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FDS: Fractal decomposition based direct search approach for continuous dynamic optimization 基于分形分解的连续动态优化直接搜索方法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-23 DOI: 10.1016/j.ins.2025.122237

Arcadi Llanza , Nadiya Shvai , Amir Nakib

{"title":"FDS: Fractal decomposition based direct search approach for continuous dynamic optimization","authors":"Arcadi Llanza , Nadiya Shvai , Amir Nakib","doi":"10.1016/j.ins.2025.122237","DOIUrl":"10.1016/j.ins.2025.122237","url":null,"abstract":"<div><div>Dynamic optimization problems (DOPs) are known to be challenging due to the variability of their objective functions and constraints over time. The complexity of these problems increases further when the frequency of landscape change and the dimensionality of the search space are large. In this work, we propose a novel fractal decomposition-based method designed for DOPs, called FDS. It is a new single solution metaheuristic that introduces a new hypersphere-based space decomposition for efficient exploration, an archive for diversity control, and a pseudo-gradient-based local search (called GraILS) for fast exploitation. Extensive experiments on the well-known and the standard benchmark (the Moving Peak Benchmark: MPB) demonstrate that FDS consistently outperforms state-of-the-art competitors. Furthermore, FDS shows high robustness across diverse scenarios, maintaining superior performance despite variations in key benchmark parameters, such as the severity of landscape shifts, the number of peaks, the dimensionality of the problem, and the frequency of change. FDS achieves the highest average rank across all experiments and demonstrates dominant performance in 19 out of 23 scenarios. The implementation of FDS is available via the following GitHub repository: <span><span>https://github.com/alc1218/FDS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122237"},"PeriodicalIF":8.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143885962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multiobjective edge-based learning algorithm for the vehicle routing problem with time windows 带时间窗车辆路径问题的多目标边缘学习算法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-23 DOI: 10.1016/j.ins.2025.122223

Ying Zhou , Lingjing Kong , Hui Wang

{"title":"A multiobjective edge-based learning algorithm for the vehicle routing problem with time windows","authors":"Ying Zhou , Lingjing Kong , Hui Wang","doi":"10.1016/j.ins.2025.122223","DOIUrl":"10.1016/j.ins.2025.122223","url":null,"abstract":"<div><div>The multiobjective vehicle routing problem with time windows has attracted much attention in recent decades. Until now, various metaheuristic methods have been proposed to solve the problem. However, designing effective methods is not trivial and heavily depends on experts' knowledge. As a research hotspot in recent years, a few deep reinforcement learning methods have been tried to solve the multiobjective vehicle routing problem with symmetric distance and time matrices. However, due to the complex traffic conditions, the travel distance and time between two nodes are probably asymmetric in real-world scenarios. This article introduces a multiobjective edge-based learning algorithm (MOEL) to tackle this issue. In this method, a single neural network model is established and trained to approximate the whole Pareto front of the problem. The edge features, including travel distance and time matrices, are fully learned and used to construct high-quality solutions. MOEL is compared against three state-of-the-art deep reinforcement learning methods (MODRL/D-EL, PMOCO, EMNH) and five metaheuristic methods (NSGA-II, MOEA/D, NSGA-III, MOEA/D-D, MOIA). Experimental results on the real-world instances indicate that MOEL significantly outperforms all competitors, improving IGD by up to 99.80% and HV by up to 62.84%. In addition, MOEL achieves a maximum runtime reduction of 88.65% compared to the deep reinforcement learning methods, highlighting its efficiency and effectiveness for solving the problem.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122223"},"PeriodicalIF":8.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards undetectable adversarial attack on time series classification 针对时间序列分类的不可检测对抗性攻击

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-23 DOI: 10.1016/j.ins.2025.122216

Hoki Kim , Yunyoung Lee , Woojin Lee , Jaewook Lee

{"title":"Towards undetectable adversarial attack on time series classification","authors":"Hoki Kim , Yunyoung Lee , Woojin Lee , Jaewook Lee","doi":"10.1016/j.ins.2025.122216","DOIUrl":"10.1016/j.ins.2025.122216","url":null,"abstract":"<div><div>Although deep learning models have shown superior performance for time series classification, prior studies have recently discovered that small perturbations can fool various time series models. This vulnerability poses a serious threat that can cause malfunctions in real-world systems, such as Internet-of-Things (IoT) devices and industrial control systems. To defend these systems against adversarial time series, recent studies have proposed a detection method using time series characteristics. In this paper, however, we reveal that this detection-based defense can be easily circumvented. Through an extensive investigation into existing adversarial attacks and generated adversarial time series examples, we discover that they tend to ignore the trends in local areas and add excessive noise to the original examples. Based on the analyses, we propose a new adaptive attack, called trend-adaptive interval attack (TIA), that generates a hardly detectable adversarial time series by adopting trend-adaptive loss and gradient-based interval selection. Our experiments demonstrate that the proposed method successfully maintains the important features of the original time series and deceives diverse time series models without being detected.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122216"},"PeriodicalIF":8.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-label feature selection for imbalanced data via KNN-based multi-label rough set theory 基于knn的多标签粗糙集理论的不平衡数据多标签特征选择

