自航水雷突防航路优化仿真

Optimization and Simulation of Self-Propelled Mine Penetration Route

研究自航水雷航路优化问题,自航水雷需要自动隐蔽航行,突破敌方防线进入预定布雷阵地。在突防过程中,航路的选择直接关系到水雷能否高效安全地到达预定阵位。传统的利用人工计算和经验进行航路规划的方法,难以同时满足自航水雷突防航路最短、转向点最少和转向角平滑的要求。为提高自航水雷的航路规划效率,利用熵的自适应遗传算法解决自航水雷航路规划问题,并给出了算法编码方法、算子设计和终止条件。仿真结果表明,改进算法能够有效地提高航路规划的计算速度和保证航路规划的质量。

Self - propelled mines need to arrive at predetermined position by penetrating the enemy defense auto- matically and quietly. In the course, path planning may determine if the mine can go to the position. The traditional method of path planning relies on artificial calculation and experience. The method can not meet the requirements of shortest range, least turning points and smoothest turning angle at same time. In order to improve the efficiency of self - propelled mine path planning, this paper introduced the relevant concepts of genetic algorithm and carried out research on the information entropy genetic algorithm to plan the path of self - propelled mine. Then, encoding meth- ods were offered, as well as genetic operator and termination conditions. The results of simulation show that this im- proved method is feasible and efficient for improving the computing performance and route quality of route planning of self - propelled mine.