{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T17:31:18Z","timestamp":1793208678713,"version":"4.5.0"},"reference-count":146,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100018512","name":"Univerzita Hradec Kr\u00e1lov\u00e9","doi-asserted-by":"publisher","award":["2226\/2025"],"award-info":[{"award-number":["2226\/2025"]}],"id":[{"id":"10.13039\/100018512","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018512","name":"Univerzita Hradec Kr\u00e1lov\u00e9","doi-asserted-by":"publisher","award":["2226\/2025"],"award-info":[{"award-number":["2226\/2025"]}],"id":[{"id":"10.13039\/100018512","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018512","name":"Univerzita Hradec Kr\u00e1lov\u00e9","doi-asserted-by":"publisher","award":["2226\/2025"],"award-info":[{"award-number":["2226\/2025"]}],"id":[{"id":"10.13039\/100018512","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018512","name":"Univerzita Hradec Kr\u00e1lov\u00e9","doi-asserted-by":"publisher","award":["2226\/2025"],"award-info":[{"award-number":["2226\/2025"]}],"id":[{"id":"10.13039\/100018512","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-21515-3","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T14:46:58Z","timestamp":1761662818000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Cuckoo optimization algorithm via Grey Wolf Optimizer for usage in engineering optimization and optimal power flow with renewable energy sources"],"prefix":"10.1038","volume":"15","author":[{"given":"Rabeh","family":"Abbassi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavel","family":"Trojovsk\u00fd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zulkefli","family":"Mansor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Trojovsk\u00e1","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mourad","family":"Kchaou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom\u00e1\u0161","family":"Zu\u0161\u010d\u00e1k","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houssem","family":"Jerbi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Palanichamy","family":"Naveen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"21515_CR1","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.enconman.2014.06.090","volume":"87","author":"B Mahdad","year":"2014","unstructured":"Mahdad, B. & Srairi, K. Multi objective large power system planning under sever loading condition using learning DE-APSO-PS strategy. Energy Convers. Manag. 87, 338\u2013350 (2014).","journal-title":"Energy Convers. Manag"},{"key":"21515_CR2","doi-asserted-by":"crossref","unstructured":"Ghasemi, M., Zare, M., Mohammadi, S. K. & Mirjalili, S. Applications of whale migration algorithm in optimal power flow problems of power systems. In Handb. Whale Optim. Algorithm 347\u2013364 (Elsevier, 2024).","DOI":"10.1016\/B978-0-32-395365-8.00031-2"},{"key":"21515_CR3","first-page":"369","volume":"67","author":"Y Wang","year":"2021","unstructured":"Wang, Y. et al. Information theoretic weighted fuzzy clustering ensemble. C Mater. Contin. 67, 369\u2013392 (2021).","journal-title":"C Mater. Contin."},{"key":"21515_CR4","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.enconman.2018.10.069","volume":"179","author":"R Abbassi","year":"2019","unstructured":"Abbassi, R., Abbassi, A., Heidari, A. A. & Mirjalili, S. An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers. Manag. 179, 362\u2013372 (2019).","journal-title":"Energy Convers. Manag"},{"key":"21515_CR5","doi-asserted-by":"publisher","first-page":"124435","DOI":"10.1016\/j.jclepro.2020.124435","volume":"282","author":"N Yin","year":"2021","unstructured":"Yin, N., Abbassi, R., Jerbi, H., Rezvani, A. & M\u00fcller, M. A day-ahead joint energy management and battery sizing framework based on \u03b8-modified Krill herd algorithm for a renewable energy-integrated microgrid. J. Clean. Prod. 282, 124435 (2021).","journal-title":"J. Clean. Prod."},{"key":"21515_CR6","doi-asserted-by":"crossref","unstructured":"Alsac, O. & Stott, B. Optimal load flow with steady-state security. IEEE Trans. Power Appar. Syst. 745\u2013751 (1974).","DOI":"10.1109\/TPAS.1974.293972"},{"key":"21515_CR7","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/60.577285","volume":"12","author":"J-F Chen","year":"1997","unstructured":"Chen, J-F. & Chen, S-D. Multiobjective power dispatch with line flow constraints using the fast Newton-Raphson method. IEEE Trans. Energy Convers. 