UAV path planning in mountain areas based on a hybrid parallel compact arithmetic optimization algorithm - Wang, Ruo-Bin; Wang, Wei-Feng; Geng, Fang-Dong; Pan, Jeng-Shyang; Chu, Shu-Chuan; Xu, Lin (Neural computing & applications, 2025)
Unmanned Aerial Vehicle (UAV) path planning is one of the core components of its entire autonomous control system. The main challenge lies in efficiently obtaining an optimal flight route in complex environments, especially in mountain areas. To address this, we propose a novel version of arithmetic optimization algorithm (AOA), named parallel and compact AOA (PCAOA). In PCAOA, the compact technique can save the memory of UAV and shorten the calculation time, and the parallel technique can quicken the convergence speed and improve the solution accuracy. In addition, the flight path generated by PCAOA is smoothed with cubic B-spline curves, making the path suitable for a UAV. The performance of PCAOA is demonstrated on 23 benchmark functions. Experimental results show that PCAOA achieves competitive results. Finally, the simulation studies are conducted to verify that PCAOA can successfully acquire a feasible and effective route in different mountain areas.