Aiming at the shortcomings of Harris Hawks Optimization (HHO) algorithm, such as low convergence accuracy, low convergence speed and being easy to fall into local optimum, a Chaotic Elite HHO (CEHHO) algorithm was proposed. Firstly, the elite hierarchy strategy was introduced to make full use of the dominant population to enhance the population diversity and improve the convergence speed and accuracy of the algorithm. Secondly, the Tent chaotic map was used to adjust the key parameters of the algorithm. Thirdly, a nonlinear energy factor adjustment strategy was adopted to balance the exploitation and exploration of the algorithm. Finally, the Gaussian random walk strategy was used to disturb the optimal individual, and when the algorithm was stagnant, the random walk strategy was used to make the algorithm jump out of the local optimum effectively. Through the simulation experiments of 20 benchmark functions in different dimensions, the optimization ability of the algorithm was evaluated. Experimental results show that the improved algorithm outperforms Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm, and the performance of this algorithm is significantly better than that of original HHO algorithm, which prove the effectiveness of the improved algorithm.

Focusing on the issues of large alculation amount and difficult convergence of Unmanned Aerial Vehicle (UAV) path planning, a path planning method based on Chaos Sparrow Search Algorithm (CSSA) was proposed. Firstly, a two-dimensional task space model and a path cost model were established, and the path planning problem was transformed into a multi-dimensional function optimization problem. Secondly, the cubic mapping was used to initialize the population, and the Opposition-Based Learning (OBL) strategy was used to introduce elite particles, so as to enhance the diversity of the population and expand the scope of the search area. Then, the Sine Cosine Algorithm (SCA) was introduced, and the linearly decreasing strategy was adopted to balance the exploitation and exploration abilities of the algorithm. When the algorithm fell into stagnation, the Gaussian walk strategy was adopted to make the algorithm jump out of the local optimum. Finally, the performance of the proposed improved algorithm was verified on 15 benchmark test functions and applied to solve the path planning problem. Simulation results show that CSSA has better optimization performance than Particle Swarm Optimization (PSO) algorithm, Beetle Swarm Optimization (BSO) algorithm, Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO) algorithm and Sparrow Search Algorithm (SSA), and can quickly obtain a safe and feasible path with optimal cost and satisfying constraints, which proves the effectiveness of the proposed method.