12, 86\u201393 (1997).","journal-title":"IEEE Trans. Energy Convers."},{"key":"21515_CR8","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1177\/0309524X211035152","volume":"46","author":"A Maheshwari","year":"2022","unstructured":"Maheshwari, A. & Sood, Y. R. Solution approach for optimal power flow considering wind turbine and environmental emissions. Wind Eng. 46, 480\u2013502 (2022).","journal-title":"Wind Eng."},{"key":"21515_CR9","doi-asserted-by":"publisher","unstructured":"Ghasemi, M., Ghavidel, S., Aghaei, J., Gitizadeh, M. & Falah, H. Application of chaos-based chaotic invasive weed optimization techniques for environmental OPF problems in the power system. Chaos Solitons Fractals. 69 https:\/\/doi.org\/10.1016\/j.chaos.2014.10.007 (2014).","DOI":"10.1016\/j.chaos.2014.10.007"},{"key":"21515_CR10","doi-asserted-by":"publisher","first-page":"148622","DOI":"10.1109\/ACCESS.2020.3015473","volume":"8","author":"IU Khan","year":"2020","unstructured":"Khan, I. U. et al. Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources. IEEE Access. 8, 148622\u2013148643 (2020).","journal-title":"IEEE Access."},{"key":"21515_CR11","first-page":"101551","volume":"47","author":"M Ghasemi","year":"2023","unstructured":"Ghasemi, M., Trojovsk\u00fd, P., Trojovsk\u00e1, E. & Zare, M. Gaussian bare-bones levy circulatory system-based optimization for power flow in the presence of renewable units. Eng. Sci. Technol. Int. J. 47, 101551 (2023).","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"21515_CR12","doi-asserted-by":"publisher","first-page":"123795","DOI":"10.1016\/j.energy.2022.123795","volume":"250","author":"AM Shaheen","year":"2022","unstructured":"Shaheen, A. M., El-Sehiemy, R. A., Hasanien, H. M. & Ginidi, A. R. An improved heap optimization algorithm for efficient energy management based optimal power flow model. Energy 250, 123795 (2022).","journal-title":"Energy"},{"key":"21515_CR13","doi-asserted-by":"crossref","unstructured":"Pham, L. H., Dinh, B. H. & Nguyen, T. T. Optimal power flow for an integrated wind-solar-hydro-thermal power system considering uncertainty of wind speed and solar radiation. Neural Comput. Appl. 1\u201335 (2022).","DOI":"10.1007\/s00521-022-07000-2"},{"key":"21515_CR14","first-page":"1085","volume":"67","author":"E Heidary","year":"2021","unstructured":"Heidary, E. et al. Automatic text summarization using genetic algorithm and repetitive patterns. Comput. Mater. Contin. 67, 1085\u20131101 (2021).","journal-title":"Comput. Mater. Contin"},{"key":"21515_CR15","doi-asserted-by":"crossref","unstructured":"Azn, M. M., Sadrnia, M. A., Band, S. S. & Bin, M. Z. Smart anti-pinch window simulation using H\u2013\/H\u221e criterion and MOPSO. Comput. Mater. Contin. 72 (2022).","DOI":"10.32604\/cmc.2022.023030"},{"key":"21515_CR16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.asoc.2016.01.041","volume":"42","author":"HREH Bouchekara","year":"2016","unstructured":"Bouchekara, H. R. E. H., Chaib, A. E., Abido, M. A. & El-Sehiemy, R. A. Optimal power flow using an improved colliding bodies optimization algorithm. Appl. Soft Comput. 42, 119\u2013131 (2016).","journal-title":"Appl. Soft Comput."},{"key":"21515_CR17","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.energy.2019.04.159","volume":"178","author":"EE Elattar","year":"2019","unstructured":"Elattar, E. E. & ElSayed, S. K. Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement. Energy 178, 598\u2013609 (2019).","journal-title":"Energy"},{"key":"21515_CR18","unstructured":"Kumari, B. A. & Vaisakh, K. Integration of solar and flexible resources into expected security cost with dynamic optimal power flow problem using a novel DE algorithm. Renew. Energy Focus (2022)."},{"key":"21515_CR19","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.energy.2017.01.071","volume":"122","author":"X Yuan","year":"2017","unstructured":"Yuan, X. et al. Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm. Energy 122, 70\u201382. https:\/\/doi.org\/10.1016\/j.energy.2017.01.071 (2017).","journal-title":"Energy"},{"key":"21515_CR20","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.energy.2019.01.021","volume":"171","author":"TT Nguyen","year":"2019","unstructured":"Nguyen, T. T. A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy 171, 218\u2013240. https:\/\/doi.org\/10.1016\/j.energy.2019.01.021 (2019).","journal-title":"Energy"},{"key":"21515_CR21","doi-asserted-by":"publisher","first-page":"8463","DOI":"10.1007\/s00521-019-04338-y","volume":"32","author":"S Duman","year":"2020","unstructured":"Duman, S., Li, J., Wu, L. & Guvenc, U. Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach. Neural Comput. Appl. 32, 8463\u20138492 (2020).","journal-title":"Neural Comput. Appl."},{"key":"21515_CR22","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1080\/15325008.2015.1041625","volume":"43","author":"AA El-Fergany","year":"2015","unstructured":"El-Fergany, A. A. & Hasanien, H. M. Single and multi-objective optimal power flow using Grey Wolf Optimizer and differential evolution algorithms. Electr. Power Compon. Syst. 43, 1548\u20131559. https:\/\/doi.org\/10.1080\/15325008.2015.1041625 (2015).","journal-title":"Electr. Power Compon. Syst."},{"key":"21515_CR23","doi-asserted-by":"crossref","unstructured":"Sarda, J., Pandya, K. & Lee, K. Y. Hybrid cross entropy\u2014cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads. Optim. Control Appl. Methods. (2021).","DOI":"10.1002\/oca.2759"},{"key":"21515_CR24","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.3390\/math10122106","volume":"10","author":"S Sarhan","year":"2022","unstructured":"Sarhan, S., El-Sehiemy, R., Abaza, A. & Gafar, M. Turbulent flow of water-based optimization for solving multi-objective technical and economic aspects of optimal power flow problems. Mathematics 10, 2106 (2022).","journal-title":"Mathematics"},{"key":"21515_CR25","doi-asserted-by":"publisher","first-page":"22646","DOI":"10.1109\/ACCESS.2022.3152557","volume":"10","author":"S Mouassa","year":"2022","unstructured":"Mouassa, S., Althobaiti, A., Jurado, F. & Ghoneim, S. S. M. Novel design of slim mould optimizer for the solution of optimal power flow problems incorporating intermittent sources: A case study of Algerian electricity grid. IEEE Access. 10, 22646\u201322661 (2022).","journal-title":"IEEE Access."},{"key":"21515_CR26","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1080\/15435075.2019.1677234","volume":"16","author":"SR Salkuti","year":"2019","unstructured":"Salkuti, S. R. Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system. Int. J. Green. Energy. 16, 1547\u20131561 (2019).","journal-title":"Int. J. Green. Energy"},{"key":"21515_CR27","doi-asserted-by":"publisher","first-page":"106018","DOI":"10.1016\/j.epsr.2019.106018","volume":"178","author":"K Dasgupta","year":"2020","unstructured":"Dasgupta, K., Roy, P. K. & Mukherjee, V. Power flow based hydro-thermal-wind scheduling of hybrid power system using sine cosine algorithm. Electr. Power Syst. Res. 178, 106018 (2020).","journal-title":"Electr. Power Syst. Res."},{"key":"21515_CR28","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.ijepes.2018.01.024","volume":"99","author":"A-F Attia","year":"2018","unstructured":"Attia, A-F., El Sehiemy, R. A. & Hasanien, H. M. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. Int. J. Electr. Power Energy Syst. 99, 331\u2013343. https:\/\/doi.org\/10.1016\/j.ijepes.2018.01.024 (2018).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"21515_CR29","doi-asserted-by":"publisher","first-page":"159809","DOI":"10.1109\/ACCESS.2021.3131161","volume":"9","author":"M Ahmad","year":"2021","unstructured":"Ahmad, M., Javaid, N., Niaz, I. A., Almogren, A. & Radwan, A. A bio-inspired heuristic algorithm for solving optimal power flow problem in hybrid power system. IEEE Access. 9, 159809\u2013159826 (2021).","journal-title":"IEEE Access."},{"key":"21515_CR30","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s00202-015-0357-y","volume":"98","author":"M G\u00fc\u00e7yetmez","year":"2016","unstructured":"G\u00fc\u00e7yetmez, M. & \u00c7am, E. A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems. Electr. Eng. 98, 145\u2013157 (2016).","journal-title":"Electr. Eng."},{"key":"21515_CR31","unstructured":"Ferroudj, N. E. I., Amroune, M. & Slimani, L. Optimal Power Flow of Hybrid Wind\/Thermal Generators Concedering Economic and Environmental Factors using BES Algorithm n.d."},{"key":"21515_CR32","doi-asserted-by":"crossref","unstructured":"Mittal, U., Nangia, U., Jain, N. K. & Gupta, S. Optimal power flow solution using a learning-based sine-cosine algorithm. J. Supercomput. 