利用水下无人集群执行协同作战任务，是未来海战场的一种新型作战样式，而优化部署技术是集群组网运用效能发挥的关键。针对水下无人集群协同探测需求，本文组合使用和声搜索算法（Harmony Search，HS)和粒子群优化算法（Particle Swarm Optimization，PSO），自动解算集群成员阵位，实现水下无人集群对任务区域的最大化有效探测覆盖。建立多约束条件下的有效探测覆盖率综合评价方法，对优化部署效果进行分析评估。仿真结果表明：HS-PSO算法具有相对稳定的全局寻优能力，与人工部署方案相比，经HS-PSO算法优化集群协同探测效能得到明显提升，可为水下无人集群智能协同任务规划提供有力支撑。
Using underwater unmanned swarm to carry out cooperative mission is a new operational style in the future naval battle field. The efficiency of cluster networking mainly depends on optimal deployment techniques. In order to meet cooperative detecting requirements of underwater unmanned swarm, in this paper, the combination of harmony search (HS) and particle swarm optimization (PSO) algorithm has been used to automatically calculate the optimal parameters such as swarm member’s location. It can maximize the effective detection coverage of the task area. The comprehensive evaluation algorithm of effective detection coverage under multiple constraints is established to analyze and evaluate the optimal deployment effect. The simulation result shows that the algorithm has an relatively stable global optimization ability, and the efficiency of swarm collaborative detection optimized by HS-PSO algorithm is significantly improved, which can provide strong support for intelligent cooperative task planning of underwater unmanned swarm.
2022,44(24): 50-55 收稿日期：2022-07-25
DOI：10.3404/j.issn.1672-7649.2022.24.011
分类号：E317
作者简介：崔化超(1986-),男,高级工程师,研究方向水下情报处理与作战辅助决策技术
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