在针对多无人机（multi-UAV）协同运动目标搜索问题的研究中，提出了一种融入信息图模型的优化哈里斯鹰优化算法的协同搜索决策方法。构建了基于高斯分布的目标概率信息图模型；通过建立确定度信息图模型，提升了环境中目标存在概率的确定性；引入吸引与排斥机制的数字信息素图，指导无人机向尚未探索区域移动，有效降低重复搜寻行为，提升协同搜索的效率。针对哈里斯鹰优化算法易陷入局部最优的问题，提出一种非线性能量因子更新策略，整合最优个体位置，提出新的位置更新公式。最后，针对轨迹随机变化的动态目标，设计了可回访数字信息图及自适应目标搜索增益函数，增强无人机针对动态目标的捕获能力。仿真结果验证了改进哈里斯鹰优化算法在多无人机协同搜索动态目标问题上的有效性。

In the study of cooperative search for moving targets by multi-Unmanned Aerial Vehicle （multi-UAV）， this paper proposes a cooperative search decision-making method that integrates an information graph model with the optimized Harris hawk optimization algorithm. A target probability information graph model based on Gaussian distribution is constructed， which enhances the certainty of target existence probability in the environment through the establishment of a certainty information graph model. Furthermore， a digital information pheromone graph with attraction and repulsion mechanisms is used to guide UAVs to move towards unexplored areas， effectively reducing repetitive search behaviors and enhancing the efficiency of cooperative search. To address the issue of Harris hawk optimization algorithm being prone to local optima， a non-linear energy factor updating strategy is proposed， integrating the optimal individual positions and presenting a new position update formula. Finally， to search for the targets with randomly changing trajectories， a revisitable digital information graph and an adaptive target search gain function are designed to enhance the capability of UAVs to capture moving targets. Simulation results verify the effectiveness of the improved Harris hawk optimization algorithm in cooperative search for moving targets by multiple UAVs.