A global dynamic evolution snow ablation optimizer for unmanned aerial vehicle path planning under space obstacle threat.

In this paper, an improved Global Dynamic Evolution Snow Ablation Optimizer (GDSAO) is proposed in order to solve the problem of global optimization and Unmanned Aerial Vehicle (UAV) path planning in 3D space with obstacle threats. Three improvement schemes are proposed in GDSAO: (1) Population initialization is carried out using the theory of the best point set to obtain a more diverse initial population; (2) A dynamic snowmelt ratio using the global evolutionary dispersion is proposed to adapt the exploitation process of the original SAO to the evolutionary process of population fitness; (3) A neighborhood dimensional search scheme is proposed to update the locations of all searched individuals outside the elite pool to obtain better population fitness. The algorithm was tested on 30 10-dimensional problems at CEC 2017 and performed better than a series of joint and leading optimization algorithms. The path planning problem of UAV was solved, and the path satisfying all obstacle avoidance threats and corner constraints was obtained. By comparison, GDSAO is superior to the existing algorithms in terms of reliability and stability of optimization.