Abstract
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.
Highlights
To introduce unmanned vessels into commercial shipping lanes, an effective collision avoidance system must be built to ensure the level of safety required for unmanned vessel autonomy
We use a Dimensional Learning Hunting (DLH) strategy to improve Grey Wolf Optimizer (GWO) for ship track prediction experiments. e improved optimization model can well balance the exploration and exploitation capabilities of GWO while maintaining the diversity of the population to avoid GWO falling into the local optimal solution, improving the accuracy of trajectory prediction
By conducting prediction experiments on ship trajectories with different MMSI and different lengths, it is not difficult to see that the prediction accuracy of the DLGWO-Support Vector Regression (SVR) model meets the requirements of marine ship trajectories prediction and improves the prediction accuracy of ship trajectories
Summary
To introduce unmanned vessels into commercial shipping lanes, an effective collision avoidance system must be built to ensure the level of safety required for unmanned vessel autonomy. Long et al [23] proposed Random Opposition-Based Learning Grey Wolf Optimizer (ROLGWO) by modifying parameter C, which increased the searching ability of the algorithm. B. Singh [24] proposed an improved Grey Wolf Optimization algorithm to solve the economic dynamical load scheduling problem, which can both increase the global search and local search simultaneously. Meng et al [31] proposed a hybrid Crisscross Search-Based Grey Wolf Optimizer (CS-GWO) algorithm, which used two crossover operators to improve the global search ability of α, β and δ wolves while maintaining the population diversity, but the algorithm convergence occurred too soon. To solve the problem of poor population diversity and slow convergence rate of GWO, a hybrid Grey Wolf Optimizer based on Elite Opposition (EOGWO) was proposed by introducing the elite opposition-based learning strategy and simplex method into GWO [32].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
