Expert Systems with Applications最新文献_第7页

A sliding window feature extraction and weight adaptive random forest-based method for TBM tunnel rock mass grade identification 基于滑动窗特征提取和权值自适应随机森林的TBM隧道岩体等级识别方法

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-23 DOI: 10.1016/j.eswa.2026.131336

Honggan Yu , Shuzhan Xu , Xin Yin , Mahdi Hasanipanah

Identifying the rock mass grade of the tunnel face is the basis for analyzing the stability of the surrounding rock and predicting the performance of the tunnel boring machine (TBM). Current research on using TBM’s tunneling parameters to identify rock mass grade usually overlooks the geological information contained in the changes of tunneling parameters, and the machine learning methods rarely consider the impact of class weights. Therefore, this paper proposes a sliding window feature extraction and weight adaptive random forest-based method for rock mass grade identification. Firstly, the raw data is preprocessed, including parameter screening based on experience and random forest, outlier detection based on isolated forest etc. After that, a novel sliding window-based feature extraction method is proposed, which can extract geological-related features from the changes in tunneling parameters. Finally, a weight adaptive random forest algorithm is proposed, and the particle swarm optimization is used to obtain the optimal class weights. On-site data from a water conveyance project was used to validate the effectiveness of the proposed method. The results show that the proposed sliding window feature extraction method can significantly improve the model’s performance compared with directly using tunneling parameters as the model’s input. Moreover, the proposed weight adaptive random forest algorithm can effectively suppress misclassification caused by high similarity among classes, and its performance is better than random forest, adaptive boosting, extreme gradient boosting, and light gradient boosting machine. Therefore, the proposed method can accurately identify the rock mass grade, which has essential engineering value.

确定巷道工作面岩体等级是分析围岩稳定性和预测隧道掘进机性能的基础。目前利用掘进机掘进参数识别岩体品位的研究往往忽略了掘进参数变化所包含的地质信息，机器学习方法也很少考虑类权值的影响。为此，本文提出了一种基于滑动窗口特征提取和权值自适应随机森林的岩体品位识别方法。首先对原始数据进行预处理，包括基于经验和随机森林的参数筛选、基于孤立森林的离群点检测等；在此基础上，提出了一种基于滑动窗的特征提取方法，该方法可以从掘进参数的变化中提取地质相关特征。最后，提出了一种权值自适应随机森林算法，并利用粒子群算法获得最优的类权值。利用某输水工程的现场数据验证了该方法的有效性。结果表明，与直接使用隧道参数作为模型输入相比，所提出的滑动窗口特征提取方法能显著提高模型的性能。此外，所提出的权重自适应随机森林算法能有效抑制由于类间高度相似而导致的误分类，其性能优于随机森林、自适应增强、极端梯度增强和轻梯度增强机。因此，该方法能准确识别岩体品位，具有重要的工程价值。

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引用次数: 0

Beyond clicks: Measuring attractiveness and satisfaction in e-commerce using Bayesian models with conversion signals 超越点击：使用带有转换信号的贝叶斯模型测量电子商务的吸引力和满意度

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-23 DOI: 10.1016/j.eswa.2026.131299

Hacer Turgut , Afra Arslan , Ömür Bali , Mehmet Yasin Ulukuş

Understanding user behavior is essential for improving user experience and maximizing conversion rates on e-commerce platforms. To more accurately capture user satisfaction, the iLab Click and Conversion Dynamic Bayesian Network (iCCDBN) is introduced, a novel click model that jointly incorporates click and post-click conversion signals. iCCDBN employs separate satisfaction parameters for clicks and conversions, enhancing interpretability while maintaining computational efficiency. The probabilistic formulation of the model is derived, and parameter estimation is carried out using the Expectation-Maximization (EM) algorithm. For evaluation, iCCDBN is compared with established click models on large-scale interaction logs from a real estate marketplace. Results show that iCCDBN, together with strong baselines, achieves the lowest click-through rate prediction errors, with optimal performance observed when (query, item) pairs have at least 60 historical sessions. In satisfaction prediction, iCCDBN surpasses the Dynamic Bayesian Network (DBN) with a lower mean squared error (0.1927 vs. 0.2313). KL divergence analysis further demonstrates that iCCDBN achieves an 8.6% reduction in KL divergence when evaluated on raw prediction scores. When score ranges are normalized via min-max scaling, thereby emphasizing distributional shape rather than scale, the relative improvement increases to 17.4%. These findings highlight the benefits of integrating conversion data and refined behavioral structures into click models, offering a more faithful representation of user satisfaction.

