地震场景下无人机群路径规划与任务分配均衡联合优化

Joint optimization of UAV swarm path planning and task allocation balance in earthquake scenarios

无人机(UAV)群路径规划和任务分配是UAV群救援应用的核心,然而传统方法分开求解路径规划与任务分配,导致资源分配不均。为了解决上述问题,结合UAV群的物理属性与应用环境因素,改进蚁群算法(ACO),提出联合并行蚁群(JPACO)模型。首先,借助分级信息素增强系数机制更新信息素,以提高JPACO任务分配均衡性和能耗均衡性;其次,设计路径平衡因子和动态概率转移因子优化蚁群模型易陷入局部收敛的情况,从而提高JPACO的全局搜索能力;最后,引入集群并行处理机制,以降低JPACO运算耗时。将JPACO与自适应动态蚁群算法(ADACO)、扫描动态蚁群算法(SMACO)、贪婪策略蚁群算法(GSACO)和交叉蚁群算法(IACO)在公开数据集CVRPLIB上对比最优路径、任务分配均衡、能耗均衡和运算耗时。实验结果表明:与IACO和ADACO相比,JPACO处理小规模运算的最优路径平均值分别降低7.4%和16.3%;处理大规模运算的求解耗时与GSACO、ADACO相比降低8.2%和22.1%。以上结果验证了JPACO在处理小规模运算时能够改善最优路径,处理大规模运算时任务分配均衡、能耗均衡和运算耗时明显优于对比算法。

Unmanned Aerial Vehicle(UAV)swarm path planning and task allocation are the cores of UAV swarm rescue applications.However,traditional methods solve path planning and task allocation separately,resulting in uneven resource allocation.In order to solve the above problem,combined with the physical attributes and application environmental factors of UAV swarm,the Ant Colony Optimization(ACO)was improved,and a Joint Parallel ACO(JPACO)was proposed.Firstly,the pheromone was updated by the hierarchical pheromone enhancement coefficient mechanism to improve the performance of JPACO task allocation balance and energy consumption balance.Secondly,the path balance factor and dynamic probability transfer factor were designed to optimize the ant colony model,which is easy to fall into local convergence,so as to improve the global search capability of JPACO.Finally,the cluster parallel processing mechanism was introduced to reduce the time consumption of JPACO operation.JPACO was compared with Adaptive Dynamic ACO(ADACO),Scanning Motion ACO(SMACO),Greedy Strategy ACO(GSACO)and Intersecting ACO(IACO)in terms of optimal path,task allocation balance,energy consumption balance and operation time on the open dataset CVRPLIB.Experimental results show that the average value of the optimal paths of JPACO is 7.4%and 16.3%lower than of IACO and ADACO respectively in processing small-scale operations.Compared with GSACO and ADACO,JPACO has the solution time reduced by 8.2%and 22.1%in large-scale operations.It is verified that JPACO can improve the optimal path when dealing with small-scale operations,and is obviously superior to the comparison algorithms in terms of task allocation balance,energy consumption balance,and operation time consumption when processing large-scale operations.