{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T18:45:31Z","timestamp":1782931531281,"version":"3.54.5"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The National key research and development program","award":["2022YFB3705504"],"award-info":[{"award-number":["2022YFB3705504"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["2073276,62273295"],"award-info":[{"award-number":["2073276,62273295"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the key research and development program in Hebei","award":["21310301D"],"award-info":[{"award-number":["21310301D"]}]},{"name":"Provincial Key Laboratory Performance  Subsidy Project","award":["No.22567619H"],"award-info":[{"award-number":["No.22567619H"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07471-9","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T08:12:39Z","timestamp":1749456759000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Solving non-cyclic dynamic multi-objective optimization problems via GRU prediction and multi-information hybrid exploration"],"prefix":"10.1007","volume":"81","author":[{"given":"Hao","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochuang","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"7471_CR1","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1109\/tevc.2023.3313689","volume":"28","author":"G Chen","year":"2024","unstructured":"Chen G, Guo Y, Wang Y et al (2024) Evolutionary dynamic constrained multiobjective optimization: test suite and algorithm. IEEE Trans Evol Comput 28:1381\u20131395. https:\/\/doi.org\/10.1109\/tevc.2023.3313689","journal-title":"IEEE Trans Evol Comput"},{"key":"7471_CR2","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6:182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"key":"7471_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2025.101883","volume":"94","author":"Y Deng","year":"2025","unstructured":"Deng Y, Zheng Y, Meng Z et al (2025) Gaussian process regression for evolutionary dynamic multiobjective optimization in complex environments. Swarm Evol Comput 94:1\u201311","journal-title":"Swarm Evol Comput"},{"key":"7471_CR4","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/tevc.2004.831456","volume":"8","author":"M Farina","year":"2004","unstructured":"Farina M, Deb K, Amato P (2004) Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans Evol Comput 8:425\u2013442. https:\/\/doi.org\/10.1109\/tevc.2004.831456","journal-title":"IEEE Trans Evol Comput"},{"key":"7471_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2024.120565","volume":"669","author":"J Feng","year":"2024","unstructured":"Feng J, Chen D, Zou F et al (2024) Guided prediction strategy based on regional multi-directional information fusion for dynamic multi-objective optimization. Inf Sci 669:1\u201321","journal-title":"Inf Sci"},{"key":"7471_CR6","doi-asserted-by":"crossref","unstructured":"Gao S, He J, Liao Y, et\u00a0al (2024) A moea\/d framework based on memory differential cooperation mechanism for dynamic multiobjective optimization. In: 2024 25th International Arab Conference on Information Technology (ACIT), IEEE, pp 1\u20138","DOI":"10.1109\/ACIT62805.2024.10877109"},{"issue":"2","key":"7471_CR7","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1109\/TEVC.2023.3261134","volume":"28","author":"YR Gu","year":"2023","unstructured":"Gu YR, Bian C, Li M et al (2023) Subset selection for evolutionary multiobjective optimization. IEEE Trans Evol Comput 28(2):403\u2013417","journal-title":"IEEE Trans Evol Comput"},{"key":"7471_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119627","author":"X Hou","year":"2023","unstructured":"Hou X, Ge FZ, Chen DB et al (2023) Temporal distribution-based prediction strategy for dynamic multi-objective optimization assisted by gru neural network. Inf Sci. https:\/\/doi.org\/10.1016\/j.ins.2023.119627","journal-title":"Inf Sci"},{"key":"7471_CR9","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1109\/tevc.2023.3253850","volume":"28","author":"Y Hu","year":"2024","unstructured":"Hu