Safe and energy-aware navigation for unmanned aerial vehicles (UAVs) requires the simultaneous optimization of path length, curvature, obstacle clearance, altitude, energy expenditure, and mission time—within the tight computational limits of on-board processors. This study proposes a two-phase hybrid optimizer that couples the global search capability of Differential Evolution (DE) with an Enhanced Whale Optimization Algorithm (E-WOA) specialized for local refinement. E-WOA improves on the canonical WOA through three principled modifications: real-time boundary repair to ensure path feasibility, quasi-oppositional learning to restore population diversity, and an adaptive stagnation trigger that re-initiates exploration when progress stalls. When the population’s improvement plateaus, control transfers from DE to E-WOA, combining broad exploration with focused exploitation. Comparative experiments conducted in 3D environments with static obstacles that block direct line-of-sight routes demonstrate that the hybrid achieves lower composite cost—normalized over path length, curvature, risk, altitude, energy and time—shorter and smoother trajectories, and faster convergence than standard metaheuristics while preserving obstacle clearances and curvature limits. Averaged over 30 independent trials, our hybrid framework reduced the normalized composite cost by 14.5% relative to the next-best algorithm (Grey Wolf Optimizer) and produced feasible paths in an average of 2.35 seconds on commodity hardware—adequate for strategic re-planning, though further optimization is needed for sub-second control loops. Blending DE’s global reach with a diversity-aware, adaptively stalled WOA provides a practical foundation for strategic, near-real-time replanning in 3D airspaces.

This paper presents an Enhanced Adaptive B-Spline Smoothing approach for UAV path planning in complex three-dimensional environments. By leveraging the inherent local control and smoothness properties of cubic B-Splines, the proposed method integrates an adaptive knot selection mechanism—optimized via a genetic algorithm—with curvature-aware control point refinement to generate dynamically feasible and smooth flight paths. Simulation studies in a cluttered 3D airspace show that the proposed technique reduces path length and lowers maximum curvature compared to uniform and chord-length-based B-Spline strategies. Despite a moderate computational overhead, the results demonstrate smoother, more stable flight trajectories that adhere to aerodynamic constraints and ensure safe obstacle avoidance. This approach is particularly valuable for near-real-time missions, where flight stability, rapid re-planning, and energy efficiency are paramount. Results emphasize the potential of the proposed method for improving UAV navigation in various applications—such as urban logistics, infrastructure inspection, and search-and-rescue—by providing better maneuverability, reduced energy consumption, and increased operational safety to the UAV agents.