为缩短船舶航行的时间，避免船舶航行触碰障碍物发生故障，提出改进粒子群优化算法的船舶最优航线导航方法。由于基础粒子群算法寻优时会陷入早熟，基于代价函数选取较差粒子与精英粒子，采用具有动能补偿的更新速度方法处理较差粒子，避免寻优陷入早熟；借鉴最差粒子的失败经验，避免单个粒子在运动时搜索最差解。基于改进粒子群算法构建船舶最优航线导航模型，结合海洋实际环境改进应用方向，通过粒子的适应度函数大小，确定船舶最优航线导航。经过试验验证，该方法计算船舶航行参数较为准确，搜索出的航线用时最短，能够正确规避海洋上的障碍物。
In order to shorten the sailing time of ships and avoid the failure of ships touching obstacles, an improved particle swarm optimization (PSO) algorithm for ship optimal navigation is studied. Because the basic particle swarm optimization algorithm will fall into prematurity, the poor particles and elite particles are selected based on the cost function, and the update speed method with kinetic energy compensation is used to deal with the poor particles, so as to avoid falling into prematurity. Learning from the failure experience of the worst particle, avoid searching the worst solution when a single particle is moving. Based on the improved particle swarm optimization (PSO) algorithm, a ship optimal navigation model was built, and the application direction was improved according to the actual marine environment. The ship optimal navigation was determined by the particle fitness function. The experimental results show that this method is more accurate in calculating ship's sailing parameters, and it can search the shortest route time and correctly avoid the obstacles on the ocean.
2022,44(11): 82-85 收稿日期：2021-12-16
DOI：10.3404/j.issn.1672-7649.2022.11.017
分类号：TP24
基金项目：广西中青年教师基础能力提升项目（2020KY05030）
作者简介：万剑锋(1986-)，男，硕士，讲师，研究方向为信号与信息处理技术及通信技术
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