A novel hybrid improved dingo algorithm for unmanned aerial vehicle path planning

The unmanned aerial vehicle (UAV) trajectory planning is a crucial research area research area in the field of unmanned aerial system (UAS), which aims to find optimal flight paths in complex and changing environments. It could ensure better performance of (unmanned aerial vehicles) UAVs during their missions. A new hybrid algorithm named HIDOA-SOS is proposed by combining improved dingo algorithm (HIDOA) and the symbiotic organism search algorithm (SOS) in this paper. The DOA algorithm is optimized with an update strategy to speed up convergence and maintain better population exploration. The optimized DOA algorithm is gradually merged with the SOS algorithm in order to improve the population development of the algorithm. The generated trajectory is smoothed by cubic B-spline curves to make the path more suitable for UAV flight. The results of simulation experiments show that the HIDOA-SOS algorithm can successfully obtain feasible paths and outperforms the DOA algorithm, SOS algorithm and SSA algorithm.