IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第2页

IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information IEEE系统、人与控制论汇刊：系统出版信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-17 DOI: 10.1109/TSMC.2025.3575151

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IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-17 DOI: 10.1109/TSMC.2025.3575153

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TechRxiv: Share Your Preprint Research With the World! techxiv：与世界分享你的预印本研究！

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-17 DOI: 10.1109/TSMC.2025.3575177

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Attack Detection and Security Control for Advanced Cyber-Physical Systems and Its Applications 先进网络物理系统的攻击检测与安全控制及其应用

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-17 DOI: 10.1109/TSMC.2025.3574771

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IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-17 DOI: 10.1109/TSMC.2025.3575147

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IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-17 DOI: 10.1109/TSMC.2025.3573476

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A Multiform Framework for Multiobjective Feature Selection in Unbalanced Classification: Combining Oversampling and Cost-Sensitive Learning 非平衡分类中多目标特征选择的多形式框架：结合过采样和代价敏感学习

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-10 DOI: 10.1109/TSMC.2025.3573080

Jing Liang;Yu-Yang Zhang;Boyang Qu;Ke Chen;Kunjie Yu;Caitong Yue

{"title":"A Multiform Framework for Multiobjective Feature Selection in Unbalanced Classification: Combining Oversampling and Cost-Sensitive Learning","authors":"Jing Liang;Yu-Yang Zhang;Boyang Qu;Ke Chen;Kunjie Yu;Caitong Yue","doi":"10.1109/TSMC.2025.3573080","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3573080","url":null,"abstract":"Unbalanced classification problems have attracted significant academic attention due to their widespread existence in the real world. The lack of recognition accuracy of minority class samples and the “curse of dimensionality” are two major difficulties in unbalanced classification problems. Existing unbalanced classification methods run the risk of losing the original feature information and are prone to bias toward the majority class. Multiform optimization is famous for capturing useful knowledge from alternative forms to help solve the original task. Motivated by this, this article introduces a multiform evolutionary framework that addresses the issue of multiobjective feature selection in unbalanced classification scenarios. It aims to utilize the advanced experience of selecting features on balanced datasets to assist in the search for feature subsets that can more accurately identify minority classes on the original dataset. Specifically, a knowledge transfer strategy is proposed to draw on the search experience of the auxiliary task from the oversampled dataset to help the cost-sensitive learning task based on the original dataset jump out of the local optimum. In addition, an offspring repairing mechanism is proposed to filter redundant features by considering the frequency of selected features. Experimental results on 23 real-world benchmark datasets demonstrate that the proposed method can select fewer features and achieve better classification results compared to six state-of-the-art multiobjective feature selection algorithms and three classical oversampling algorithms. Furthermore, the difference in performance of four base classifiers is investigated through a series of comparative experiments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5717-5729"},"PeriodicalIF":8.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657422","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

Twin Trust Region Policy Optimization 双信任域策略优化

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-10 DOI: 10.1109/TSMC.2025.3573513

Haotian Xu;Junyu Xuan;Guangquan Zhang;Jie Lu

{"title":"Twin Trust Region Policy Optimization","authors":"Haotian Xu;Junyu Xuan;Guangquan Zhang;Jie Lu","doi":"10.1109/TSMC.2025.3573513","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3573513","url":null,"abstract":"Trust region policy optimization (TRPO) is an iterative reinforcement learning algorithm that both maximizes a surrogate objective based on a generalized advantage function and enforces a trust region constraint between two consecutive policies based on the Kullback-Leibler divergence in each iteration. On the one hand, its surrogate objective only approximates the positive theoretical improvement and thus relaxes the theoretical guarantee. One the other hand, currently, TRPO is approximately solved via a linear approximation for its objective and a quadratic approximation for its constraint, which results in an upper bound for the linear search of the step size. However, this solution strategy does not provide a lower bound for the step size search. Intuitively, a lower bound could be determined manually, but such a setting has no physical meaning. To this end, we present an alternative solution in this article, which is to exchange the objective and the constraint in TRPO according to reciprocal optimization technique, and then derive a reciprocal TRPO. Applying a similar approximation solution of TRPO to our reciprocal TRPO produces a lower bound for the step size search that has a physical interpretation for improving the surrogate objective or return. Further, we aggregate the original TRPO with this reciprocal TRPO to construct a twin TRPO, whose step size has a lower bound from our reciprocal TRPO and an upper bound from TRPO, to facilitate the policy optimization and achieve a least return improvement. Extensive experiments on twelve benchmark environments show that our twin TRPO is superior to six existing techniques: the original TRPO, proximal policy optimization, off-policy TRPO and two entropy regularized TRPOs, as well as our reciprocal TRPO. Our code is available at <uri>https://github.com/HTXu-UTS/twinTRPO</uri>.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5422-5436"},"PeriodicalIF":8.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663732","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}

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Energy-Efficiency Oriented Distributed Heterogeneous Hybrid Flow Shop Scheduling With Multilevelled Mixed-Model Assembly 面向能效的多层次混合模型装配分布式异构混合流水车间调度

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-10 DOI: 10.1109/TSMC.2025.3572378

Weishi Shao;Zhongshi Shao;Dechang Pi;Jiaquan Gao

{"title":"Energy-Efficiency Oriented Distributed Heterogeneous Hybrid Flow Shop Scheduling With Multilevelled Mixed-Model Assembly","authors":"Weishi Shao;Zhongshi Shao;Dechang Pi;Jiaquan Gao","doi":"10.1109/TSMC.2025.3572378","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3572378","url":null,"abstract":"This article studies an energy-efficient scheduling problem in a two-stage manufacturing system with distributed heterogeneous hybrid flow shops and mixed-model assembly lines (EDHHFSP-MMAL). A mixed-integer linear programming model is proposed that simultaneously optimizes total tardiness and energy consumption (including operational, idle, and common energy components). To solve this multiobjective problem, a learning competitive swarm optimizer (LCSO) is proposed that integrates two novel mechanisms: 1) environmental-competitive learning through probability models capturing product-task relationships and 2) comprehensive learning utilizing reinforcement learning to guide local search based on nondominated solution states. The hybrid approach balances convergence speed and solution diversity by combining solution-space and policy-space learning perspectives. Experimental results demonstrate LCSO’s superior performance over compared methods, achieving 25% improvement in energy-time tradeoff compared to other state-of-the-art multiobjective optimizers in solving related problems. The proposed method particularly excels in optimizing complex energy-time tradeoffs while maintaining better solution diversity and convergence across different problem scales.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5581-5595"},"PeriodicalIF":8.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657416","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

Safety-Aware Pursuit-Evasion Game Based on Control Barrier Function and Reinforcement Learning 基于控制障碍函数和强化学习的安全意识追逃博弈

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-10 DOI: 10.1109/TSMC.2025.3546968

Yupeng Jia;Xiran Cui;Yi Dong;Xiaoming Hu

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