To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and Support Vector Regression (DLGWO-SVR), Grey Wolf Optimized Support Vector Regression (GWO-SVR), and Differential Evolution Grey Wolf Optimized Support Vector Regression (DEGWO-SVR) model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on DLGWO-SVR has higher prediction accuracy and meets the requirements of ship track prediction. The results of ship track prediction can not only improve the efficiency of marine traffic management but also prevent the occurrence of traffic accidents and maintain marine safety.

中文翻译：

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基于DLGWO-SVR的船舶航迹预测

为了提高船舶轨迹预测的准确性，改进的灰狼优化器（GWO）和支持向量回归（SVR）模型被结合用于船舶轨迹预测。维度学习的狩猎策略被用来优化GWO的移动搜索过程，在保持种群多样性的同时平衡探索和开发。选择和更新程序使 GWO 不会陷入局部最优解。将修改后的 GWO 得到的最优参数代入 SVR 模型来预测船舶轨迹。进行了维度学习灰狼优化和支持向量回归（DLGWO-SVR）、灰狼优化支持向量回归（GWO-SVR）和微分进化灰狼优化支持向量回归（DEGWO-SVR）模型轨迹预测模拟。结果对比表明，基于DLGWO-SVR的轨迹预测模型具有更高的预测精度，满足船舶轨迹预测的要求。船舶航迹预测结果不仅可以提高海上交通管理的效率，而且可以预防交通事故的发生，维护海上安全。