限定搜索区域的分层遗传算法无人机路径规划

Restricted Searching Area Hierarchical Genetic Algorithm for UAV Path Planning

为克服简单遗传算法易陷入局部最优解的缺点,减小路径搜索范围,提出了限定搜索区域的分层遗传算法无人机路径规划方法,该方法将分层遗传算法引入无人机路径规划的优化搜索问题中,将路径节点的二维坐标作为基因进行编码,根据威胁的分布情况缩小路径规划算法的搜索范围,使子种群可以获得包含不同优良模式的新个体,为子种群提供更加平等的竞争生存机会,使优化搜索有较为明确的搜索方向。仿真结果表明:与基于分层遗传算法的路径规划方法相比,该方法提高了路径寻优算法的性能,减少了绕行路径的出现几率,缩短了最优路径的长度。

In order to overcome the shortcoming of simple genetic algorithm（SGA） that it is to fall into the local optimal solution and reduce the path search range,a restricted-searching-area HGA path planning approach was proposed.In this approach,the Hierarchy Genetic Algorithm（HGA） was introduced into the optimization problem of the UAV path planning,2D coordinates of path nodes were coded as genes,searching area of path planning algorithm was reduced according to the distribution of threats,subpopulation could obtain individuals of different optimal patterns,and provided the subpopulation more equal an opportunity to compete with each to survival,thus the searching process became more directional.The simulation results showed that,comparing with HGA based path planning approaches,the proposed approach enhanced the performance optimal path planning and reduced the incidence of by-pass paths,thus the length of optimal path was shortened.