基于改进沙猫群算法的水下AUV路径规划
Path Planning of Underwater AUV Based on the Improved Sand Cat Swarm Algorithm

针对水下自主航行器(AUV)在复杂水下环境中进行三维路径规划时，沙猫群算法所面临的障碍物规避能力有限、迭代效率较低以及容易陷入局部最优解等问题，本研究提出了一种将沙猫群优化算法与莱维飞行方法相结合的策略，旨在提升沙猫群算法的整体性能。基于混沌映射的均匀分布特性，改进初始种群的生成策略，有效增强了群体的多样性；此外，引入互利共生机制，并结合莱维飞行策略进行调整，显著提高算法寻找全局最优解的能力。这一改进不仅提高了算法的收敛速度，也提升了求解精度。通过静态障碍与动态洋流干扰场景的仿真测试，改进的沙猫群算法(LVSCSO)在全局收敛性上显著优于PSO、GA等六类算法：最优解偏离度降低21.4%，最差解稳定性提升33.7%，平均解精度优化19.5%。结果表明，LVSCSO可有效应对复杂水下路径规划任务(如海底勘探)，具备工程部署潜力。
For underwater autonomous vehicle (AUV) in complex underwater environment for 3D path planning, the sand cat group algorithm facing obstacle avoidance ability, slow convergence and easily into local optimal solution, this study puts forward a sand cat group optimization algorithm and levy flight method combining strategy, aims to improve the overall performance of the sand cat group algorithm. By initializing the initial population with the consistency of chaotic mapping, the population diversity is effectively enhanced. In addition, the mutualism mechanism and the adjustment of Levy flight strategy significantly enhance the algorithm’s ability to find the global optimal solution. This improvement not only improves the convergence speed of the algorithm, but also improves the solution accuracy. Through simulation tests in scenarios of static obstacles and dynamic current interference, the improved Sand Cat Swarm Optimization algorithm (LVSCSO) significantly outperforms six types of algorithms including PSO and GA in terms of global convergence: the deviation of the optimal solution is reduced by 21.4%, the stability of the worst solution is improved by 33.7%, and the average solution accuracy is optimized by 19.5%. The results indicate that LVSCSO can effectively address complex underwater path planning tasks (such as seabed exploration) and has potential for engineering deployment.