{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:26:27Z","timestamp":1775118387225,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:00:00Z","timestamp":1736553600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["62133015"],"award-info":[{"award-number":["62133015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["62273348"],"award-info":[{"award-number":["62273348"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The operational optimization of the coal mine integrated energy system (CMIES) is crucial for reducing costs and carbon emissions. However, the system\u2019s multi-objective nature, stringent constraints, and the uncertainty of renewable and mine-derived energy make solving its optimization challenging. Thus, this paper first presents a data-driven uncertainty transformation method to address the uncertainty of renewable energy and mining derived energy output; then, a multi-task multi-objective evolutionary algorithm based on adaptive auxiliary tasks (MMOEA-AS) is proposed, which includes a main task and three auxiliary tasks. Meanwhile, an adaptive update strategy for auxiliary tasks and a matching degree-guided knowledge transfer mechanism are proposed to improve the performance of the algorithm. Finally, taking the energy scheduling problem of a coal mine in Shanxi, China as an example, MMOEA-AS is compared with five advanced evolutionary algorithms. The results show that MMOEA-AS can effectively solve the operation optimization of the CMIES, and obtain the optimal scheduling results.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf004","type":"journal-article","created":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T12:08:58Z","timestamp":1736597338000},"page":"1-13","source":"Crossref","is-referenced-by-count":5,"title":["A novel multi-task algorithm for operational optimization of coal mine integrated energy system under multiple uncertainties"],"prefix":"10.1093","volume":"12","author":[{"given":"Xiaotian","family":"Fei","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology , Xuzhou, Jiangsu 221116 ,","place":["China"]}]},{"given":"Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, China University of Mining and Technology , Xuzhou, Jiangsu 221116 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