用改进的蛇群算法优化模型，提升轨迹预测精度
Multi-Strategy Improved Snake Optimizer Accelerated CNN-LSTM-Attention-Adaboost for Trajectory Prediction
- 融合CNN-LSTM-注意力机制，分层提取时空特征
- 改进蛇群算法使预测准确率提升39.89%
- 适合交通流、飞行轨迹等高维时序数据预测
针对中长距离四维（4D）轨迹预测模型的不足，本文提出一种融合多策略改进蛇群优化（SO）算法的CNN-LSTM-注意力-Adaboost混合神经网络模型。该模型通过Adaboost将多个弱学习器组合为强学习器，各子模型分别利用CNN提取空间特征、LSTM捕捉时间特征、注意力机制整合全局信息。强学习器结合多子模型，借助模拟自然选择行为的SO算法优化预测模型超参数。基于西安至天津的真实ADS-B数据进行对比实验与消融研究，结果表明：相较于粒子群、鲸鱼、灰狼等传统优化器，SO-CLA-Adaboost在处理大规模高维轨迹数据时表现更优；引入全策略协同改进的SO算法后，模型预测准确率提升39.89%。
原文摘要 · Abstract (English)
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%.
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