多子群社会群体优化算法的无人机航迹规划

Unmanned Air Vehicle Route Planning Based on Multiple Subgroups Social Group Optimizing Algorithm

为了规划出一条航程短且安全性高的航迹,提出了多子群社会群体优化算法的航迹规划方法。建立了威胁模型和航迹规划模型,将航迹规划问题转化为带约束优化问题;在社会群体算法基础上,将个体分为多个子群,通过制定多渠道信息获取方法和多样式学习方式,改进了算法的提高阶段和获得阶段;通过子群重组实现了子群间信息交流,达到提高算法种群多样性的目的。使用基本测试函数对算法性能进行测试可以看出,多子群算法提高了算法的种群多样性和寻优能力。同时使用多子群算法和传统社会群体算法对无人机航迹进行规划,多子群算法规划航迹的目标函数比传统算法减少了6.65%,说明多子群算法规划的航迹在航程和安全性综合评价上优于传统算法。

In order to plan a short and safe route,route planning method based on multiple subgroups social group optimizing algorithm is proposed. Threaten model and route planning model are built,so that route planning problem is transferred to optimizing problem with constraint. Based on social group optimizing algorithm,individuals are divided to several subgroups. By making multiple channel information acquirement method and multiple mode learning method,elevated stage and acquisition stage of the algorithm are improved. Information communication among subgroups overcomes by subgroups re-grouping,the goal of improving population diversity is achieved. Basic testing function is used to test property of the algorithms,it can be seen that population diversity and optimizing ability of multiple subgroups algorithm are improved compared with traditional algorithm. Multiple subgroups algorithm and traditional algorithm are both used to plan UAV route,and objective function value of route planned by multiple subgroups algorithm decrease by 6.65% compared with route planned by traditional algorithm,which means comprehensive assessment of voyage and safety of route planned by multiple subgroups algorithm is superior to route planned by traditional algorithm.