UUV集群任务分配问题与狼群攻击围捕目标的问题十分相似，本文尝试将狼群行为理论和灰狼算法引入到UUV集群任务分配问题中。针对传统灰狼算法未考虑全局与局部搜索的协调配合以及未考虑位置更新的权重问题进行改进，本文提出改进收敛因子以余弦规律减小和引入动态权重的改进灰狼算法。为了验证方法的可行性和优越性，进行侦察和攻击目标任务分配仿真，并针对同一场景分别采用灰狼算法（GWO)、遗传算法(GA）以及改进灰狼算法（IGWO）进行仿真。结果表明，IGWO算法比GA算法和GWO能够更好对UUV集群侦察目标任务分配的问题进行求解，并能够减小资源浪费，提高工作效率。
The problem of UUV swarm task assignment is very similar to the problem of wolves attacking and rounding up targets. This paper attempts to introduce the wolf pack behavior theory and gray wolf algorithm into the UUV swarm task assignment problem for the first time. In order to improve the traditional gray wolf algorithm which does not consider the coordination of global and local search and does not consider the weight of position update, this paper proposes an improved gray wolf algorithm that reduces the convergence factor by cosine law and introduces dynamic weights. Feasibility and superiority, carry out reconnaissance and attack target task assignment simulation, and use gray wolf algorithm (GWO), genetic algorithm (GA) and improved gray wolf algorithm (IGWO) to simulate the same scene, result shows that IGWO algorithm is better than GA. The algorithm and GWO can better solve the problem of UUV cluster reconnaissance target task assignment, also reduce the waste of resources and improve work efficiency.
2022,44(22): 69-75 收稿日期：2022-02-09
DOI：10.3404/j.issn.1672-7649.2022.22.013
分类号：TJ630；TJ67
作者简介：杨芳(1995-),女,硕士,助理工程师,研究方向为水下无人系统控制
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