It is important to optimize sonobuoys deployment for enhancing cumulative detection probability in aviation ASW. Firstly the target motion model is established and method of calculation cumulative detection probability is interpreted. Then multi-start random search, branch-and-bound based partition and a genetic algorithm are designed validly for sonobuoys deployment, so three corresponding optimization sonobuoys deployment methods are constructed. The simulation results show that the foregoing two methods fit regular sonobuoy array placement in simple environments for their good performance and quick calculation speed, and the genetic algorithm fits irregular sonobuoy group deployment in complex environments, but need long time to calculate.%在航空搜潜中为了提高搜索概率,需要优化声纳浮标群的布放位置.首先建立目标运动模型和累积搜索概率的计算方法,然后采用多点随机搜索、分区&分支界定和遗传算法对规则阵形和不规则阵形的声纳浮标群的布放位置进行优化,相应地建构了三种声纳浮标搜潜优化布放方法.仿真结果表明:在简单环境下搜潜,用前两种方法优化规则浮标阵的阵形参数,其搜索概率高而且运算时间短；在复杂环境下搜潜,利用遗传算法优化不规则浮标阵的布放位置,具有最高的搜索概率但运算时间较长.
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