Trajectory planning for low attitude penetration based on improved ant colony algorithm
-
摘要: 为保证低空突防的成功率,在航迹规划时必须设计出以最小的被发现概率及可接受的航程为目标的航迹.蚁群算法ACA(Ant Colony Algorithm)作为一种新型的模拟进化算法,适合用于航迹规划中最优航迹的搜索,但是算法存在搜索时间长、收敛速度慢、易陷于局部最优解的缺点,为了克服算法自身不足,提高算法性能,引入了遗传算法中变异操作和挥发系数的自适应调节,从而形成改进蚁群算法,最后结合建立的航迹规划性能指标,利用等概率寻优、原有蚁群算法和改进蚁群算法3种方法分别进行航迹规划,并通过比较和分析结果的时间花费和航路代价,验证了改进蚁群算法的有效性.Abstract: To ensure the mission success rate for low attitude penetration, a trajectory with high survivability and acceptable path length must be planned. As a kind of new emulated evolutional algorithm, ant colony algorithm (ACA) is fit for searching the best way in trajectory planning. The algorithm has several shortages including long searching time, slow convergence rate and limiting to local optimal solution easily. In order to overcome these shortcomings and improve its performance, the improved ant colony algorithm was established, and it introduces the mutation in genetic algorithms (GA) and the adaptive adjustment of the volatilization coefficient. With the establishment of the performance index, the results derived from the equiprobable optimization, the original method and the improved one were compared and analyzed in the example. Base on the comparison of the time expenditure and the performance of the flight paths, the effectiveness of the improved ant colony algorithm was proved.
-
Key words:
- algorithms /
- trajectories /
- planning /
- low attitude penetration /
- ant colony algorithm
-
[1] 闵昌万,袁建平.军用飞行器航迹规划综述[J].飞行力学,1998,16(4):14~19 Min Changwan, Yuan Jianping. Introduction of military aircraft route planning . Flight Dynamics,1998,16(4):14~19(in Chinese) [2] Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies . Proceedings of First European Conference on Artificial Life . Paris:France Elsevier Publishing,1991.134~142 [3] Weimann A, Scheibe D, Schroder P. Terrain masking and threat avoidance using land mass data . Proceedings of IEEE National Aerospace and Electronic Conference . Munich:IEEE, 1988.540~545 [4] 李 栋,曹义华,冯 婷.基于地形特征的简易地形模拟算法[J].航空计算技术,2005,2(35):32~35 Li Dong, Cao Yihua, Feng Ting, Simplified simulation algroithm based on terrain feature[J]. Aeronautical Computer Technique, 2005,2(35):32~35(in Chinese) [5] Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents[J]. IEEE Transaction on SMC,1996,26(1):28~41 [6] 彭斯俊,黄樟灿,刘道海,等. 基于蚂蚁系统的T SP 问题的新算法[J].武汉汽车工业大学学报,1998,20(5):88~92 Peng Sijun, Huang Zhangcan, Liu Daohai, %et al%. Ant colony system:a new algorithm for TSP[J]. Journal of Wu Han Automotive Polytechnic University,1998,20(5):88~92(in Chinese) [7] 白俊强,柳长安. 基于蚁群算法的无人机航路规划[J]. 飞行力学,2005,23(2):35~38 Bai Junqiang, Liu Changan. Path planning based on the ant algorithm for a reconnaissance UAV[J]. Flight Dynamics,2005,23(2):35~38(in Chinese) [8] 王 颖,谢剑英.一种自适应蚁群算法及其仿真研究[J].系统仿真学报,2001,1(14):31~33 Wang Ying, Xie Jianying. An adaptive ant colony optimization algorithm and simulation[J]. Journal of System Simulation,2001,1(14):31~33(in Chinese) -
点击查看大图
计量
- 文章访问数: 4988
- HTML全文浏览量: 198
- PDF下载量: 902
- 被引次数: 0
下载:
