is then used
to find the optimal path that minimizes the cost function by efficiently
searching the configuration space of the UAV via the correspondence between the
particle position and the speed, turn angle and climb/dive angle of the UAV. To
evaluate the performance of SPSO, eight benchmarking scenarios have been
generated from real digital elevation model maps. The results show that the
proposed SPSO outperforms not only other particle swarm optimization (PSO)
variants including the classic PSO, phase angle-encoded PSO and quantum-behave
PSO but also other state-of-the-art metaheuristic optimization algorithms
including the genetic algorithm (GA), artificial bee colony (ABC), and
differential evolution (DE) in most scenarios. In addition, experiments have
been conducted to demonstrate the validity of the generated paths for real UAV
operations. Source code of the algorithm can be found at
https://github.com/duongpm/SPSO.
