Recently, path planning has achieved remarkable progress in enhancing global search capability and convergence accuracy through heuristic and learning-inspired optimization frameworks. However, real-time adaptability in dynamic environments remains a critical challenge for autonomous navigation, particularly when robots must generate collision-free, smooth, and efficient trajectories under complex constraints. By analyzing the difficulties in dynamic path planning, the Wind Driven Optimization (WDO) algorithm emerges as a promising framework owing to its physically interpretable search dynamics. Motivated by these observations, this work revisits the WDO principle and proposes a variant formulation, Multi-hierarchical adaptive wind driven optimization(MAWDO), that improves adaptability and robustness in time-varying environments. To mitigate instability and premature convergence, a hierarchical-guidance mechanism divides the population into multiple groups guided by individual, regional, and global leaders to balance exploration and exploitation. Extensive evaluations on sixteen benchmark functions show that MAWDO achieves superior optimization accuracy, convergence stability, and adaptability over state-of-the art metaheuristics. In dynamic path planning, MAWDO shortens the path length to 469.28 pixels, improving over Multi-strategy ensemble wind driven optimization(MEWDO), Adaptive wind driven optimization(AWDO) and WDO by 3.51\%, 11.63\% and 14.93\%, and achieves the smallest optimality gap (1.01) with smoothness 0.71 versus 13.50 and 15.67 for AWDO and WDO, leading to smoother, shorter, and collision-free trajectories that confirm its effectiveness for real-time path planning in complex environments.

翻译：近年来，路径规划通过启发式和受学习启发的优化框架，在增强全局搜索能力和收敛精度方面取得了显著进展。然而，动态环境中的实时适应性仍然是自主导航面临的关键挑战，尤其是在机器人必须在复杂约束下生成无碰撞、平滑且高效的轨迹时。通过分析动态路径规划中的难点，风驱动优化算法因其物理可解释的搜索动力学而成为一个有前景的框架。受这些观察启发，本研究重新审视了风驱动优化原理，并提出了一种变体公式——多层自适应风驱动优化算法，该算法提高了时变环境中的适应性和鲁棒性。为了缓解不稳定性和早熟收敛问题，一种分层引导机制将种群划分为多个组，分别由个体、区域和全局领导者引导，以平衡探索与利用。在十六个基准函数上的广泛评估表明，MAWDO在优化精度、收敛稳定性和适应性方面优于当前最先进的元启发式算法。在动态路径规划中，MAWDO将路径长度缩短至469.28像素，相比多策略集成风驱动优化算法、自适应风驱动优化算法和风驱动优化算法分别提升了3.51%、11.63%和14.93%，并实现了最小的最优性差距（1.01），平滑度为0.71，而自适应风驱动优化算法和风驱动优化算法的平滑度分别为13.50和15.67，从而生成了更平滑、更短且无碰撞的轨迹，证实了其在复杂环境中实时路径规划的有效性。