Pub Date : 2026-02-04DOI: 10.1109/tcyb.2024.3514833
Haibao Tian, Xiuxian Li, Shanying Zhu
{"title":"Nonconvex Federated Composite Optimization With Random Reshuffling and Biased Compression","authors":"Haibao Tian, Xiuxian Li, Shanying Zhu","doi":"10.1109/tcyb.2024.3514833","DOIUrl":"https://doi.org/10.1109/tcyb.2024.3514833","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"215 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115896","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}
Pub Date : 2026-02-04DOI: 10.1109/tcyb.2026.3652224
Zeci Chen, Wenwu Yu, Qingshan Liu
{"title":"A Fixed Step-Size Algorithm for Distributed Optimization With Both Globally Coupled and Locally Separated Constraints","authors":"Zeci Chen, Wenwu Yu, Qingshan Liu","doi":"10.1109/tcyb.2026.3652224","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3652224","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"288 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115983","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}
Pub Date : 2026-02-03DOI: 10.1109/TCYB.2026.3652053
{"title":"IEEE Transactions on Cybernetics Information for Authors","authors":"","doi":"10.1109/TCYB.2026.3652053","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3652053","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"56 2","pages":"C4-C4"},"PeriodicalIF":10.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11371485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1109/tcyb.2026.3651182
Irfan Ganie, Sarangapani Jagannathan
{"title":"Safe Optimal Control Framework for Cooperative Manipulation of Objects in Human–Robot Teams","authors":"Irfan Ganie, Sarangapani Jagannathan","doi":"10.1109/tcyb.2026.3651182","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3651182","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"6 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110398","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}
Pub Date : 2026-02-03DOI: 10.1109/TCYB.2026.3652055
{"title":"IEEE Transactions on Cybernetics","authors":"","doi":"10.1109/TCYB.2026.3652055","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3652055","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"56 2","pages":"C3-C3"},"PeriodicalIF":10.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11371481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1109/tcyb.2026.3656420
Yao Li, Chengpu Yu, Renshuo Cheng, Fang Deng, Jie Chen
{"title":"Inverse Dynamic Games With Process Noise and Unknown Target States: A Linear Estimation Approach","authors":"Yao Li, Chengpu Yu, Renshuo Cheng, Fang Deng, Jie Chen","doi":"10.1109/tcyb.2026.3656420","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3656420","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"100 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110399","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}
Pub Date : 2026-02-02DOI: 10.1109/tcyb.2026.3656946
Bing Sun, Wei-Jie Yu, Xiao-Fang Liu, Jinghui Zhong, Jian-Yu Li, Zhi-Hui Zhan, Sam Kwong, Jun Zhang
{"title":"Tensor-Based Ant Colony Optimization for Set Meal Design in Online-to-Offline Restaurants","authors":"Bing Sun, Wei-Jie Yu, Xiao-Fang Liu, Jinghui Zhong, Jian-Yu Li, Zhi-Hui Zhan, Sam Kwong, Jun Zhang","doi":"10.1109/tcyb.2026.3656946","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3656946","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101330","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}
Optimization problems in real-world applications often involve dynamic environmental changes, requiring algorithms to adapt quickly, track optimal solutions, and maintain efficiency. Existing dynamic multiobjective optimization evolutionary algorithms (DMOEAs) typically rely on fixed or limited dynamic response mechanisms, which are often insufficient to handle complex and varied dynamic environments. To overcome these limitations, this article proposes an adaptive dynamic response-based DMOEA (ADR-DMOEA), which employs a subpopulation-level adaptive mechanism to coordinate diversity-driven, prediction-driven, and memory-driven strategies. The strategy weights are dynamically adjusted according to the static optimization distance of each subpopulation, ensuring that appropriate strategies are adaptively deployed in different environments. This design overcomes the inefficiency of fixed assignments and the instability of individual-level perturbations, enabling coordinated and stable evolution. Extensive experiments on DF benchmark functions and a blast furnace (BF) ironmaking case study demonstrate that ADR-DMOEA achieves superior convergence, diversity, and robustness compared to state-of-the-art algorithms, effectively supporting real-world decision-making under dynamic conditions.
{"title":"ADR-DMOEA: A Dynamic Multiobjective Optimization Evolutionary Algorithm Based on Adaptive Dynamic Response Strategy.","authors":"Yuying Wang,Ping Zhou,Shengxiang Yang,Tianyou Chai","doi":"10.1109/tcyb.2026.3652642","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3652642","url":null,"abstract":"Optimization problems in real-world applications often involve dynamic environmental changes, requiring algorithms to adapt quickly, track optimal solutions, and maintain efficiency. Existing dynamic multiobjective optimization evolutionary algorithms (DMOEAs) typically rely on fixed or limited dynamic response mechanisms, which are often insufficient to handle complex and varied dynamic environments. To overcome these limitations, this article proposes an adaptive dynamic response-based DMOEA (ADR-DMOEA), which employs a subpopulation-level adaptive mechanism to coordinate diversity-driven, prediction-driven, and memory-driven strategies. The strategy weights are dynamically adjusted according to the static optimization distance of each subpopulation, ensuring that appropriate strategies are adaptively deployed in different environments. This design overcomes the inefficiency of fixed assignments and the instability of individual-level perturbations, enabling coordinated and stable evolution. Extensive experiments on DF benchmark functions and a blast furnace (BF) ironmaking case study demonstrate that ADR-DMOEA achieves superior convergence, diversity, and robustness compared to state-of-the-art algorithms, effectively supporting real-world decision-making under dynamic conditions.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146069935","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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