Y, Zheng J, Jiang S et al (2024) A mahalanobis distance-based approach for dynamic multiobjective optimization with stochastic changes. IEEE Trans Evol Comput 28:238\u2013251. https:\/\/doi.org\/10.1109\/tevc.2023.3253850","journal-title":"IEEE Trans Evol Comput"},{"key":"7471_CR10","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.isatra.2020.02.024","volume":"102","author":"Z Hu","year":"2020","unstructured":"Hu Z, Wei Z, Ma X et al (2020) Multi-parameter deep-perception and many-objective autonomous-control of rolling schedule on high speed cold tandem mill. ISA Trans 102:193\u2013207. https:\/\/doi.org\/10.1016\/j.isatra.2020.02.024","journal-title":"ISA Trans"},{"key":"7471_CR11","doi-asserted-by":"publisher","first-page":"17749","DOI":"10.1007\/s00521-023-08633-7","volume":"35","author":"Z Hu","year":"2023","unstructured":"Hu Z, Li Z, Wei L et al (2023) A dynamic multiobjective optimization algorithm based on decision variable relationship. Neural Comput Appl 35:17749\u201317775. https:\/\/doi.org\/10.1007\/s00521-023-08633-7","journal-title":"Neural Comput Appl"},{"key":"7471_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2025.122018","volume":"707","author":"J Ji","year":"2025","unstructured":"Ji J, Zhang X, Yang C et al (2025) A similar environment transfer strategy for dynamic multiobjective optimization. Inf Sci 707:1\u201322","journal-title":"Inf Sci"},{"issue":"1","key":"7471_CR13","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TEVC.2020.3004027","volume":"25","author":"M Jiang","year":"2020","unstructured":"Jiang M, Wang Z, Hong H et al (2020) Knee point-based imbalanced transfer learning for dynamic multiobjective optimization. IEEE Trans Evol Comput 25(1):117\u2013129","journal-title":"IEEE Trans Evol Comput"},{"key":"7471_CR14","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/tcyb.2015.2510698","volume":"47","author":"S Jiang","year":"2017","unstructured":"Jiang S, Yang S (2017) Evolutionary dynamic multiobjective optimization: benchmarks and algorithm comparisons. IEEE Trans Cybern 47:198\u2013211. https:\/\/doi.org\/10.1109\/tcyb.2015.2510698","journal-title":"IEEE Trans Cybern"},{"key":"7471_CR15","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/tevc.2016.2574621","volume":"21","author":"S Jiang","year":"2017","unstructured":"Jiang S, Yang S (2017) A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization. IEEE Trans Evol Comput 21:65\u201382. https:\/\/doi.org\/10.1109\/tevc.2016.2574621","journal-title":"IEEE Trans Evol Comput"},{"key":"7471_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3524495","volume":"55","author":"S Jiang","year":"2022","unstructured":"Jiang S, Zou J, Yang S et al (2022) Evolutionary dynamic multi-objective optimisation: a survey. ACM Comput Surv 55:1\u201347. https:\/\/doi.org\/10.1145\/3524495","journal-title":"ACM Comput Surv"},{"issue":"2","key":"7471_CR17","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1109\/TCSS.2023.3293331","volume":"11","author":"H Li","year":"2023","unstructured":"Li H, Wang Z, Lan C et al (2023) A novel dynamic multiobjective optimization algorithm with hierarchical response system. IEEE Trans Comput Soc Syst 11(2):2494\u20132512","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"7471_CR18","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1016\/j.ins.2022.06.095","volume":"608","author":"J Li","year":"2022","unstructured":"Li J, Liu R, Wang R (2022) Handling dynamic multiobjective optimization problems with variable environmental change via classification prediction and dynamic mutation. Inf Sci 608:970\u2013995. https:\/\/doi.org\/10.1016\/j.ins.2022.06.095","journal-title":"Inf Sci"},{"key":"7471_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117380","volume":"203","author":"R Li","year":"2022","unstructured":"Li R, Gong W, Lu C (2022) A reinforcement learning based rmoea\/d for bi-objective fuzzy flexible job shop scheduling. Expert Syst Appl 203:117380. https:\/\/doi.org\/10.1016\/j.eswa.2022.117380","journal-title":"Expert Syst