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-23 DOI: 10.1016/j.ins.2025.122220

Weihua Xu, Yuzhe Li

{"title":"Multi-label feature selection for imbalanced data via KNN-based multi-label rough set theory","authors":"Weihua Xu, Yuzhe Li","doi":"10.1016/j.ins.2025.122220","DOIUrl":"10.1016/j.ins.2025.122220","url":null,"abstract":"<div><div>In the realm of multi-label feature selection, the intricacy of data structures and semantics has been escalating, rendering traditional single-label feature selection methodologies inadequate for contemporary demands to meet contemporary demands. This manuscript introduces an innovative neighborhood rough set model that integrates <em>δ</em>-neighborhood rough sets with <em>k</em>-nearest neighbor techniques, facilitating a transition from single-label to multi-label learning frameworks. The study delves into the attribute dependency concept within rough set theory and proposes a novel importance function based thereon, which can effectively quantify the significance of features within multi-label decision-making contexts. Building on this theoretical foundation, we have crafted a feature selection algorithm specifically tailored for imbalanced datasets. Extensive experiments conducted on 12 datasets, coupled with comparative analyses with 10 cutting-edge methods, have substantiated the superior performance of our algorithm in managing imbalanced datasets. This research not only offers a fresh theoretical perspective but also has significant practical implications, particularly in scenarios involving imbalanced datasets with multiple labels.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122220"},"PeriodicalIF":8.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning-boosted intelligent frequency control of multi-area Markov jumping power system via multiplayer Stackelberg-Nash game 基于Stackelberg-Nash博弈的多区域马尔可夫跳变电力系统学习推进智能频率控制

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-22 DOI: 10.1016/j.ins.2025.122218

Yuzhuo Zhang , Mengzhuo Luo , Jun Cheng , Huaicheng Yan , Kaibo Shi

{"title":"Learning-boosted intelligent frequency control of multi-area Markov jumping power system via multiplayer Stackelberg-Nash game","authors":"Yuzhuo Zhang , Mengzhuo Luo , Jun Cheng , Huaicheng Yan , Kaibo Shi","doi":"10.1016/j.ins.2025.122218","DOIUrl":"10.1016/j.ins.2025.122218","url":null,"abstract":"<div><div>This paper proposes an intelligent frequency control (IFC) scheme integrating multiple strategies, which aims to address the frequency control challenges of multi-area Markov jumping power systems (MMJPSs) under load fluctuations and external disturbances. Firstly, the Markov superposition technique is employed to conduct refined modeling on the system component matrices, precisely capturing the diversity of load operating states. Secondly, within the framework of the multiplayer Stackelberg-Nash game (MSNG), the load aggregator (LA) is set as the leader and the turbines in each area are regarded as the followers. By constructing the value functions of the leader and the followers, the dynamic process of hierarchical decision-making is elaborately depicted. Meanwhile, an adaptive event-triggered mechanism (AETM) is designed to alleviate the computational and communication burdens. On this basis, by combining the integral reinforcement learning (IRL) algorithm with the neural network (NN), the Hamilton-Jacobi-Bellman (HJB) equation based on the AETM is solved to obtain the approximately optimal control law and achieve the Stackelberg-Nash equilibrium (SNE). Utilizing Lyapunov stability theory, the uniform ultimate boundedness (UUB) of the system states and the NN weight errors is rigorously proved. Finally, comparative simulation results validate the effectiveness and practicality of the proposed method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122218"},"PeriodicalIF":8.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep unsupervised clustering by information maximization on Gaussian mixture autoencoders 基于信息最大化的高斯混合自编码器深度无监督聚类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-22 DOI: 10.1016/j.ins.2025.122215

Peng Wu , Li Pan

引用次数: 0

Incorporating metadata: A novel variational neural topic model for bond default prediction 结合元数据：一种新的债券违约预测变分神经主题模型

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-22 DOI: 10.1016/j.ins.2025.122219

