Multi-constraint weighted-cost optimization for UAV path planning using an improved harris hawks optimization

To mitigate the flight safety risks and ensure the feasibility of the trajectories for Unmanned Aerial Vehicles (UAVs) in complex and hazardous environments, this paper proposes a multi-constraint weighted-cost optimization method using an Improved Harris Hawks Optimization (IHHO) algorithm. First, a weighted-cost function is formulated by aggregating path length, flight altitude, flight attitude, threat distance, and the metric of obstacle avoidance response time into a single objective, subject to the kinematic and safety constraints of the UAV. Second, to enhance the optimization performance in path planning, three improvement strategies are introduced to the standard HHO: (1) position updates inspired by natural exploration and trap avoidance mechanisms to augment population diversity and prevent local optima stagnation; (2) a cosine-exponential based non-linear energy decay strategy to replace the original linear decay, enhancing the global search capability; and (3) modulation of the number of Lévy flight iterations to refine local solution accuracy. Finally, experiments on a simulated map demonstrate that the proposed IHHO algorithm efficiently generates optimal trajectories that minimize the weighted cost while harmonizing flight efficiency, path smoothness, and obstacle avoidance safety. This provides a reliable methodological foundation for UAV mission execution in complex environments.