快速且准确地制定舰船避碰行动方案对保证舰船安全及作战能力具有重要意义。本文将智能算法应用到舰船避碰行动策略制定中，从环境适应性、复杂场景处理能力以及实时性等3个方面对遗传算法、粒子群优化算法以及神经网络算法进行比较，以避碰成功率、避碰时间、收敛速度为指标，对多种智能算法进行仿真分析，结果表明GA-PSO算法优于其他算法，提出了基于GA-PSO的舰船避碰行动策略。
It is of great significance to formulate the collision avoidance action plan quickly and accurately to ensure the safety and combat capability of the ship. The intelligent algorithm is applied to the formulation of ship collision avoidance action strategy, and the genetic algorithm, particle swarm optimization algorithm and neural network algorithm are compared from three aspects, including environmental adaptability, complex scene processing ability and real-time performance. The success rate of collision avoidance, collision avoidance time and convergence speed are taken as indicators to conduct simulation analysis on multiple intelligent algorithms. The results show that GA-PSO algorithm is superior to other algorithms. Finally, a collision avoidance strategy based on GA-PSO is proposed.
2025,47(6): 149-153 收稿日期：2024-9-18
DOI：10.3404/j.issn.1672-7649.2025.06.025
分类号：U675.79.
基金项目：山东省高等学校科技计划项目（J17KB153）
作者简介：苏娜（1980 – ），女，硕士，副教授，研究方向为计算机技术
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