A Hybrid Optimization Algorithm for Enhanced Path Planning in Dynamic Multi-UAV Environments

Multi-UAV path planning in dynamic and complex environments is a challenging constrained optimization problem because it must simultaneously consider path efficiency, obstacle avoidance, altitude feasibility, flight smoothness, and inter-UAV path diversity. Existing methods often struggle to maintain search diversity, balance exploration and exploitation, and avoid premature convergence in high-dimensional search spaces. To address this issue, this paper proposes a Q-learning-guided Harris Hawk Optimization-Genetic Algorithm (QHHO_GA), which integrates Genetic Algorithm (GA), Harris Hawk Optimization (HHO), Q-learning, prioritized experience replay, entropy-based state partitioning, and a Rapidly exploring Random Tree (RRT)-based stagnation adjustment mechanism. In the proposed framework, GA enhances population quality and diversity, HHO performs the core search, Q-learning adaptively guides HHO behaviors, and stagnation monitoring with RRT-based stagnation adjustment improves the ability to escape locally trapped regions. Experimental results on the CEC2017 benchmark suite and a multi-UAV path planning task demonstrate the effectiveness of the proposed method. On the CEC2017 benchmark, QHHO_GA ranks among the top two on 18 out of 30 test functions and achieves the best overall ranking among the compared algorithms. In the UAV path planning experiments, it achieves an average ranking of 3.44 and also achieves the best overall rank among all compared methods. These results indicate that QHHO_GA is a robust and competitive method for high-dimensional constrained optimization, and is particularly effective for complex multi-UAV path planning tasks.