1\u201339. (2024).","DOI":"10.1007\/s11227-024-06043-7"},{"key":"21515_CR33","doi-asserted-by":"publisher","first-page":"712","DOI":"10.3390\/electricity5040035","volume":"5","author":"H Pulluri","year":"2024","unstructured":"Pulluri, H., Basetti, V., Srikanth Goud, B. & Kalyan, C. H. N. S. Exploring evolutionary algorithms for optimal power flow: A comprehensive review and analysis. Electricity 5, 712\u2013733 (2024).","journal-title":"Electricity"},{"key":"21515_CR34","doi-asserted-by":"publisher","first-page":"28367","DOI":"10.1038\/s41598-024-78086-y","volume":"14","author":"EH Houssein","year":"2024","unstructured":"Houssein, E. H., Ismaeel, A. A. K. & Said, M. Optimum solution of power flow problem based on search and rescue algorithm. Sci. Rep. 14, 28367 (2024).","journal-title":"Sci. Rep."},{"key":"21515_CR35","doi-asserted-by":"crossref","unstructured":"Abaci, K., Yetgin, Z., Yamacli, V. & Isiker, H. Modified effective butterfly optimizer for solving optimal power flow problem. Heliyon. 10 (2024).","DOI":"10.1016\/j.heliyon.2024.e32862"},{"key":"21515_CR36","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1049\/gtd2.12738","volume":"17","author":"M Premkumar","year":"2023","unstructured":"Premkumar, M. et al. A reliable optimization framework using ensembled successive history adaptive differential evolutionary algorithm for optimal power flow problems. IET Gener Transm Distrib. 17, 1333\u20131357 (2023).","journal-title":"IET Gener Transm Distrib."},{"key":"21515_CR37","doi-asserted-by":"crossref","unstructured":"Merah, H. et al. Solving single-and multi-objective optimal power flow problems using the spider Wasp optimization algorithm. Electr. Eng. 1\u201345 (2025).","DOI":"10.1007\/s00202-024-02937-0"},{"key":"21515_CR38","doi-asserted-by":"publisher","first-page":"102663","DOI":"10.1016\/j.asej.2024.102663","volume":"15","author":"HM Hasanien","year":"2024","unstructured":"Hasanien, H. M., Alsaleh, I., Ullah, Z. & Alassaf, A. Probabilistic optimal power flow in power systems with renewable energy integration using enhanced Walrus optimization algorithm. Ain Shams Eng. J. 15, 102663 (2024).","journal-title":"Ain Shams Eng. J."},{"key":"21515_CR39","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s10462-024-10752-z","volume":"57","author":"J-H Zhu","year":"2024","unstructured":"Zhu, J-H. et al. Multi-objective Coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem. Artif. Intell. Rev. 57, 117 (2024).","journal-title":"Artif. Intell. Rev."},{"key":"21515_CR40","doi-asserted-by":"publisher","first-page":"13422","DOI":"10.1038\/s41598-024-58565-y","volume":"14","author":"AAZ Diab","year":"2024","unstructured":"Diab, A. A. Z., Abdelhamid, A. M. & Sultan, H. M. Comprehensive analysis of optimal power flow using recent metaheuristic algorithms. Sci. Rep. 14, 13422 (2024).","journal-title":"Sci. Rep."},{"key":"21515_CR41","doi-asserted-by":"crossref","unstructured":"Kyomugisha, R., Muriithi, C. M. & Nyakoe, G. N. Performance of various voltage stability indices in a stochastic multiobjective optimal power flow using Mayfly algorithm. J. Electr. Comput. Eng. 2022 (2022).","DOI":"10.1155\/2022\/7456333"},{"key":"21515_CR42","doi-asserted-by":"publisher","first-page":"46763","DOI":"10.1109\/ACCESS.2019.2909561","volume":"7","author":"Z Ullah","year":"2019","unstructured":"Ullah, Z., Wang, S., Radosavljevi\u0107, J. & Lai, J. A solution to the optimal power flow problem considering WT and PV generation. IEEE Access. 7, 46763\u201346772 (2019).","journal-title":"IEEE Access."},{"key":"21515_CR43","doi-asserted-by":"publisher","first-page":"e08631","DOI":"10.1016\/j.heliyon.2021.e08631","volume":"7","author":"R Kyomugisha","year":"2021","unstructured":"Kyomugisha, R., Muriithi, C. M. & Edimu, M. Multiobjective optimal power flow for static voltage stability margin improvement. Heliyon 7, e08631 (2021).","journal-title":"Heliyon"},{"key":"21515_CR44","doi-asserted-by":"publisher","first-page":"5248","DOI":"10.3390\/su12135248","volume":"12","author":"MZ Islam","year":"2020","unstructured":"Islam, M. Z. et al. A Harris Hawks optimization based single-and multi-objective optimal power flow considering environmental emission. Sustainability 12, 5248 (2020).","journal-title":"Sustainability"},{"key":"21515_CR45","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.3390\/math10071201","volume":"10","author":"ZM Ali","year":"2022","unstructured":"Ali, Z. M., Aleem, S. H. E. A., Omar, A. I. & Mahmoud, B. S. Economical-environmental-technical operation of power networks with high penetration of renewable energy systems using multi-objective coronavirus herd immunity algorithm. Mathematics 10, 1201 (2022).","journal-title":"Mathematics"},{"key":"21515_CR46","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1111\/coin.12312","volume":"36","author":"S Chandrasekaran","year":"2020","unstructured":"Chandrasekaran, S. Multiobjective optimal power flow using interior search algorithm: A case study on a real-time electrical network. Comput. Intell. 