To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast. To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration, a novel route planning method was proposed. First and foremost, a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA), an efficient global optimization algorithm. A dynamic route representation form was also adopted to improve the flight route accuracy. was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation. Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following, terrain avoidance, threat avoidance (TF / TA2) and lower route costs than other existing algorithms. In addition, feasible flight routes can be acquired within 2 s, and the convergence rate of the whole evolutionar y process is very fast.