Appl"},{"key":"7471_CR20","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.isatra.2021.01.053","volume":"117","author":"X Li","year":"2021","unstructured":"Li X, Yang J, Sun H et al (2021) A dual prediction strategy with inverse model for evolutionary dynamic multiobjective optimization. ISA Trans 117:196\u2013209. https:\/\/doi.org\/10.1016\/j.isatra.2021.01.053","journal-title":"ISA Trans"},{"key":"7471_CR21","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ins.2019.01.066","volume":"485","author":"Z Liang","year":"2019","unstructured":"Liang Z, Zheng S, Zhu Z et al (2019) Hybrid of memory and prediction strategies for dynamic multiobjective optimization. Inf Sci 485:200\u2013218. https:\/\/doi.org\/10.1016\/j.ins.2019.01.066","journal-title":"Inf Sci"},{"key":"7471_CR22","doi-asserted-by":"publisher","first-page":"7441","DOI":"10.1109\/tsmc.2023.3298804","volume":"53","author":"XF Liu","year":"2023","unstructured":"Liu XF, Zhang J, Wang J (2023) Cooperative differential evolution with an attention-based prediction strategy for dynamic multiobjective optimization. IEEE Trans Syst Man Cybern Syst 53:7441\u20137452. https:\/\/doi.org\/10.1109\/tsmc.2023.3298804","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"7471_CR23","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1016\/j.ejor.2021.01.028","volume":"295","author":"X Ma","year":"2021","unstructured":"Ma X, Yang J, Sun H et al (2021) Feature information prediction algorithm for dynamic multi-objective optimization problems. Eur J Oper Res 295:965\u2013981. https:\/\/doi.org\/10.1016\/j.ejor.2021.01.028","journal-title":"Eur J Oper Res"},{"key":"7471_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.07.009","volume":"545","author":"X Ma","year":"2021","unstructured":"Ma X, Yang J, Sun H et al (2021) Multiregional co-evolutionary algorithm for dynamic multiobjective optimization. Inf Sci 545:1\u201324. https:\/\/doi.org\/10.1016\/j.ins.2020.07.009","journal-title":"Inf Sci"},{"key":"7471_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101356","author":"H Peng","year":"2023","unstructured":"Peng H, Mei C, Zhang S et al (2023) Multi-strategy dynamic multi-objective evolutionary algorithm with hybrid environmental change responses. Swarm Evolut Comput. https:\/\/doi.org\/10.1016\/j.swevo.2023.101356","journal-title":"Swarm Evolut Comput"},{"key":"7471_CR26","doi-asserted-by":"publisher","first-page":"3362","DOI":"10.1109\/tcyb.2018.2842158","volume":"49","author":"M Rong","year":"2019","unstructured":"Rong M, Gong D, Zhang Y et al (2019) Multidirectional prediction approach for dynamic multiobjective optimization problems. IEEE Trans Cybern 49:3362\u20133374. https:\/\/doi.org\/10.1109\/tcyb.2018.2842158","journal-title":"IEEE Trans Cybern"},{"key":"7471_CR27","unstructured":"Sch\u00fctze O, Rodriguez-Fernandez AE, Segura C, et\u00a0al (2024) Finding the set of nearly optimal solutions of a multi-objective optimization problem. IEEE Trans Evolut Comput"},{"key":"7471_CR28","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1016\/j.ins.2021.08.027","volume":"579","author":"H Sun","year":"2021","unstructured":"Sun H, Cao A, Hu Z et al (2021) A novel quantile-guided dual prediction strategies for dynamic multi-objective optimization. Inf Sci 579:751\u2013775. https:\/\/doi.org\/10.1016\/j.ins.2021.08.027","journal-title":"Inf Sci"},{"key":"7471_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121538","author":"C Wang","year":"2024","unstructured":"Wang C, Sun H, Wei L et al (2024) A novel knee-guided algorithm based on frequency analysis for non-cyclic dynamic multiobjective optimization problems. