UAV Flight Path Planning Based on HPSOCAOA Optimization Algorithm

To address the issues with the Crocodile Ambush Optimization Algorithm (CAOA) in UAV trajectory planning—such as its tendency to get stuck in local optima, the difficulty in balancing global search and local exploration, and low convergence accuracy—this study proposes a three-dimensional trajectory planning method based on the Hybrid Particle Swarm and Crocodile Ambush Optimization Algorithm (HPSOCAOA). First, a collaborative search structure combining the Particle Swarm Optimization (PSO) algorithm and the Crocodile Ambush Optimization Algorithm (CAOA) is established; second, an adaptive energy consumption coefficient is designed to address the issues of premature individual elimination in the early stages and insufficient convergence momentum in the later stages, thereby further balancing global exploration and local exploitation; finally, crossover learning is introduced. Using a cross-group replacement mechanism for superior individuals, PSO’s fine-tuning identifies high-quality individuals, which are then substituted for lower-quality individuals in CAOA. This resolves the problems of redundant low-quality individuals within the population and low search efficiency, and enhances overall optimization performance. Standard test functions demonstrate that HPSOCAOA outperforms the comparison algorithms in terms of optimization accuracy and stability. In simulation experiments for path planning in complex 3D mountainous environments, HPSOCAOA was compared with classical intelligent algorithms, verifying its superiority and practicality in complex 3D scenarios.