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%.

翻译：针对中长期四维（4D）轨迹预测模型的局限性，本文提出了一种融合多策略改进蛇群优化（SO）算法的混合CNN-LSTM-attention-adaboost神经网络模型。该模型应用Adaboost算法划分多个弱学习器，每个子模型利用CNN提取空间特征，LSTM捕捉时序特征，并借助注意力机制全面捕获全局特征。结合多个子模型的强学习器模型，随后通过SO模拟的自然选择行为模式优化预测模型的超参数。本研究基于西安至天津的真实ADS-B数据，开展了多种优化器的对比实验与消融研究，并进行了全面的测试与评估分析。结果表明，SO-CLA-adaboost在处理大规模高维轨迹数据时，其性能优于粒子群、鲸鱼算法和灰狼算法等传统优化器。此外，引入全策略协同改进的SO算法使模型的预测精度提升了39.89%。