了解用户行为对于改善用户体验和最大化电子商务平台的转化率至关重要。为了更准确地捕捉用户满意度，引入了iLab点击和转换动态贝叶斯网络（iCCDBN），这是一种结合点击和点击后转换信号的新颖点击模型。iCCDBN为点击和转换使用单独的满意度参数，在保持计算效率的同时增强了可解释性。推导了模型的概率表达式，并采用期望最大化（EM）算法进行参数估计。为了进行评估，将iCCDBN与基于房地产市场大规模交互日志的已建立的点击模型进行了比较。结果表明，iCCDBN与强基线一起实现了最低的点击率预测误差，当（查询，项目）对具有至少60个历史会话时观察到最佳性能。在满意度预测方面，iCCDBN以较低的均方误差（0.1927 vs. DBN）优于动态贝叶斯网络（DBN）。0.2313)。KL散度分析进一步表明，当对原始预测分数进行评估时，iCCDBN的KL散度降低了8.6%。当分数范围通过最小-最大尺度归一化，从而强调分布形状而不是尺度时，相对改善增加到17.4%。这些发现突出了将转换数据和精炼的行为结构整合到点击模型中的好处，提供了更忠实的用户满意度表示。

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引用次数: 0

Design of Bayesian acceptance test for multi-state reliability growth of equipment with multinomial distribution 多项分布设备多状态可靠性增长的贝叶斯验收试验设计

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-23 DOI: 10.1016/j.eswa.2026.131306

Haobang Liu, Tong Chen , Peng Di, Tao Hu, Haolin Wen, Lisha Zheng, Minggui Li

Traditional equipment reliability acceptance test predominantly relies on binary outcomes (normal/failure), which inadequately capture the multi-state nature of performance degradation in operation. To address this, this paper proposes a knowledge-integrated Bayesian acceptance test framework for multi-state reliability growth, modeled via a multinomial distribution. The framework systematically incorporates domain expert knowledge and simulation test information through Dirichlet prior, and embeds sequential constraints of reliability growth into the Bayesian model, effectively serving as an expert-augmented decision support system. To solve this model, the existing Markov chain-Monte Carlo (MCMC) and Gibbs sampling algorithms are improved to ensure parameter samples extracted in each iteration meet the sequential constraints. Experimental analysis demonstrates that the standard deviation of the reliability estimation results obtained by this research method is reduced from 0.079 to 0.058 compared with the existing Bayesian method, and the operating characteristic (OC) curve is steeper, indicating stronger discrimination and sharper decision-making. This work provides a scalable and knowledge-integrated Bayesian framework that aligns with the development of expert systems for intelligent reliability estimation and acceptance test.

传统的设备可靠性验收测试主要依赖于二元结果（正常/故障），这不能充分反映运行中性能退化的多状态性质。为了解决这个问题，本文提出了一个知识集成的贝叶斯验收测试框架，用于通过多项分布建模的多状态可靠性增长。该框架通过Dirichlet先验算法系统地融合了领域专家知识和仿真试验信息，并将可靠性增长的顺序约束嵌入到贝叶斯模型中，有效地作为专家增强决策支持系统。为了求解该模型，改进了现有的Markov chain-Monte Carlo （MCMC）和Gibbs采样算法，保证每次迭代提取的参数样本满足序列约束。实验分析表明，与现有贝叶斯方法相比，该方法得到的可靠性估计结果的标准差从0.079降低到0.058，OC曲线更陡，具有更强的判别能力和更敏锐的决策能力。这项工作提供了一个可扩展和知识集成的贝叶斯框架，与智能可靠性评估和验收测试专家系统的发展保持一致。

