Multi-Strategy Improved Snake Optimizer Accelerated CNN-LSTM-Attention-Adaboost for Trajectory Prediction

TL;DR

This paper introduces a hybrid CNN-LSTM-attention-Aboost model optimized with a multi-strategy improved snake-herd algorithm, significantly enhancing 4D trajectory prediction accuracy using real ADS-B data.

Contribution

It presents a novel multi-strategy improved snake-herd optimization integrated with CNN-LSTM-attention-Aboost for trajectory prediction, outperforming traditional optimizers.

Findings

SO-CLA-adaboost outperforms particle swarm, whale, and gray wolf optimizers.

The model improves prediction accuracy by 39.89%.

The approach effectively handles large-scale high-dimensional trajectory data.

Abstract

To address the limitations of medium- and long-term four-dimensional (4D) trajectory prediction models, this paper proposes a hybrid CNN-LSTM-attention-adaboost neural network model incorporating a multi-strategy improved snake-herd optimization (SO) algorithm. The model applies the Adaboost algorithm to divide multiple weak learners, and each submodel utilizes CNN to extract spatial features, LSTM to capture temporal features, and attention mechanism to capture global features comprehensively. The strong learner model, combined with multiple sub-models, then optimizes the hyperparameters of the prediction model through the natural selection behavior pattern simulated by SO. In this study, based on the real ADS-B data from Xi'an to Tianjin, the comparison experiments and ablation studies of multiple optimizers are carried out, and a comprehensive test and evaluation analysis is carried out. The results show that SO-CLA-adaboost outperforms traditional optimizers such as particle swarm, whale, and gray wolf in handling large-scale high-dimensional trajectory data. In addition, introducing the full-strategy collaborative improvement SO algorithm improves the model's prediction accuracy by 39.89%.