UAV Obstacle Avoidance Trajectory Planning Based on Improved Zebra Optimization Algorithm
Liang Qing, Miao Niu, Xinyuan Xu, Yue Hu · 2024
An upgraded Zebra Optimization Algorithm (GSZOA) is presented to solve the issues of slow convergence speed and easy fall into local optimum of Zebra Optimization Algorithm (ZOA) in obstacle avoidance trajectory planning of Unmanned aerial vehicle (UAV). In order to solve the problem of slow convergence, chaotic mapping is invoked for ZOA to enhance the population diversity and accelerate the convergence speed. The golden sine algorithm is integrated to improve the zebra position update formula, which effectively coordinates the global search and local mining ability. Aiming at the problem of easily falling into local optimization, a cycle mutation strategy is introduced to reduce the probability of this problem. Finally, the improved algorithm is compared with the other five algorithms on the classical benchmark function to verify the superiority of the GSZOA algorithm. It is also applied to UAV obstacle avoidance trajectory planning, and the simulation results show that show that the GSZOA algorithm has better optimization seeking ability and faster convergence.