Enhanced Whale Optimization Algorithm and Its Application in 3D Flight Path Planning for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) face significant challenges in global route planning within three-dimensional environments, which require avoiding obstacles, threat areas, and optimizing path length under various constraints. This paper formulates this problem as a constrained optimization task and proposes the Enhanced Advanced Adaptive Whale Optimization Algorithm (AAWOA), tailored to efficiently generate optimal and feasible routes. The AAWOA algorithm introduces chaos mapping, an optimized inertia weight strategy, and a nonlinear convergence factor to enhance global search capabilities and avoid entrapment in local optima. Simulation experiments within a three-dimensional framework demonstrate that AAWOA significantly outperforms both the original Whale Optimization Algorithm (WOA) and the Particle Swarm Optimization (PSO) algorithm. These results confirm the superior effectiveness of the proposed AAWOA algorithm for UAV flight path planning.