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引用次数: 0

AAF-Bi-LSTM-NARX: A bidirectional network with adaptive attention fusion for sensor data imputation AAF-Bi-LSTM-NARX：基于自适应注意力融合的传感器数据输入双向网络

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-22 DOI: 10.1016/j.eswa.2026.131266

Zhao Zhao, Xian Yi, Jianjun Xiong, Lin Ran, Jieyi Zhao, Yalan Zhu

Sensors in wind tunnel tests occasionally experience data missing due to harsh environmental conditions, which affects data quality and system safety. Traditional interpolation and machine learning methods are difficult to effectively capture the nonlinear and temporal features in the data, while most existing deep learning models rely on unidirectional information flow and fail to make full use of contextual information. To address this issue, a bidirectional LSTM-NARX model based on adaptive attention fusion (AAF-Bi-LSTM-NARX) is proposed for imputing the missing data of wind tunnel sensors.The model extracts forward and backward temporal features respectively through a bidirectional LSTM-NARX network. Furthermore, an adaptive attention fusion module is designed to dynamically fuse the bidirectional prediction results, thereby improving the accuracy of data imputation. Experiments on a real wind tunnel dataset show that the proposed model significantly outperforms comparative models such as LSTM-NARX, Bi-LSTM, LSTM, Informer, GRU-D, and ARIMA under the missing rate of 5%, 10%, and 25%. Ablation studies further verify the effectiveness of the bidirectional structure and the global error verification mechanism, and explore the applicable conditions of local attention under different missing rates. This study provides an efficient and reliable solution for sensor data imputation, and is of great significance for improving the data quality of wind tunnel tests and supporting the development of data-driven wind tunnel systems.

风洞试验中的传感器由于环境条件恶劣，有时会出现数据丢失的情况，影响数据质量和系统安全性。传统的插值和机器学习方法难以有效捕获数据中的非线性和时间特征，而现有的深度学习模型大多依赖于单向信息流，未能充分利用上下文信息。针对这一问题，提出了一种基于自适应注意力融合的双向LSTM-NARX模型（AAF-Bi-LSTM-NARX），用于风洞传感器缺失数据的输入。该模型通过双向LSTM-NARX网络分别提取前向和后向时间特征。设计了自适应注意力融合模块，对双向预测结果进行动态融合，提高了数据输入的准确性。在真实风洞数据集上的实验表明，在缺失率分别为5%、10%和25%的情况下，该模型显著优于LSTM- narx、Bi-LSTM、LSTM、Informer、GRU-D和ARIMA等模型。消融研究进一步验证了双向结构和全局误差验证机制的有效性，探索了不同缺失率下局部关注的适用条件。该研究为传感器数据的输入提供了一种高效、可靠的解决方案，对于提高风洞试验数据质量，支持数据驱动风洞系统的发展具有重要意义。

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引用次数: 0

DRKT: Learning differential relationships for efficient knowledge tracing with learner’s knowledge internalization representation 基于学习者知识内化表征的有效知识跟踪学习微分关系

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-22 DOI: 10.1016/j.eswa.2026.131319

Zhaoli Zhang , Jiahao Li , Hai Liu , Erqi Zhang , Tingting Liu , Minhong Wang

Knowledge tracing (KT), is a crucial task in educational data mining that aims to model the state of learners’ knowledge by analyzing their behavioral data in real time. However, unlike the dynamic evolution process of learners’ knowledge internalization in KT modeling, the intrinsic features associated with exercises and knowledge components (KCs) remain static. Many existing models overlook this distinction and fail to implement differentiated feature processing. Additionally, the rapidly expanding volume of data in online learning platforms poses new challenges to model performance and efficiency. To address these issues, we propose DRKT, a new model that employs intrinsic information mining (IIM) module to extract inherent feature information from exercises and KCs. We also utilize the Mamba network to capture learner-exercise interaction patterns and achieve a balance between performance and efficiency. Furthermore, we introduce a double matrix dynamic update (DMDU) strategy to differentially model the complex dynamics of knowledge internalization and the inherent invariability of exercises and KCs. Experimental results on four real-world educational datasets demonstrate that DRKT outperforms existing methods in predictive accuracy, resource consumption, and time complexity, providing effective technical support for pedagogical interventions and personalized learning recommendations.

