{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:07:41Z","timestamp":1783901261008,"version":"3.55.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":55,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Internet Things"],"DOI":"10.1007\/s43926-026-00330-w","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T13:12:07Z","timestamp":1779196327000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent distribution network dynamic reconstruction optimization model based on improved multi-objective genetic algorithm"],"prefix":"10.1007","volume":"6","author":[{"given":"Min","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changming","family":"Mo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhua","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juncheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"330_CR1","doi-asserted-by":"publisher","first-page":"10640","DOI":"10.1109\/ACCESS.2022.3144665","volume":"10","author":"M Mahdavi","year":"2022","unstructured":"Mahdavi M, Alhelou HH, Hesamzadeh MR. An efficient stochastic reconfiguration model for distribution systems with uncertain loads. IEEe Access. 2022;10:10640\u201352. https:\/\/doi.org\/10.1109\/ACCESS.2022.3144665.","journal-title":"IEEe Access"},{"key":"330_CR2","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.egyr.2021.01.057","volume":"7","author":"X Wang","year":"2021","unstructured":"Wang X, Liu X, Jian S, Peng X, Yuan H. A distribution network reconfiguration method based on comprehensive analysis of operation scenarios in the long-term time period. Energy Rep. 2021;7:369\u201379. https:\/\/doi.org\/10.1016\/j.egyr.2021.01.057.","journal-title":"Energy Rep"},{"key":"330_CR3","doi-asserted-by":"publisher","first-page":"165618","DOI":"10.1109\/ACCESS.2021.3134872","volume":"9","author":"T Van Tran","year":"2021","unstructured":"Van Tran T, Truong BH, Nguyen TP, Nguyen TA, Duong TL, Vo DN. Reconfiguration of distribution networks with distributed generations using an improved neural network algorithm. IEEe Access. 2021;9:165618\u201347. https:\/\/doi.org\/10.1109\/ACCESS.2021.3134872.","journal-title":"IEEe Access"},{"issue":"3","key":"330_CR4","doi-asserted-by":"publisher","first-page":"637","DOI":"10.35833\/MPCE.2020.000067","volume":"10","author":"RB Navesi","year":"2021","unstructured":"Navesi RB, Nazarpour D, Ghanizadeh R, Alemi P. Switchable capacitor bank coordination and dynamic network reconfiguration for improving operation of distribution network integrated with renewable energy resources. J Mod power Syst clean energy. 2021;10(3):637\u201346. https:\/\/doi.org\/10.35833\/MPCE.2020.000067.","journal-title":"J Mod power Syst clean energy"},{"key":"330_CR5","doi-asserted-by":"publisher","first-page":"72373","DOI":"10.1109\/ACCESS.2021.3051230","volume":"10","author":"X Wang","year":"2021","unstructured":"Wang X, Sheng X, Qiu W, He W, Xu J, Xin Y, Jv J. Fault reconfiguration strategies of active distribution network with uncertain factors for maximum supply capacity enhancement. IEEE Access. 2021;10:72373\u201380. https:\/\/doi.org\/10.1109\/ACCESS.2021.3051230.","journal-title":"IEEE Access"},{"key":"330_CR6","doi-asserted-by":"publisher","first-page":"67186","DOI":"10.1109\/ACCESS.2021.3076670","volume":"9","author":"AM Shaheen","year":"2021","unstructured":"Shaheen AM, El-Sehiemy RA, Kamel S, Elattar EE, Elsayed AM. Improving distribution networks\u2019 consistency by optimal distribution system reconfiguration and distributed generations. IEEE Access. 2021;9:67186\u2013200. https:\/\/doi.org\/10.1109\/ACCESS.2021.3076670.","journal-title":"IEEE Access"},{"issue":"12","key":"330_CR7","doi-asserted-by":"publisher","first-page":"9580","DOI":"10.3390\/su15129580","volume":"15","author":"X Yan","year":"2023","unstructured":"Yan X, Zhang Q. Research on combination of distributed generation placement and dynamic distribution network reconfiguration based on MIBWOA. Sustainability. 2023;15(12):9580. https:\/\/doi.org\/10.3390\/su15129580.","journal-title":"Sustainability"},{"issue":"6","key":"330_CR8","doi-asserted-by":"publisher","first-page":"1827","DOI":"10.3390\/pr11061827","volume":"11","author":"N Tu","year":"2023","unstructured":"Tu N, Fan Z. IMODBO for optimal dynamic reconfiguration in active distribution networks. Processes. 