针对复杂水下环境中的水下无人航行器（Unmanned Underwater Vehicle，UUV）集群航路规划问题，本文提出一种动态多种群灰狼优化算法（Dynamic Multi -Population -Grey Wolf Optimizer，DyP-GWO）。DyP-GWO引入动态聚类机制，将灰狼种群划分为多个子群体独立进化，增加搜索空间的多样性，避免算法陷入早熟收敛。采用收敛因子分阶段递减策略，提升全局和局部搜索能力。设计动态位置更新机制，根据个体间距及子群体间距自适应调整更新策略，增强搜索的灵活性，避免固定更新策略带来的局部最优问题。仿真结果表明，与传统GWO算法相比，其规划的航行时间减少8.1%，航行路程减少11.9%，显著提升了优化性能，为UUV集群航路规划提供了高效可靠的方案。
To address the path planning problem for UUV swarms in complex underwater environments, this paper proposes a dynamic multi-population grey wolf optimizer (DyP-GWO). The algorithm introduces a dynamic clustering mechanism to divide the grey wolf population into multiple sub-populations that evolve independently, thereby increasing search space diversity and avoiding premature convergence. It employs a phased decreasing strategy for the convergence factor to enhance both global exploration and local exploitation capabilities. Furthermore, a dynamic position update mechanism, adaptive to the distances between individuals and sub-populations, is designed to increase search flexibility and mitigate local optima traps associated with fixed update strategies. Simulation results demonstrate that, compared with the traditional grey wolf optimizer (GWO), DyP-GWO achieves an 8.1% reduction in voyage time and an 11.9% reduction in travel distance, signifying significantly improved optimization performance. The proposed method provides an efficient and reliable solution for UUV swarm path planning.
2026,48(6): 174-180 收稿日期：2025-11-28
DOI：10.3404/j.issn.1672-7649.2026.06.023
分类号：U674.9
基金项目：国家自然科学基金资助项目（52171297）
作者简介：尹洪亮(1984-),男,研究员,研究方向为潜艇总体、无人系统技术
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