知识追踪（Knowledge tracing， KT）是教育数据挖掘中的一项重要任务，旨在通过实时分析学习者的行为数据，对学习者的知识状态进行建模。然而，与KT建模中学习者知识内化的动态演变过程不同，与练习和知识组件（KCs）相关的内在特征是静态的。许多现有模型忽略了这一区别，未能实现差异化的特征处理。此外，在线学习平台中快速增长的数据量对模型的性能和效率提出了新的挑战。为了解决这些问题，我们提出了一种新的DRKT模型，该模型采用内在信息挖掘（IIM）模块从练习和KCs中提取固有特征信息。我们还利用曼巴网络来捕捉学习者-锻炼的互动模式，并实现性能和效率之间的平衡。此外，我们还引入了双矩阵动态更新（DMDU）策略，对知识内化的复杂动态以及练习和KCs的固有不变性进行差异性建模。在四个真实教育数据集上的实验结果表明，DRKT在预测精度、资源消耗和时间复杂度方面优于现有方法，为教学干预和个性化学习建议提供了有效的技术支持。

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引用次数: 0

Variational oblique predictive clustering trees 变分倾斜预测聚类树

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-22 DOI: 10.1016/j.eswa.2026.131255

Viktor Andonovikj , Sašo Džeroski , Biljana Mileva Boshkoska , Pavle Boškoski

Oblique predictive clustering trees (SPYCTs) are semi-supervised multi-target prediction models mainly used for structured output prediction (SOP) problems. They are computationally efficient and when combined in ensembles they achieve state-of-the-art results. However, one major issue is that it is challenging to interpret an ensemble of SPYCTs without the use of a model-agnostic method. We propose variational oblique predictive clustering trees, which address this challenge. The parameters of each split node are treated as random variables, described with a probability distribution, and they are learned through the Variational Bayes method. We evaluate the model on several benchmark datasets of different sizes. The experimental analyses show that a single variational oblique predictive clustering tree (VSPYCT) achieves competitive, and sometimes better predictive performance than the ensemble of standard SPYCTs. We also present a method for extracting feature importance scores from the model. Finally, we present a method to visually interpret the model’s decision making process through analysis of the relative feature importance in each split node.

斜预测聚类树（spyct）是一种半监督多目标预测模型，主要用于结构化输出预测问题。它们在计算上是高效的，当组合在一起时，它们达到了最先进的结果。然而，一个主要问题是，在不使用模型不可知方法的情况下解释spyct集合是具有挑战性的。我们提出了变分倾斜预测聚类树，解决了这一挑战。将每个分裂节点的参数作为随机变量，用概率分布来描述，并通过变分贝叶斯方法进行学习。我们在几个不同规模的基准数据集上对模型进行了评估。实验分析表明，单一变分倾斜预测聚类树（VSPYCT）的预测性能优于标准的倾斜预测聚类树。我们还提出了一种从模型中提取特征重要性分数的方法。最后，我们提出了一种通过分析每个分裂节点的相对特征重要性来直观解释模型决策过程的方法。

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引用次数: 0

GWO-DAGRU: A hybrid deep learning framework with metaheuristic feature selection and self-weighted context GRU for short-term wind power forecast 基于元启发式特征选择和自加权上下文GRU的风电短期预测混合深度学习框架

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-22 DOI: 10.1016/j.eswa.2026.131279