2023;11(6):1827. https:\/\/doi.org\/10.3390\/pr11061827.","journal-title":"Processes"},{"issue":"15","key":"330_CR9","doi-asserted-by":"publisher","first-page":"5589","DOI":"10.3390\/en15155589","volume":"15","author":"H Cai","year":"2022","unstructured":"Cai H, Yuan X, Xiong W, Zheng H, Xu Y, Cai Y, Zhong J. Flexible interconnected distribution network with embedded DC system and its dynamic reconfiguration. Energies. 2022;15(15):5589. https:\/\/doi.org\/10.3390\/en15155589.","journal-title":"Energies"},{"key":"330_CR10","doi-asserted-by":"publisher","first-page":"3910","DOI":"10.1016\/j.egyr.2023.02.082","volume":"9","author":"JA Marquez","year":"2023","unstructured":"Marquez JA, Al-Ja\u2019Afreh MAA, Mokryani G, Kabir S, Campean F, Dao C, Riaz S. Optimal planning and operation of distribution systems using network reconfiguration and flexibility services. Energy Rep. 2023;9:3910\u20139. https:\/\/doi.org\/10.1016\/j.egyr.2023.02.082.","journal-title":"Energy Rep"},{"key":"330_CR11","doi-asserted-by":"publisher","unstructured":"Guerraiche K, Dekhici L, Chatelet E, Zeblah A. Multi-objective electrical power system design optimization using a modified bat algorithm. Energies. 2021;14(13). https:\/\/doi.org\/10.3390\/en14133956.","DOI":"10.3390\/en14133956"},{"key":"330_CR12","doi-asserted-by":"publisher","unstructured":"Abedinia O, Bagheri M. Power distribution optimization based on demand respond with improved multi-objective algorithm in power system planning. Energies. 2021;14(10). https:\/\/doi.org\/10.3390\/en14102961.","DOI":"10.3390\/en14102961"},{"issue":"4","key":"330_CR13","doi-asserted-by":"publisher","first-page":"3021","DOI":"10.1109\/TTE.2021.3066123","volume":"7","author":"D Lawhorn","year":"2021","unstructured":"Lawhorn D, Rallabandi V, Ionel DM. Multi-objective optimization for aircraft power systems using a network graph representation. IEEE Trans Transp Electrification. 2021;7(4):3021\u201331. https:\/\/doi.org\/10.1109\/TTE.2021.3066123.","journal-title":"IEEE Trans Transp Electrification"},{"key":"330_CR14","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.egyr.2021.01.070","volume":"7","author":"Y Chen","year":"2021","unstructured":"Chen Y, Chen C, Ma J, Qiu W, Liu S, Lin Z, Qian M, Zhu L, Zhao D. Multi-objective optimization strategy of multi-sources power system operation based on fuzzy chance constraint programming and improved analytic hierarchy process. Energy Rep. 2021;7:268\u201374. https:\/\/doi.org\/10.1016\/j.egyr.2021.01.070.","journal-title":"Energy Rep"},{"key":"330_CR15","doi-asserted-by":"publisher","first-page":"119245","DOI":"10.1016\/j.apenergy.2022.119245","volume":"318","author":"Q Shi","year":"2022","unstructured":"Shi Q, Li F, Dong J, Olama M, Wang X, Winstead C, Kuruganti T. Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience. Appl Energy. 2022;318:119245. https:\/\/doi.org\/10.1016\/j.apenergy.2022.119245.","journal-title":"Appl Energy"},{"issue":"8","key":"330_CR16","doi-asserted-by":"publisher","first-page":"6541","DOI":"10.1016\/j.aej.2021.12.012","volume":"61","author":"S Malekshah","year":"2022","unstructured":"Malekshah S, Rasouli A, Malekshah Y, Ramezani A, Malekshah A. Reliability-driven distribution power network dynamic reconfiguration in presence of distributed generation by the deep reinforcement learning method. Alexandria Eng J. 2022;61(8):6541\u201356. https:\/\/doi.org\/10.1016\/j.aej.2021.12.012.","journal-title":"Alexandria Eng J"},{"issue":"5","key":"330_CR17","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.35833\/MPCE.2020.000870","volume":"10","author":"H Gao","year":"2021","unstructured":"Gao H, Ma W, Xiang Y, Tang Z, Xu X, Pan H, Zhang F, Liu J. Multi-objective dynamic reconfiguration for urban distribution network considering multi-level switching modes. J Mod Power Syst Clean Energy. 