基于改进黑猩猩算法的异构多无人机协同任务分配  

Heterogeneous multi-UAV collaborative task allocation based on modified chimp optimization algorithm

作　　者：许子俍 胡涛[1] 王书 刘凯越 秦宜辉 张申建 何润泽 邓文杰 XU Ziliang;HU Tao;WANG Shu;LIU Kaiyue;QIN Yihui;ZHANG Shenjian;HE Runze;DENG Wenjie(University of Information Engineering,Zhengzhou 450001;Unit 66389 of PLA,Zhengzhou 450001,China)

机构地区：[1]信息工程大学,河南郑州450001 [2]中国人民解放军66389部队,河南郑州450001

出　　处：《指挥控制与仿真》2024年第5期13-20,共8页Command Control & Simulation

摘　　要：针对异构多无人机对地面多类型固定目标的任务分配问题,提出一种基于协同多任务分配模型和改进黑猩猩算法的任务分配方法。通过对四元组内元素的设定完成模型构建,建立总收益最高、威胁代价最低、总航程最短的多目标评价函数。使用改进黑猩猩算法完成模型求解,引入混沌反向学习策略提升初始种群分布的多样性,在迭代阶段使用抢食行为动态调整策略,提升了跳出局部最优解的能力。实验表明:相比原算法,改进黑猩猩算法所得任务分配方案在保持较高任务收益和较低威胁代价的同时,在两种规模场景下的平均总航程分别降低了22.6%和21.1%。Aiming at the task assignment problem of heterogeneous UAV performing multi-type fixed tasks on the ground,a task allocation method based on Cooperative Multiple Task Allocation Problem(CMTAP)and Modified Chimp Optimization Algorithm(MChOA)is proposed.Firstly,the CMTAP model is established,and the combat scene is depicted by setting the elements in the quad,and a multi-objective evaluation function with the highest total return,the lowest threat cost,and the shortest total flight range is established.Secondly,the MChOA algorithm is used to complete the model solving:the chaotic reverse learning strategy is adopted to improve the diversity of the intial population distribution.In the iteration stage,the global and local search capabilities of the algorithm are better balanced by adopting the dynamic adjustment strategy of preemptive behavior.Comparative experiments show that compared with the original algorithm,the resulting task allocation scheme reduces the average total voyage in the two scale scenarios by 22.6%and 21.1%,respectively,while maintaining a higher task gain and a lower threat cost.

关 键 词：无人机 任务分配 协同多任务分配模型 改进黑猩猩算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249