Saira Mudassar , Aneela Zameer , Muhammad Asif Zahoor Raja

Accurate wind power forecasting is crucial for effectively integrating renewable energy into the electric grid, enabling the optimal utilization of generated clean energy. The intermittent nature of wind and the operational complexity of sustainable energy systems make prediction a highly challenging task. This study combines the strengths of a metaheuristic algorithm, grey wolf optimization (GWO), for feature selection with a time-series multivariate forecasting model, gated recurrent unit (GRU), along with a double attention mechanism (DAGRU) for effective, precise, and efficient predictions. The proposed model, GWO-DAGRU, is a short-term wind power forecasting model that integrates grey wolf optimization for feature selection with a double attention gated recurrent unit for time-series prediction. GWO, combined with an XGBoost regressor, is first used to identify key input features and refined by a double attention mechanism in DAGRU to capture temporal dependencies more effectively. The proposed approach is validated on data from seven European wind farms and further tested on the ELIA dataset to assess generalization capability. Performance is benchmarked using error metrics and statistical validation through the Wilcoxon signed-rank test at a 95% confidence level. The findings demonstrate that GWO-DAGRU achieves superior accuracy and robustness, outperforming several existing forecasting methods for efficient management and planning of a sustainable energy support system.

准确的风电预测对于有效地将可再生能源纳入电网，实现清洁能源的最佳利用至关重要。风能的间歇性和可持续能源系统运行的复杂性使得预测成为一项极具挑战性的任务。本研究结合了用于特征选择的元启发式算法灰狼优化（GWO）与时间序列多元预测模型门控循环单元（GRU）以及双注意机制（DAGRU）的优势，以实现有效、精确和高效的预测。提出的GWO-DAGRU模型是一种短期风电预测模型，它将灰狼优化的特征选择与双关注门控循环单元的时间序列预测相结合。GWO与XGBoost回归器相结合，首先用于识别关键输入特征，并通过DAGRU中的双注意机制进行细化，以更有效地捕获时间依赖性。该方法在七个欧洲风电场的数据上进行了验证，并在ELIA数据集上进行了进一步测试，以评估泛化能力。通过95%置信水平的Wilcoxon sign -rank检验，使用误差度量和统计验证对性能进行基准测试。研究结果表明，GWO-DAGRU在可持续能源支持系统的有效管理和规划方面具有优越的准确性和鲁棒性，优于现有的几种预测方法。

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引用次数: 0

Improving face re-identification via identity-conditioned synthetic augmentation and inference-time embedding fusion 利用身份条件合成增强和推理时间嵌入融合改进人脸再识别

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-22 DOI: 10.1016/j.eswa.2026.131302

Héctor Penadés, Félix Escalona, Miguel Cazorla

The task of face re-identification seeks to match identities across images captured under varying conditions. In conventional single-registration scenarios, only one real image per subject is available during inference, limiting the discriminative capability of the embedding. Advances in synthetic data present new opportunities for improving recognition systems, particularly as privacy concerns restrict data availability. We propose a novel method that leverages identity-guided synthetic augmentation to enrich facial representations at inference time. Unlike traditional data augmentation, it enhances embeddings through sample aggregation, introducing an inference-time paradigm for representation enrichment without expanding the training set or retraining existing models. Using Arc2Face, we generate diverse, identity-consistent synthetic images from each real sample, synthesizing multiple facial variations to approximate the distributional space around each identity. A non-parametric analysis of ten embedding fusion strategies showed consistent improvements over the baselines, with the Mean, Median, and hybrid Mean-Median (Meta-MM) achieving the best performance and Meta-MM showing the lowest variability across models. Experiments demonstrated consistent improvements across re-identification and verification settings. On Labeled Faces in the Wild (LFW) dataset, Rank-1 accuracy improved by an average of 6.97 points and mean Average Precision (mAP) by 5.82 and 8.10 points. On the Surveillance Cameras Face (SCFace) dataset, a low-quality, cross-distance dataset, Rank-1 gains ranged from 10.98 to 31.33 points. On the Cross-Pose LFW (CPLFW) verification benchmark, accuracy generally matched or exceeded AdaFace baselines, with gains of up to 5.57 points. Incorporating latent consistency models with low-rank adaptation (LCM-LoRA) accelerated sample generation tenfold, making the framework suitable for large-scale applications.