2021;10(5):1241\u201355. https:\/\/doi.org\/10.35833\/MPCE.2020.000870.","journal-title":"J Mod Power Syst Clean Energy"},{"issue":"3","key":"330_CR18","doi-asserted-by":"publisher","first-page":"3723","DOI":"10.1109\/JSYST.2021.3135716","volume":"16","author":"SF Santos","year":"2022","unstructured":"Santos SF, Gough M, Fitiwi DZ, Pogeira J, Shafie-khah M, Catal\u00e3o JP. Dynamic distribution system reconfiguration considering distributed renewable energy sources and energy storage systems. IEEE Syst J. 2022;16(3):3723\u201333. https:\/\/doi.org\/10.1109\/JSYST.2021.3135716.","journal-title":"IEEE Syst J"},{"key":"330_CR19","doi-asserted-by":"publisher","first-page":"106355","DOI":"10.1016\/j.ijepes.2020.106355","volume":"124","author":"Q Shi","year":"2021","unstructured":"Shi Q, Li F, Olama M, Dong J, Xue Y, Starke M, Winstead C, Kuruganti T. Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience. Int J Electr Power Energy Syst. 2021;124:106355. https:\/\/doi.org\/10.1016\/j.ijepes.2020.106355.","journal-title":"Int J Electr Power Energy Syst"},{"issue":"2","key":"330_CR20","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1109\/TPWRD.2021.3070796","volume":"37","author":"SM Razavi","year":"2021","unstructured":"Razavi SM, Momeni HR, Haghifam MR, Bolouki S. Multi-objective optimization of distribution networks via daily reconfiguration. IEEE Trans Power Delivery. 2021;37(2):775\u201385. https:\/\/doi.org\/10.1109\/TPWRD.2021.3070796.","journal-title":"IEEE Trans Power Delivery"},{"key":"330_CR21","doi-asserted-by":"publisher","first-page":"216873","DOI":"10.1109\/ACCESS.2020.3041398","volume":"8","author":"J Wang","year":"2020","unstructured":"Wang J, Wang W, Wang H, Zuo H. Dynamic reconfiguration of multiobjective distribution networks considering DG and EVs based on a novel LDBAS algorithm. IEEE access. 2020;8:216873\u201393. https:\/\/doi.org\/10.1109\/ACCESS.2020.3041398.","journal-title":"IEEE Access"},{"key":"330_CR22","doi-asserted-by":"publisher","first-page":"200932","DOI":"10.1109\/ACCESS.2020.3035791","volume":"8","author":"Q Chen","year":"2020","unstructured":"Chen Q, Wang W, Wang H, Wu J, Wang J. An improved beetle swarm algorithm based on social learning for a game model of multiobjective distribution network reconfiguration. IEEE Access. 2020;8:200932\u201352. . https:\/\/doi.org\/10.1109\/ACCESS.2020.3035791","journal-title":"IEEE Access"},{"issue":"3","key":"330_CR23","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/s40565-018-0480-7","volume":"7","author":"A Landeros","year":"2019","unstructured":"Landeros A, Koziel S, Abdel-Fattah MF. Distribution network reconfiguration using feasibility-preserving evolutionary optimization. J Mod Power Syst Clean Energy. 2019;7(3):589\u201398. https:\/\/doi.org\/10.1007\/s40565-018-0480-7.","journal-title":"J Mod Power Syst Clean Energy"},{"issue":"7","key":"330_CR24","doi-asserted-by":"publisher","first-page":"3092","DOI":"10.3390\/app11073092","volume":"11","author":"O Kahouli","year":"2021","unstructured":"Kahouli O, Alsaif H, Bouteraa Y, Ben Ali N, Chaabene M. Power system reconfiguration in distribution network for improving reliability using genetic algorithm and particle swarm optimization. Appl Sci. 2021;11(7):3092. https:\/\/doi.org\/10.3390\/app11073092.","journal-title":"Appl Sci"},{"key":"330_CR25","doi-asserted-by":"publisher","unstructured":"Qays MO, Ahmad I, Habibi D, Masoum MA, Mahmoud T. Addressing system strength and reliability concerns in renewable energy-based weak grids using synchronous condensers determined by hybrid GRU-classical optimization method. Sustainable Energy Grids Networks. 2025;102111. https:\/\/doi.org\/10.1016\/j.segan.2025.102111.","DOI":"10.1016\/j.segan.2025.102111"},{"issue":"1","key":"330_CR26","doi-asserted-by":"publisher","first-page":"e70022","DOI":"10.1049\/stg2.70022","volume":"8","author":"MO Qays","year":"2025","unstructured":"Qays MO, Ahmad I, Habibi D, Masoum MA. Assessing techno-economic performance of synchronous condensers and static synchronous compensators in renewable energy\u2010integrated weak\u2010grids using hedge feedforward feedback\u2010based online gated recurrent unit. IET Smart Grid. 2025;8(1):e70022. https:\/\/doi.org\/10.1049\/stg2.70022.","journal-title":"IET Smart Grid"},{"key":"330_CR27","doi-asserted-by":"publisher","first-page":"104294","DOI":"10.1016\/j.seta.2025.104294","volume":"76","author":"MO Qays","year":"2025","unstructured":"Qays MO, Ahmad I, Habibi D, Moses P. Long-term techno-economic analysis considering system strength and reliability shortfalls of electric vehicle-to-grid-systems installations integrated with renewable energy generators using hybrid GRU-classical optimization method. Sustain Energy Technol Assess. 