Optimization of LSTM Ship Trajectory Prediction Based on Hybrid Genetic Algorithm

Accurate prediction of the movement trajectory of sea surface targets holds significant importance in achieving an advantageous position in the sea battle field. This prediction plays a crucial role in ensuring security defense and confrontation, and is essential for effective deployment of military strategy. Accurately predicting the trajectory of sea surface targets using AIS (Automatic Identification System) information is crucial for security defense and confrontation, and holds significant importance for military strategy deployment. In response to the problem of insufficient accuracy in ship trajectory prediction, this study proposes a hybrid genetic algorithm to optimize the Long Short-Term Memory (LSTM) algorithm. The HGA-LSTM algorithm is proposed for ship trajectory prediction. It can converge faster and obtain better parameter solutions, thereby improving the effectiveness of ship trajectory prediction. Compared to traditional LSTM and GA-LSTM algorithms, experimental results demonstrate that this algorithm outperforms them in both single-step and multi-step prediction.