对无人机（UAV）在动态战场环境中的协同搜索问题进行研究，在考虑目标存在概率、价值收益、UAV斥力代价及任务执行代价的情形下，建立了多UAV协同搜索的滚动优化模型。为了提高模型求解的效率，提出一种改进种群增量学习算法（PBIL）对该问题进行求解，采用混合编码的方法构造种群，同时采用了自适应的更新率，并利用自适应交叉和变异方式,将该算法应用于动态目标的搜索问题，仿真结果表明该方法能有效地搜索到战场目标，提高了搜索效率。

The paper focuses on the problem of cooperative target search for multiple unmanned aerial vehicles (UAV) in dynamic battle environment.The model of cooperative search was established with consideration of target existence probabilitytarget valuerepulsive force and task execution cost.In order to enhance the solution efficiency of the modelan improved populationbased incremental learning (PBIL) was proposed.Hybrid encoding mechanism was adopted in the proposed algorithm for generating the populationand an adaptive updating rate was used.The crossover and mutation operator could also change adaptively according to the evolution condition.The improved PBIL was used to the dynamic target searchand the experimental results show that the algorithm can improve the search efficiency and can search more targets than before.