人脸再识别的任务是在不同条件下捕获的图像中匹配身份。在传统的单配准场景中，在推理过程中每个受试者只有一张真实图像，限制了嵌入的判别能力。合成数据的进步为改进识别系统提供了新的机会，特别是在隐私问题限制数据可用性的情况下。我们提出了一种新的方法，利用身份引导合成增强来丰富推理时的面部表征。与传统的数据增强不同，它通过样本聚合来增强嵌入，在不扩展训练集或重新训练现有模型的情况下，引入了一个用于表示丰富的推理时间范式。使用Arc2Face，我们从每个真实样本中生成多样化，身份一致的合成图像，合成多种面部变化以近似每个身份周围的分布空间。对10种嵌入融合策略的非参数分析显示，在基线上有一致的改进，Mean、Median和hybrid Mean-Median （Meta-MM）获得了最佳性能，Meta-MM显示出不同模型之间最低的可变性。实验证明了在重新识别和验证设置中一致的改进。在Labeled Faces in the Wild （LFW）数据集上，Rank-1的准确率平均提高了6.97点，平均平均精度（mAP）提高了5.82点和8.10点。在监控摄像头面部（SCFace）数据集（一个低质量的跨距离数据集）上，排名1的增益范围从10.98到31.33分不等。在交叉位姿LFW （Cross-Pose LFW， CPLFW）验证基准上，准确率基本达到或超过AdaFace基线，最高可达5.57分。将潜在一致性模型与低秩自适应（LCM-LoRA）相结合，使样本生成速度加快了10倍，使框架适合大规模应用。

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引用次数: 0

Adaptive decomposition-based transfer learning for dynamic constrained multi-objective optimization 基于自适应分解的动态约束多目标优化迁移学习

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-22 DOI: 10.1016/j.eswa.2026.131220

Li Yan , Yinjin Wu , Boyang Qu , Chao Li , Jing Liang , Kunjie Yu , Caitong Yue , Baihao Qiao , Yuqi Lei

Dynamic constrained multiobjective optimization problems (DCMOPs) are characterized by objective functions and constraints that change complexly over time. This time-varying characteristic proposes significant challenges for existing optimization algorithms, particularly in rapidly tracking the dynamic feasible regions and accurately converging to the changing Dynamic Constrained Pareto Optimal Front (DCPOF). To address the above challenges, an adaptive decomposition-based transfer learning method is proposed in this article, termed ADTL. The method introduces an adaptive objective space decomposition strategy to locate the dynamic feasible regions accurately. Upon the detection of a new environment, the objective space is decomposed by the historical optimal solutions. To efficiently track the DCPOF, an individual-based transfer learning strategy is proposed, which associates each solution in the current environment with its nearest reference vector. Then, a single-layer autoencoder is employed to learn the features of historical optimal solutions and transfer historical knowledge to the current population. Furthermore, to improve search efficiency, a diversity and feasibility enhancement strategyis proposed. This strategy evaluates the diversity and feasibility of the predicted population, introduces random solutions according to the diversity level, and relocates infeasible solutions to the boundary of the feasible regions. Comprehensive experiments on widely used benchmark problems demonstrate that the proposed algorithm is highly competitive in dealing with DCMOPs when compared with seven state-of-the-art algorithms.

动态约束多目标优化问题具有目标函数和约束随时间复杂变化的特点。这种时变特性对现有优化算法提出了重大挑战，特别是在快速跟踪动态可行区域和准确收敛到变化的动态约束帕累托最优前沿（DCPOF）方面。为了解决上述挑战，本文提出了一种基于自适应分解的迁移学习方法，称为ADTL。该方法引入自适应目标空间分解策略，精确定位动态可行区域。在检测到新环境后，用历史最优解对目标空间进行分解。为了有效地跟踪DCPOF，提出了一种基于个体的迁移学习策略，该策略将当前环境中的每个解与其最近的参考向量相关联。然后，采用单层自编码器学习历史最优解的特征，并将历史知识传递给当前种群；为了提高搜索效率，提出了一种多样性和可行性增强策略。该策略评估预测种群的多样性和可行性，根据多样性水平引入随机解，并将不可行解重新定位到可行区域的边界。在广泛应用的基准问题上进行的综合实验表明，与现有的7种算法相比，该算法在处理DCMOPs方面具有很强的竞争力。

