Path planning is a key research issue in the field of unmanned aerial vehicle (UAV) applications. In practical applications, multi-objective path planning is usually required for multi-UAVs, so this paper proposes the improved balanced artificial bee colony (IB-ABC) algorithm to optimize multi-objective path planning. The algorithm adopts the ABC algorithm that emphasizes the global search capability, which is based on iterative feedback information. It uses single-element points, multi-element points, and iteration constraints to optimize the strategies of employed bees, follower bees, and scout bees, respectively. In terms of time and priority, simulation experiments prove that the IB-ABC algorithm can balance local and global search capabilities, accelerate the speed of convergence, and realize multi-UAV path planning in complex mountain environments.
