Trajectory planning for low attitude penetration based on improved ant colony algorithm

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.