摘要
针对无人机航路规划问题,研究了一种基于元胞蚂蚁算法的无人机航路规划方法。元胞蚂蚁算法对基本蚁群算法进行了系列改进,并将元胞邻居演化和改进后的蚂蚁寻优相结合,有效地克服了基本蚁群算法的收敛速度慢、易于过早陷入局部最优的缺点,提高了算法的运算精度,从而为解决复杂战场环境下无人机航路规划这一多约束多目标优化问题提供了一条可行的途径。
It was proposed to use cellular ant algorithm in path planning of Unmanned Aerial Vehicle （UAV）. A series of improvements were made in cellular ant algorithm on the basis of the basic ant colony algorithm. Then the improved ant colony algorithm was used together with evolutionary rule of cellular in cellular space. The simulation results showed that the cellular ant algorithm could help the solutions to escape from their local optimum and could find a better path at higher convergence speed and with a higher precision. Therefore, the cellular ant algorithm is an effective method for such kind of multi-objective optimization problems with multiple constraints as UAV path planning under complex environment.
出处
《电光与控制》
北大核心
2011年第2期8-12,17,共6页
Electronics Optics & Control
基金
航空电子系统综合技术国防科技重点实验室和航空科学基金资助项目(20085584010)
