混沌多精英鲸鱼优化算法

针对无人机（UAV）的航迹规划问题，提出了一种基于混沌多精英鲸鱼优化算法（CML-WOA）的航迹规划方法。首先，在已知飞行环境下，建立3D飞行空间模型和航迹代价模型。通过引入罚函数，将有约束3D航迹规划问题转化为无约束多维函数优化问题，利用CML-WOA求解模型来获得最优航迹。其次，为克服WOA易陷入局部最优的缺陷，引入立方映射混沌算子改善初始种群，增强种群多样性，并通过自适应框架融入正余弦算法（SCA），利用多精英搜索策略有效地提高了算法开发能力和探索能力。最后，使用贪婪策略保证了收敛效率。通过20个基准函数测试和航迹规划仿真实验对提出的改进WOA进行验证。结果表明:所提算法相对其他算法，寻优性能明显提升，具有较强局部最优规避能力和更高的收敛精度与收敛速度；能够稳定快速地规划出代价最少、满足约束的安全可行的飞行航迹。 

Chaotic multi-leader whale optimization algorithm

Aimed at the path planning problem of Unmanned Aerial Vehicle (UAV), a path planning method based on a Chaotic Multi-Leader Whale Optimization Algorithm (CML-WOA) is proposed. In the known flight environment, 3D model of flight area and a flight path cost model are established. By introducing penalty functions, the constrained 3D path planning problem is transformed into an unconstrained multi-dimensional function optimization problem, which is solved using CML-WOA to obtain the optimal flight path. To overcome the defect that the WOA is easy to fall into local optimum, this paper introduces the cubic mapping chaos operator to improve the initial population and enhance the population diversity. And the Sine Cosine Algorithm (SCA) is integrated through an adaptive framework. A multi-leader search strategy is used to effectively improve the algorithm exploitation and exploration capability. Finally, a greedy strategy is used to ensure the convergence efficiency. The proposed improved CML-WOA is tested and validated by 20 benchmark functions test and path planning simulation experiments. The results show that the algorithm has significantly improved performance compared to other algorithms, with strong local optimal avoidance capability, higher convergence accuracy and convergence speed. Also, it is able to provide stable and fast planning of safe and feasible flight path with minimum cost and satisfying constraints.