Trajectory planning for low attitude penetration based on improved ant colony algorithm
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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.
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Key words:
- algorithms /
- trajectories /
- planning /
- low attitude penetration /
- ant colony algorithm
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