36, 1078\u20131096. https:\/\/doi.org\/10.1111\/coin.12312 (2020).","journal-title":"Comput. Intell."},{"key":"21515_CR47","doi-asserted-by":"publisher","unstructured":"Jeddi, B., Einaddin, A. H. & Kazemzadeh, R. Optimal power flow problem considering the cost, loss, and emission by multi-objective electromagnetism-like algorithm. In 2016 6th Conf Therm Power Plants. https:\/\/doi.org\/10.1109\/ctpp.2016.7482931 (2016).","DOI":"10.1109\/ctpp.2016.7482931"},{"key":"21515_CR48","first-page":"466","volume":"5","author":"R Ma","year":"2019","unstructured":"Ma, R., Li, X., Luo, Y., Wu, X. & Jiang, F. Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response. CSEE J. Power Energy Syst. 5, 466\u2013473 (2019).","journal-title":"CSEE J. Power Energy Syst."},{"key":"21515_CR49","first-page":"1","volume":"2021","author":"A Maheshwari","year":"2021","unstructured":"Maheshwari, A., Sood, Y. R., Jaiswal, S., Sharma, S. & Kaur, J. Ant Lion optimization based OPF solution incorporating wind turbines and carbon emissions. Innov. Power Adv. Comput. Technol. 2021, 1\u20136 (2021).","journal-title":"Innov. Power Adv. Comput. Technol."},{"key":"21515_CR50","doi-asserted-by":"publisher","first-page":"108334","DOI":"10.1016\/j.asoc.2021.108334","volume":"116","author":"HT Kahraman","year":"2022","unstructured":"Kahraman, H. T., Akbel, M. & Duman, S. Optimization of optimal power flow problem using multi-objective Manta ray foraging optimizer. Appl. Soft Comput. 116, 108334 (2022).","journal-title":"Appl. Soft Comput."},{"key":"21515_CR51","doi-asserted-by":"publisher","first-page":"34395","DOI":"10.1109\/ACCESS.2021.3061471","volume":"9","author":"C Srithapon","year":"2021","unstructured":"Srithapon, C., Fuangfoo, P., Ghosh, P. K., Siritaratiwat, A. & Chatthaworn, R. Surrogate-assisted multi-objective probabilistic optimal power flow for distribution network with photovoltaic generation and electric vehicles. IEEE Access. 9, 34395\u201334414 (2021).","journal-title":"IEEE Access."},{"key":"21515_CR52","first-page":"411","volume":"20","author":"K Teeparthi","year":"2017","unstructured":"Teeparthi, K. & Kumar, D. M. V. Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators. Eng. Sci. Technol. Int. J. 20, 411\u2013426 (2017).","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"21515_CR53","doi-asserted-by":"crossref","unstructured":"Duman, S., Wu, L. & Li, J. Moth swarm algorithm based approach for the ACOPF considering wind and tidal energy. In Int. Conf. Artif. Intell. Appl. Math. Eng. 830\u2013843. (Springer, 2019).","DOI":"10.1007\/978-3-030-36178-5_72"},{"key":"21515_CR54","doi-asserted-by":"publisher","first-page":"6883","DOI":"10.3390\/app11156883","volume":"11","author":"M Riaz","year":"2021","unstructured":"Riaz, M. et al. An optimization-based strategy for solving optimal power flow problems in a power system integrated with stochastic solar and wind power energy. Appl. Sci. 11, 6883 (2021).","journal-title":"Appl. Sci."},{"key":"21515_CR55","doi-asserted-by":"publisher","first-page":"4027","DOI":"10.1007\/s00500-020-05431-4","volume":"25","author":"S Kamel","year":"2021","unstructured":"Kamel, S., Ebeed, M. & Jurado, F. others. An improved version of salp swarm algorithm for solving optimal power flow problem. Soft Comput. 25, 4027\u20134052 (2021).","journal-title":"Soft Comput."},{"key":"21515_CR56","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.asoc.2016.01.041","volume":"42","author":"HREH Bouchekara","year":"2016","unstructured":"Bouchekara, H. R. E. H., Chaib, A. E., Abido, M. A. & El-Sehiemy, R. A. Optimal power flow using an improved colliding bodies optimization algorithm. Appl. Soft Comput. 42, 119\u2013131. https:\/\/doi.org\/10.1016\/j.asoc.2016.01.041 (2016).","journal-title":"Appl. Soft Comput."},{"key":"21515_CR57","doi-asserted-by":"crossref","unstructured":"Hassan, M. H., Elsayed, S. K., Kamel, S., Rahmann, C. & Taha, I. B. M. Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources. Int. J. Energy Res. (2022).","DOI":"10.1002\/er.7928"},{"key":"21515_CR58","doi-asserted-by":"publisher","first-page":"89581","DOI":"10.1109\/ACCESS.2019.2927193","volume":"7","author":"EE Elattar","year":"2019","unstructured":"Elattar, E. E. Optimal power flow of a power system incorporating stochastic wind power based on modified moth swarm algorithm. IEEE Access. 7, 89581\u201389593 (2019).","journal-title":"IEEE Access."},{"key":"21515_CR59","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.energy.2014.06.026","volume":"73","author":"M Ghasemi","year":"2014","unstructured":"Ghasemi, M., Ghavidel, S., Akbari, E. & Vahed, A. A. Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos. Energy 73, 340\u2013353. https:\/\/doi.org\/10.1016\/j.energy.2014.06.026 (2014).","journal-title":"Energy"},{"key":"21515_CR60","doi-asserted-by":"publisher","first-page":"94","DOI":"10.22068\/IJEEE.15.1.94","volume":"15","author":"O Herbadji","year":"2019","unstructured":"Herbadji, O., Slimani, L. & Bouktir, T. Optimal power flow with four conflicting objective functions using multiobjective ant Lion algorithm: A case study of the Algerian electrical network. Iran. J. Electr. Electron. Eng. 15, 94\u2013113. https:\/\/doi.org\/10.22068\/IJEEE.15.1.94 (2019).","journal-title":"Iran. J. Electr. Electron. Eng."},{"key":"21515_CR61","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s12652-021-03622-x","volume":"13","author":"RA El-Sehiemy","year":"2022","unstructured":"El-Sehiemy, R. A. A novel single\/multi-objective frameworks for techno-economic operation in power systems using tunicate swarm optimization technique. J. Ambient Intell. Humaniz. Comput. 13, 1073\u20131091 (2022).","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"21515_CR62","unstructured":"Avvari, R. K. & DM VK. A novel hybrid multi-objective evolutionary algorithm for optimal power\u200e flow in wind, PV, and PEV systems\u200e. J. Oper. Autom. Power Eng. (2022)."},{"key":"21515_CR63","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1016\/j.enconman.2017.06.071","volume":"148","author":"PP Biswas","year":"2017","unstructured":"Biswas, P. P., Suganthan, P. N. & Amaratunga, G. A. J. Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers. Manag. 148, 1194\u20131207 (2017).","journal-title":"Energy Convers. Manag"},{"key":"21515_CR64","doi-asserted-by":"publisher","first-page":"139726","DOI":"10.1109\/ACCESS.2019.2943480","volume":"7","author":"G Chen","year":"2019","unstructured":"Chen, G., Qian, J., Zhang, Z. & Sun, Z. Multi-objective optimal power flow based on hybrid firefly-bat algorithm and constraints-prior object-fuzzy sorting strategy. IEEE Access. 7, 139726\u2013139745 (2019).","journal-title":"IEEE Access."},{"key":"21515_CR65","doi-asserted-by":"publisher","first-page":"678","DOI":"10.3390\/en9090678","volume":"9","author":"W Warid","year":"2016","unstructured":"Warid, W., Hizam, H., Mariun, N. & Abdul-Wahab, N. I. Optimal power flow using the Jaya algorithm. Energies 9, 678 (2016).","journal-title":"Energies"},{"key":"21515_CR66","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1049\/rpg2.12023","volume":"15","author":"S Duman","year":"2021","unstructured":"Duman, S., Li, J. & Wu, L. AC optimal power flow with thermal\u2013wind\u2013solar\u2013tidal systems using the symbiotic organisms search algorithm. IET Renew. Power Gener. 15, 278\u2013296 (2021).","journal-title":"IET Renew. Power Gener"},{"key":"21515_CR67","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1016\/j.enconman.2010.08.017","volume":"52","author":"M Basu","year":"2011","unstructured":"Basu, M. Multi-objective optimal power flow with FACTS devices. Energy Convers. Manag. 52, 903\u2013910. https:\/\/doi.org\/10.1016\/j.enconman.2010.08.017 (2011).","journal-title":"Energy Convers. Manag"},{"key":"21515_CR68","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.ijepes.2014.10.027","volume":"65","author":"M Ghasemi","year":"2015","unstructured":"Ghasemi, M., Ghavidel, S., Gitizadeh, M. & Akbari, E. An improved teaching\u2013learning-based optimization algorithm using L\u00e9vy mutation strategy for non-smooth optimal power flow. Int. J. Electr. Power Energy Syst. 65, 375\u2013384. https:\/\/doi.org\/10.1016\/j.ijepes.2014.10.027 (2015).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"21515_CR69","doi-asserted-by":"publisher","first-page":"107814","DOI":"10.1016\/j.asoc.2021.107814","volume":"112","author":"S Duman","year":"2021","unstructured":"Duman, S., Akbel, M. & Kahraman, H. T. Development of the multi-objective adaptive guided differential evolution and optimization of the MO-ACOPF for wind\/PV\/tidal energy sources. Appl. Soft Comput. 