A Novel Hybrid Differential Evolution and Enhanced Whale Optimization Algorithm for UAV Path Planning

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.