UAV mountain material transportation based on an improved sparrow search algorithm

Abstract

Aiming at problems such as slow planning time and local optimality when the sparrow search algorithm is applied to UAV transportation of materials in mountainous areas, a multi-strategy improved sparrow search algorithm is proposed by analyzing UAV performance and performance constraints. Firstly, the optimal point set is used in the population initialization stage to increase the quality of the initial solution. Secondly, the nonlinear dynamic weight factor is used to optimize the discoverer update formula to avoid the discoverer's dependence on its position, extend the search range in the early stage, and accelerate the convergence rate in the later stage. Then the crazy operator is integrated to optimize the predator update formula and improve the local search ability. Finally, the lens imaging reverse learning of dynamic boundary is used to avoid the local optimization of the algorithm. The effectiveness of the proposed algorithm is verified by six test functions, and the proposed algorithm is applied to UAV material transportation. The experimental results show that the proposed algorithm has a faster convergence speed and a shorter planned path than the traditional algorithm.