采用航路点进化的空袭目标突防航路预测

空袭目标突防航路预测是进行防空兵力部署方案评估的辅助手段之一。在分析突防航路预测问题的基础上建立了空袭目标突防航路预测模型,并针对传统进化算法将航路作为一个整体后导致先前候选航路中高质量航路点利用效果较差的问题,提出了基于航路点进化的空袭目标突防航路预测算法。首先通过改进传统进化算法框架与评价函数使航路点可被分别进化和评价,然后使用自适应差分进化算法(JADE)实现航路点的进化,最后采用多准则决策方法实现航路点的评价与选择,从而提升了高质量航路点的利用效果。为了测试算法的性能,设置了障碍数目分别为15、30、60、120的4个仿真情景,仿真结果表明算法能够有效实现空袭目标突防航路的预测,并且在障碍数目较多的情景中其性能优于遗传算法。

Path Forecasting for Air Strike Aircraft by Evolving Waypoints

Path forecasting for air strike aircraft is one of the assistant methods in assessing the schemes of the deployment of air defense forces. A model of path forecasting is built based on analyzing the path forecasting problem for air strike aircraft. Then a path forecasting algorithm by evolving waypoints is presented. The methods of path forecasting by using traditional evolutionary algorithms (EAs) can hardly exploit the high quality waypoints in previous candidate paths for further evolution, since they regard all the waypoints of a path as an integrated individual. The proposed algorithm improves the framework of the traditional EAs and evaluation functions to evaluate and evolve waypoints separately. The waypoints are evolved with JADE, a state-of-the-art variant of the differential evolution (DE) algorithm, and evaluated and selected by using a multi-criteria handling method based on the priorities, exploiting high quality waypoints. To test the capabilities of the new algorithm, 4 scenarios with 15, 30, 60 and 120 obstacles are constructed respectively. The simulation results show that the proposed algorithm meets the needs for the path forecasting for air strike aircraft effectively, and its performance is previous to the genetic algorithm-based path forecasting algorithm in the scenarios with lots of obstacles.