Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line

Purpose Multi-unmanned aerial vehicle (UAV) missions aim to optimize the execution of multiple missions using limited resources, making it possible to balance the objectives of each mission while minimizing the time to completion. Design/methodology/approach An algorithm combining cluster analysis and differential evolution particle swarm optimization (DE-PSO) is proposed to solve this problem. Findings The investigative study is based on the homogenization of multi-UAV missions in multi-objective task distribution to reduce the total elapsed time. Practical implications This method effectively reduces task time and provides a solution for multi-UAV operations in transmission line cooperation. Originality/value A novel heuristic algorithm is proposed, and the algorithm fully considers the clustering characteristics under multi-region and the positional relationship characteristics of scene target distribution. It also fully considers the physical characteristics of airport location and UAV power to uniformly optimize the time.