With the application scenarios of low-altitude economy evolving towards scale and diversification, multi-UAV cooperative path planning technology has become a key research direction to break through the performance limitations of single UAVs and enhance the overall system efficiency. This study proposes an Improved Harris Hawks Optimization (IHHO)-based algorithm for multi-UAV path planning to address efficiency and convergence challenges. A comprehensive path planning model is established by integrating critical path cost metrics and UAV kinematic constraints, ensuring a robust formulation of cost functions and operational limitations. To address the lack of diversity and randomness in the population, a Circle chaotic map is introduced in the population initialization stag the algorithm further incorporates a nonlinear escape energy update mechanism to mitigate premature convergence, effectively balancing global exploration and local exploitation capabilities. Additionally, the predation strategy from the Whale Optimization Algorithm (WOA)is embedded into the soft siege phase to reinforce local optima avoidance. Experimental results show IHHO significantly outperforms conventional HHO in overall performance, with improved path planning, higher task efficiency, and faster convergence. Stability analysis confirms stronger iterative consistency and robustness, highlighting its reliability in complex scenarios. IHHO’s structural optimizations deliver key breakthroughs in performance. These advancements provide an efficient solution for the engineering implementation of multi-UAV cooperative path planning systems.