基于差分进化粒子群混合算法的多无人机协同区域搜索策略

Multi-UAV Collaborative Area Search Strategy Based on Differential Evolutionary Particle Swarm Mixing Algorithm

为提高无人机群在未知环境中的区域搜索效率,提出一种多无人机协同区域搜索策略。首先,根据区域搜索任务需求,建立包含区域覆盖率、区域不确定度、目标存在概率三种属性的区域信息地图;其次,以最大化搜索效率、同时最小化无人机搜索过程中的能耗为目标,建立无人机区域搜索滚动时域优化目标函数,指导无人机在线决策搜索路线;然后针对传统群智能优化算法易陷入局部最优的缺陷,设计差分进化粒子群混合算法在线求解该多目标优化问题,提高算法的寻优性能,从而提高无人机的搜索效率。最后,通过数值仿真实验,对所提算法进行验证,仿真结果表明,文中设计的基于差分进化粒子群混合算法的多无人机协同区域搜索策略与传统的群智能优化算法相比具有更高的区域搜索效率。

To improve the area search efficiency of UAV swarms in unknown environments,a multi-UAV cooperative area search strategy is proposed.Firstly,according to the demand of area search task,an area information map containing three attributes of area coverage,area uncertainty and target existence probability is established;secondly,with the goal of maximizing search efficiency and minimizing energy consumption during UAV search,a rolling time-domain optimization objective function for UAV area search is established to guide UAVs to make online decisions on search routes;then,for the traditional swarm intelligence optimization algorithm that tends to Then,to address the shortcomings of the traditional swarm intelligence optimization algorithm,which is prone to fall into the local optimum,we design a hybrid differential evolutionary particle swarm algorithm to solve the multi-objective optimization problem online,improve the optimization performance of the algorithm,and thus improve the search efficiency of the UAV.Finally,the proposed algorithm is verified through numerical simulation experiments,and the simulation results show that the multi-UAV cooperative area search strategy based on differential evolutionary particle swarm hybrid algorithm designed in this paper has higher area search efficiency compared with the traditional swarm intelligence optimization algorithm.