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引用次数: 0

STGFormer: A pyramidal spatio-temporal graph transformer with cross-disciplinary feature fusion for semantic-rich trajectory prediction in heterogeneous autonomy traffic STGFormer：一种基于多学科特征融合的金字塔形时空图转换器，用于异构自治交通中富含语义的轨迹预测

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-01-22 DOI: 10.1016/j.eswa.2026.131304

Cheng Ju , Yuansha Xie , Zhongrong Wang , Yu Zhao , Wenyao Yan , Rongjun Chai , Juan Duan , Yali Cao , Yuxin Chang

Achieving high-precision and multimodal trajectory prediction for multiple agents in mixed traffic environments, where autonomous and human-driven vehicles coexist, constitutes a fundamental scientific challenge for ensuring traffic safety and efficiency. To address the limitations of existing approaches in modeling heterogeneous behaviors, long-term dependencies, and high-level semantics in complex dynamic scenarios, a Pyramidal Spatio-Temporal Graph Transformer (STGFormer) based on cross-disciplinary feature fusion is proposed in this study. This method, grounded in hierarchical feature integration, systematically incorporates multi-source information from physical, psychological, environmental, and social domains, thereby significantly enhancing the model’s capacity to represent diverse behaviors. In the spatial modeling stage, an Adaptive Neighborhood Selection Graph Convolutional Network (ANS-GCN) is introduced, which dynamically selects key interactive agents through a multi-factor learnable weighting mechanism, enabling efficient spatial relationship modeling. For temporal modeling, a Pyramid Sparse Semantic Attention Transformer Encoder (PSSAT) is designed to progressively capture short-term dynamics and long-term trends, integrating spatial, temporal, and behavioral semantic features. Ultimately, a t-distribution-based Mixture Density Network (TDMDN) is employed for multimodal probabilistic modeling, better fitting the multi-modal and heavy-tailed distributions of future trajectories and enhancing adaptability and robustness in complex traffic contexts. Experimental results demonstrate that the proposed STGFormer achieves synergistic improvements in accuracy, diversity, and physical plausibility across multiple mainstream evaluation metrics, exhibiting superior predictive consistency and robustness, particularly in complex interactions and adverse driving scenarios. These findings not only validate the effectiveness of cross-disciplinary feature fusion and hierarchical structural design in multi-agent trajectory modeling but also provide a theoretical foundation and methodological reference for multimodal behavior understanding and safe decision-making in intelligent transportation systems.

在自动驾驶和人类驾驶车辆并存的混合交通环境中，实现多智能体的高精度多模式轨迹预测，是确保交通安全和效率的根本科学挑战。为了解决现有方法在复杂动态场景中异构行为、长期依赖关系和高级语义建模方面的局限性，本研究提出了一种基于跨学科特征融合的金字塔形时空图转换器（STGFormer）。该方法以分层特征集成为基础，系统地融合了来自物理、心理、环境和社会领域的多源信息，从而显著增强了模型表征多种行为的能力。在空间建模阶段，引入自适应邻域选择图卷积网络（ANS-GCN），通过多因素可学习的加权机制动态选择关键交互主体，实现高效的空间关系建模。对于时间建模，设计了一个金字塔稀疏语义注意转换编码器（PSSAT），以逐步捕获短期动态和长期趋势，整合空间，时间和行为语义特征。最后，采用基于t分布的混合密度网络（TDMDN）进行多模态概率建模，更好地拟合未来轨迹的多模态和重尾分布，增强复杂交通环境下的适应性和鲁棒性。实验结果表明，提出的STGFormer在多个主流评估指标之间实现了准确性、多样性和物理合理性的协同改进，表现出卓越的预测一致性和鲁棒性，特别是在复杂的相互作用和不利的驾驶场景中。这些发现不仅验证了跨学科特征融合和分层结构设计在多智能体轨迹建模中的有效性，也为智能交通系统中多式联运行为理解和安全决策提供了理论基础和方法参考。

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