IHSSAO: An Improved Hybrid Salp Swarm Algorithm and Aquila Optimizer for UAV Path Planning in Complex Terrain

In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed is to enhance performance AO by introducing leader mechanism SSA, tent chaotic map, pinhole imaging opposition-based learning strategy. Firstly, map utilized substitute randomly generated initial population original algorithm increase diversity individuals. Secondly, integrate SSA into position update formulation basic AO, which enables search individuals fully utilize optimal solution information enhances global capability AO. Thirdly, introduce escape from local optimization. To verify effectiveness algorithm, tested it against five other advanced meta-heuristic algorithms on 23 classical benchmark functions 17 IEEE CEC2017 test functions. experimental results indicate that superior seven most cases. Eventually, applied IHSSAO, solve problem. solving problem