{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:48:20Z","timestamp":1774658900916,"version":"3.50.1"},"reference-count":37,"publisher":"Wiley","issue":"27-28","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62403201"],"award-info":[{"award-number":["62403201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2025,12,25]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Large\u2010scale multi\u2010objective optimization problems are widely used in expert systems and applications, which mainly consider the simultaneous optimization of multiple conflicting objectives under large\u2010scale decision variables. Existing methods typically classify decision variables as either convergence\u2010 or diversity\u2010related, neglecting their inherent characteristics and thus failing to balance convergence and diversity effectively. In order to address above issues, this article proposes a Large\u2010scale Multi\u2010objective Dual\u2010population Co\u2010evolutionary Algorithm based on decision variable boundary penalty (LMDCA). The proposed algorithm first uses a boundary penalty based cross decision variable analysis method to quantitatively analyze the decision variables, which can quantify the contribution values of convergence variables on different objective functions for grouping. Then, according to different variable groups, different optimization strategies are adopted to more accurately approximate the Pareto front of each objective. Subsequently, the convergence and diversity populations were constructed by combining the dual\u2010population co\u2010evolutionary framework, in which three strategies of directional restriction of mating choice, environmental selection and information compensation were designed for co\u2010interaction within each of the populations to ensure the integrity of population evolution information. We conducted extensive comparisons with current algorithms on multiple benchmark datasets and real\u2010world problems. The experimental results show that the proposed algorithm is superior to the compared algorithms and exhibits strong competitiveness in practical applications.<\/jats:p>","DOI":"10.1002\/cpe.70443","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T11:29:48Z","timestamp":1763551788000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Large\u2010Scale Multi\u2010Objective Dual\u2010Population Co\u2010Evolutionary Algorithm Based on Decision Variable Boundary Penalty"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0281-0661","authenticated-orcid":false,"given":"Zhou","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering East China University of Science and Technology  Shanghai China"}]},{"given":"Na","family":"Li","sequence":"additional","affiliation":[{"name":"Sinopec Research Institute of Petroleum Processing Co. Ltd.  Beijing China"}]},{"given":"Hu","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering East China University of Science and Technology  Shanghai China"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Sinopec Research Institute of Petroleum Processing Co. Ltd.  Beijing China"}]},{"given":"Xin","family":"Guo","sequence":"additional","affiliation":[{"name":"Sinopec Research Institute of Petroleum Processing Co. Ltd.  Beijing China"}]},{"given":"Ruochen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sinopec Research Institute of Petroleum Processing Co. Ltd.  Beijing China"}]},{"given":"Wenbo","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering East China University of Science and Technology  Shanghai China"}]},{"given":"Weichao","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering East China University of Science and Technology  Shanghai China"}]}],"member":"311","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101622"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2024.103802"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/app15031247"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2024.110151"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120290"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.16182\/j.issn1004731x.joss.21\u20100667"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101262"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45823-6_49"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2017.2704782"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2896002"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2015.2455812"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2016.2600642"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470971"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106120"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2979930"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3063606"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.100626"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.01.048"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.066"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.10.007"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2985081"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2018.2849380"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2022.3183681"},{"key":"e_1_2_9_25_1","first-page":"1601","volume-title":"IEEE Computat Intelligence Soc; Jagiellonian Univ; Polish Acad Sci, Comm Informat; AGH Univ Sci & Technol; Warsaw Univ Technol","author":"Suresh A.","year":"2021"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119559"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3091615"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.35414\/akufemubid.1411831"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.112442"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC60901.2024.10611893"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2013.2281535"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3118593"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00354"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3213006"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2025.3526268"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1080\/17538947.2025.2458024"},{"key":"e_1_2_9_37_1","first-page":"109","volume-title":"Handbook for Designing and Conducting Clinical and Translational Research","author":"Patel H. 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