Wang Lu , Cuiqing Jiang , Zhao Wang , Yong Ding , Xiaoya Ni

{"title":"Incorporating metadata: A novel variational neural topic model for bond default prediction","authors":"Wang Lu , Cuiqing Jiang , Zhao Wang , Yong Ding , Xiaoya Ni","doi":"10.1016/j.ins.2025.122219","DOIUrl":"10.1016/j.ins.2025.122219","url":null,"abstract":"<div><div>Credit rating reports, especially the analyst rating opinions with positive and negative tendencies, offer key insights into the risks influencing bond issuers' ability to repay debt, making them vital for bond default prediction. However, meaningful topics are challenging to extract from these texts. Positive and negative rating opinions provide metadata that could guide topic modeling to capture both strengths and risks, but existing methods struggle to reflect these nuances. Additionally, the brevity of opinions leads to sparse data, which complicates topic extraction. To address these issues, MetaNTM is proposed as a metadata-guided variational neural topic model, which features a variational topic distribution module to manage sparsity and a metadata-guided attention mechanism to integrate rating tendencies. Experiments on Chinese corporate bond data show that MetaNTM outperforms benchmark topic models, achieving higher topic coherence. The topic features also enhance predictive accuracy across multiple bond default prediction models. Logistic regression achieves a 42.0% improvement in H-measure (from 0.355 to 0.504), random forest records a 12.9% increase in KS (from 0.605 to 0.683) and a 22.2% rise in H-measure (from 0.414 to 0.506), and XGBoost achieves a 7.2% increase in H-measure (from 0.581 to 0.623). These results underscore the predictive power of MetaNTM's topic features.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122219"},"PeriodicalIF":8.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hesitant multiplicative best and worst method for multi-criteria group decision making 多准则群体决策的犹豫乘法最佳和最差方法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-21 DOI: 10.1016/j.ins.2025.122214

Shu-Ping Wan , Xi-Nuo Chen , Jiu-Ying Dong , Yu Gao

{"title":"Hesitant multiplicative best and worst method for multi-criteria group decision making","authors":"Shu-Ping Wan , Xi-Nuo Chen , Jiu-Ying Dong , Yu Gao","doi":"10.1016/j.ins.2025.122214","DOIUrl":"10.1016/j.ins.2025.122214","url":null,"abstract":"<div><div>Best-worst method (BWM) has been extended in various uncertain scenarios owing to fewer comparisons and better reliability. This article utilizes hesitant multiplicative (HM) sets (HMSs) to express reference comparisons (RCs) and develops a novel HM BWM (HMBWM). We first define the multiplicative consistency for HM preference relation (HMPR). A fast and effective approach is designed to derive the priority weights (PWs) from an HMPR. To extend BW into HMPR, the score value of each criterion is computed to identify the best and worst criteria. Then, the PWs are acquired through constructing a 0–1 mixed goal programming model based on the HM RCs (HMRCs). The consistency ratio is given to judge the multiplicative consistency of HMRCs. An approach is proposed to enhance the consistency when the HMRCs are unacceptably consistent. Thereby, a novel HMBWM is proposed. On basis of HMBWM, this article further develops a novel method for group decision making (GDM) with HMPRs. The decision makers’ weights are determined by consistency ratio and the group PWs of alternatives are obtained by minimum relative entropy model. Four examples show that HMBWM possesses higher consistency and the proposed GDM method has stronger distinguishing ability, less computation workload and fewer modifications of elements.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122214"},"PeriodicalIF":8.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced classification of motor imagery EEG signals using spatio-temporal representations 基于时空表征的运动图像脑电信号增强分类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-20 DOI: 10.1016/j.ins.2025.122221

Renjie Lv , Wenwen Chang , Guanghui Yan , Muhammad Tariq Sadiq , Wenchao Nie , Lei Zheng

{"title":"Enhanced classification of motor imagery EEG signals using spatio-temporal representations","authors":"Renjie Lv , Wenwen Chang , Guanghui Yan , Muhammad Tariq Sadiq , Wenchao Nie , Lei Zheng","doi":"10.1016/j.ins.2025.122221","DOIUrl":"10.1016/j.ins.2025.122221","url":null,"abstract":"<div><div>Deep learning has shown promising results in motor imagery brain-computer interfaces. However, most existing methods fail to account for the topological relationships between electrodes and the nonlinear features of electroencephalogram (EEG) signals. To address this, we propose a model combining Gramian Angular Fields (GAF) and Phase-Locking Value (PLV) with a parallel convolutional neural network (CNN). GAF captures time-domain nonlinear features, while PLV represents spatial features based on electrode topology. Comparative experiments between the end-to-end parallel CNN model and the model with spatiotemporal feature representation demonstrate that considering both time-domain correlations and electrode topology significantly enhances model performance. Furthermore, when separately evaluating the temporal and spatial features of EEG signals, the results confirm that jointly considering spatiotemporal features leads to a substantial improvement. On the Physionet dataset, our model achieves an accuracy of 99.73% in binary classification tasks and 83.37% in four-class classification tasks, showing improvement over the comparison algorithms used in the paper.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"714 ","pages":"Article 122221"},"PeriodicalIF":8.1,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foundational theories of hesitant fuzzy sets and hesitant fuzzy information systems and their applications for multi-strength intelligent classifiers 犹豫模糊集和犹豫模糊信息系统的基本理论及其在多强度智能分类器中的应用

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-18 DOI: 10.1016/j.ins.2025.122212

Shizhan Lu , Zeshui Xu , Zhu Fu , Longsheng Cheng , Tongbin Yang

引用次数: 0