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2023.121538","journal-title":"Expert Syst Appl"},{"issue":"15","key":"7471_CR30","doi-asserted-by":"publisher","first-page":"18398","DOI":"10.1007\/s10489-022-04429-9","volume":"53","author":"P Wang","year":"2023","unstructured":"Wang P, Ma Y (2023) A dynamic multiobjective evolutionary algorithm based on fine prediction strategy and nondominated solutions-guided evolution. Appl Intell 53(15):18398\u201318419","journal-title":"Appl Intell"},{"issue":"1","key":"7471_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11227-024-06558-z","volume":"81","author":"Y Wang","year":"2025","unstructured":"Wang Y, Ma Y, Li Q et al (2025) A dynamic multi-objective optimization evolutionary algorithm based on classification of environmental change intensity and collaborative prediction strategy. J Supercomput 81(1):1\u201352","journal-title":"J Supercomput"},{"key":"7471_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101385","author":"J Yang","year":"2023","unstructured":"Yang J, Zou J, Yang S et al (2023) A particle swarm algorithm based on the dual search strategy for dynamic multi-objective optimization. Swarm Evolut Comput. https:\/\/doi.org\/10.1016\/j.swevo.2023.101385","journal-title":"Swarm Evolut Comput"},{"key":"7471_CR33","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1109\/tsmc.2022.3207457","volume":"53","author":"S Yang","year":"2023","unstructured":"Yang S, Huang H, Luo F et al (2023) Local-diversity evaluation assignment strategy for decomposition-based multiobjective evolutionary algorithm. IEEE Trans Syst Man Cybern Syst 53:1697\u20131709. https:\/\/doi.org\/10.1109\/tsmc.2022.3207457","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"7471_CR34","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"QF Zhang","year":"2007","unstructured":"Zhang QF, Li H (2007) Moea\/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11:712\u2013731. https:\/\/doi.org\/10.1109\/TEVC.2007.892759","journal-title":"IEEE Trans Evol Comput"},{"key":"7471_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2023.101284","volume":"78","author":"J Zheng","year":"2023","unstructured":"Zheng J, Zhou F, Zou J et al (2023) A dynamic multi-objective optimization based on a hybrid of pivot points prediction and diversity strategies. Swarm Evol Comput 78:1\u201315","journal-title":"Swarm Evol Comput"},{"key":"7471_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120398","author":"F Zou","year":"2024","unstructured":"Zou F, Yen GG (2024) Dynamic multiobjective optimization with varying number of objectives assisted by dynamic principal component analysis. Inf Sci. https:\/\/doi.org\/10.1016\/j.ins.2024.120398","journal-title":"Inf Sci"},{"key":"7471_CR37","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.ins.2019.09.016","volume":"509","author":"F Zou","year":"2020","unstructured":"Zou F, Yen GG, Tang L (2020) A knee-guided prediction approach for dynamic multi-objective optimization. Inf Sci 509:193\u2013209. https:\/\/doi.org\/10.1016\/j.ins.2019.09.016","journal-title":"Inf Sci"},{"key":"7471_CR38","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1016\/j.asoc.2017.08.004","volume":"61","author":"J Zou","year":"2017","unstructured":"Zou J, Li Q, Yang S et al (2017) A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization. Appl Soft Comput 61:806\u2013818. https:\/\/doi.org\/10.1016\/j.asoc.2017.08.004","journal-title":"Appl Soft Comput"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07471-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07471-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07471-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T08:12:42Z","timestamp":1749456762000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07471-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,9]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["7471"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07471-9","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,9]]},"assertion":[{"value":"16 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Manuscript does not report on involve any animals, human, human data, human tissue or plants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The code that supports the findings of this study is available upon reasonable request from the corresponding author.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}],"article-number":"991"}}