2025;76:104294. https:\/\/doi.org\/10.1016\/j.seta.2025.104294.","journal-title":"Sustain Energy Technol Assess"},{"issue":"1","key":"330_CR28","doi-asserted-by":"publisher","first-page":"43","DOI":"10.37936\/ecti-eec.2021191.222330","volume":"19","author":"Y Mousavi","year":"2021","unstructured":"Mousavi Y, Atazadegan MH, Mousavi A. Multi-objective power distribution network reconfiguration using chaotic fractional particle swarm optimization. ECTI Trans Electr Eng Electron Commun. 2021;19(1):43\u201350. https:\/\/doi.org\/10.37936\/ecti-eec.2021191.222330.","journal-title":"ECTI Trans Electr Eng Electron Commun"},{"key":"330_CR29","doi-asserted-by":"publisher","first-page":"pp82880","DOI":"10.1109\/ACCESS.2024.3412991","volume":"12","author":"A Mousavi","year":"2024","unstructured":"Mousavi A, Mousavi R, Mousavi Y, Tavasoli M, Arab A, Fekih A. Artificial neural networks-based fault localization in distributed generation integrated networks considering fault impedance. IEEE access. 2024;12:pp82880\u201382896. https:\/\/doi.org\/10.1109\/ACCESS.2024.3412991.","journal-title":"IEEE access"},{"key":"330_CR30","doi-asserted-by":"publisher","first-page":"125296","DOI":"10.1016\/j.apenergy.2025.125296","volume":"382","author":"R Mousavi","year":"2025","unstructured":"Mousavi R, Mousavi A, Mousavi Y, Tavasoli M, Arab A, Kucukdemiral IB, Alfi A, Fekih A. Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency. Appl Energy. 2025;382:125296. https:\/\/doi.org\/10.1016\/j.apenergy.2025.125296.","journal-title":"Appl Energy"},{"key":"330_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-026-11511-y","author":"Y Mousavi","year":"2026","unstructured":"Mousavi Y, Akbari P, Mousavi R, Mousavi A, Kucukdemiral IB, Fekih A, Cali U. QRL-AFOFA: Q-learning enhanced self-adaptive fractional order firefly algorithm for large-scale and dynamic multiobjective optimization problems. Artif Intell Rev. 2026. https:\/\/doi.org\/10.1007\/s10462-026-11511-y.","journal-title":"Artif Intell Rev"},{"key":"330_CR32","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.resconrec.2016.12.001","volume":"128","author":"H Piroozfard","year":"2018","unstructured":"Piroozfard H, Wong KY, Wong WP. Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resour Conserv Recycl. 2018;128:267\u201383. https:\/\/doi.org\/10.1016\/j.resconrec.2016.12.001.","journal-title":"Resour Conserv Recycl"},{"key":"330_CR33","doi-asserted-by":"publisher","unstructured":"Thaher T, Awad M, Sheta A, Aldasht M. 2023, June. Enhanced capuchin search algorithm using cooperative island model with application of evolutionary feedforward neural networks. In 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS). IEEE. 2023;237\u2013245 https:\/\/doi.org\/10.1109\/ICCNS58795.2023.10193500","DOI":"10.1109\/ICCNS58795.2023.10193500"}],"container-title":["Discover Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43926-026-00330-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-026-00330-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-026-00330-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T23:13:12Z","timestamp":1783897992000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43926-026-00330-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,19]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["330"],"URL":"https:\/\/doi.org\/10.1007\/s43926-026-00330-w","relation":{},"ISSN":["2730-7239"],"issn-type":[{"value":"2730-7239","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,19]]},"assertion":[{"value":"13 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable. This study does not involve human participants or animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"91"}}