112, 107814 (2021).","journal-title":"Appl. Soft Comput."},{"key":"21515_CR70","doi-asserted-by":"publisher","first-page":"107893","DOI":"10.1016\/j.ijepes.2021.107893","volume":"138","author":"MS Shaikh","year":"2022","unstructured":"Shaikh, M. S. et al. Optimal parameter estimation of 1-phase and 3-phase transmission line for various bundle conductor\u2019s using modified Whale optimization algorithm. Int. J. Electr. Power Energy Syst. 138, 107893 (2022).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"21515_CR71","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1109\/ICJECE.2021.3069143","volume":"44","author":"MS Shaikh","year":"2021","unstructured":"Shaikh, M. S., Hua, C., Jatoi, M. A., Ansari, M. M. & Qader, A. A. Parameter estimation of AC transmission line considering different bundle conductors using flux linkage technique. IEEE Can. J. Electr. Comput. Eng. 44, 313\u2013320 (2021).","journal-title":"IEEE Can. J. Electr. Comput. Eng."},{"key":"21515_CR72","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1049\/smt2.12023","volume":"15","author":"MS Shaikh","year":"2021","unstructured":"Shaikh, M. S., Hua, C., Jatoi, M. A., Ansari, M. M. & Qader, A. A. Application of grey Wolf optimisation algorithm in parameter calculation of overhead transmission line system. IET Sci. Meas. Technol. 15, 218\u2013231 (2021).","journal-title":"IET Sci. Meas. Technol."},{"key":"21515_CR73","doi-asserted-by":"publisher","first-page":"100182","DOI":"10.1016\/j.dajour.2023.100182","volume":"6","author":"MS Shaikh","year":"2023","unstructured":"Shaikh, M. S., Raj, S., Babu, R., Kumar, S. & Sagrolikar, K. A hybrid moth\u2013flame algorithm with particle swarm optimization with application in power transmission and distribution. Decis. Anal. J. 6, 100182 (2023).","journal-title":"Decis. Anal. J."},{"key":"21515_CR74","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1002\/oca.2772","volume":"43","author":"MS Shaikh","year":"2022","unstructured":"Shaikh, M. S. et al. Optimal parameter estimation of overhead transmission line considering different bundle conductors with the uncertainty of load modeling. Optim. Control Appl. Methods. 43, 652\u2013666 (2022).","journal-title":"Optim. Control Appl. Methods"},{"key":"21515_CR75","doi-asserted-by":"publisher","first-page":"5508","DOI":"10.1016\/j.asoc.2011.05.008","volume":"11","author":"R Rajabioun","year":"2011","unstructured":"Rajabioun, R. Cuckoo optimization algorithm. Appl. Soft Comput. 11, 5508\u20135518 (2011).","journal-title":"Appl. Soft Comput."},{"key":"21515_CR76","first-page":"14","volume":"4","author":"H Garg","year":"2015","unstructured":"Garg, H. An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm. Beni-Suef Univ. J. Basic. Appl. Sci. 4, 14\u201325 (2015).","journal-title":"Beni-Suef Univ. J. Basic. Appl. Sci."},{"key":"21515_CR77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2528\/PIERC13110902","volume":"46","author":"U Singh","year":"2014","unstructured":"Singh, U. & Rattan, M. Design of linear and circular antenna arrays using cuckoo optimization algorithm. Prog Electromagn. Res. C. 46, 1\u201311 (2014).","journal-title":"Prog Electromagn. Res. C"},{"key":"21515_CR78","doi-asserted-by":"publisher","first-page":"7885","DOI":"10.1007\/s00500-019-04010-6","volume":"24","author":"AK Sangaiah","year":"2020","unstructured":"Sangaiah, A. K., Tirkolaee, E. B., Goli, A. & Dehnavi-Arani, S. Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem. Soft Comput. 24, 7885\u20137905 (2020).","journal-title":"Soft Comput."},{"key":"21515_CR79","doi-asserted-by":"publisher","first-page":"3054","DOI":"10.1109\/TII.2017.2769656","volume":"14","author":"FS Gazijahani","year":"2017","unstructured":"Gazijahani, F. S. & Salehi, J. Optimal bilevel model for stochastic risk-based planning of microgrids under uncertainty. IEEE Trans. Ind. Inf. 14, 3054\u20133064 (2017).","journal-title":"IEEE Trans. Ind. Inf."},{"key":"21515_CR80","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1016\/j.energy.2015.10.006","volume":"93","author":"MA Mellal","year":"2015","unstructured":"Mellal, M. A. & Williams, E. J. Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem. Energy 93, 1711\u20131718 (2015).","journal-title":"Energy"},{"key":"21515_CR81","doi-asserted-by":"publisher","first-page":"3433","DOI":"10.1016\/j.eswa.2014.12.020","volume":"42","author":"M Kumar","year":"2015","unstructured":"Kumar, M. & Rawat, T. K. Optimal design of FIR fractional order differentiator using cuckoo search algorithm. Expert Syst. Appl. 42, 3433\u20133449 (2015).","journal-title":"Expert Syst. Appl."},{"key":"21515_CR82","doi-asserted-by":"publisher","first-page":"3749","DOI":"10.1007\/s11269-015-1027-6","volume":"29","author":"S-M Hosseini-Moghari","year":"2015","unstructured":"Hosseini-Moghari, S-M., Morovati, R., Moghadas, M. & Araghinejad, S. Optimum operation of reservoir using two evolutionary algorithms: imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA). Water Resour. Manag. 29, 3749\u20133769 (2015).","journal-title":"Water Resour. Manag"},{"key":"21515_CR83","doi-asserted-by":"publisher","first-page":"2817","DOI":"10.1049\/iet-gtd.2016.0287","volume":"10","author":"M Dalali","year":"2016","unstructured":"Dalali, M. & Kazemi Karegar, H. Optimal PMU placement for full observability of the power network with maximum redundancy using modified binary cuckoo optimisation algorithm. IET Gener Transm Distrib. 10, 2817\u20132824 (2016).","journal-title":"IET Gener Transm Distrib."},{"key":"21515_CR84","doi-asserted-by":"publisher","unstructured":"Ghasemi, M., Ghavidel, S., Ghanbarian, M. M., Gharibzadeh, M. & Azizi Vahed, A. Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm. Energy. 78 https:\/\/doi.org\/10.1016\/j.energy.2014.10.007 (2014).","DOI":"10.1016\/j.energy.2014.10.007"},{"key":"21515_CR85","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ijepes.2006.03.024","volume":"29","author":"Y Sood","year":"2007","unstructured":"Sood, Y. Evolutionary programming based optimal power flow and its validation for deregulated power system analysis. Int. J. Electr. Power Energy Syst. 29, 65\u201375. https:\/\/doi.org\/10.1016\/j.ijepes.2006.03.024 (2007).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"21515_CR86","doi-asserted-by":"publisher","first-page":"e12270","DOI":"10.1002\/2050-7038.12270","volume":"30","author":"S Duman","year":"2020","unstructured":"Duman, S., Rivera, S., Li, J. & Wu, L. Optimal power flow of power systems with controllable wind-photovoltaic energy systems via differential evolutionary particle swarm optimization. Int. Trans. Electr. Energy Syst. 30, e12270 (2020).","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"21515_CR87","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.engappai.2013.11.003","volume":"29","author":"M Ghasemi","year":"2014","unstructured":"Ghasemi, M., Ghavidel, S., Rahmani, S., Roosta, A. & Falah, H. A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions. Eng. Appl. Artif. Intell. 29, 54\u201369. https:\/\/doi.org\/10.1016\/j.engappai.2013.11.003 (2014).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"21515_CR88","doi-asserted-by":"crossref","unstructured":"Duman, S. et al. Improved phasor particle swarm optimization with fitness distance balance for optimal power flow problem of hybrid AC\/DC power grids. In Int. Conf. Artif. Intell. Appl. Math. Eng. 307\u2013336 (2021).","DOI":"10.1007\/978-3-031-09753-9_24"},{"key":"21515_CR89","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.epsr.2016.09.025","volume":"142","author":"A-A-A Mohamed","year":"2017","unstructured":"Mohamed, A-A-A., Mohamed, Y. S., El-Gaafary, A. A. M. & Hemeida, A. M. Optimal power flow using moth swarm algorithm. Electr. Power Syst. Res. 142, 190\u2013206. https:\/\/doi.org\/10.1016\/j.epsr.2016.09.025 (2017).","journal-title":"Electr. Power Syst. Res."},{"key":"21515_CR90","doi-asserted-by":"publisher","unstructured":"Ghasemi, M., Ghanbarian, M. M., Ghavidel, S., Rahmani, S. & Mahboubi Moghaddam, E. Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: A comparative study. Inf. Sci.\u00a0278. https:\/\/doi.org\/10.1016\/j.ins.2014.03.050 (2014).","DOI":"10.1016\/j.ins.2014.03.050"},{"key":"21515_CR91","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1080\/15325000252888425","volume":"30","author":"MA Abido","year":"2002","unstructured":"Abido, M. A. Optimal power flow using Tabu search algorithm. Electr. Power Compon. Syst. 30, 469\u2013483. https:\/\/doi.org\/10.1080\/15325000252888425 (2002).","journal-title":"Electr. Power Compon. Syst."},{"key":"21515_CR92","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey Wolf Optimizer. Adv. Eng. Softw. 69, 46\u201361 (2014).","journal-title":"Adv. Eng. Softw."},{"key":"21515_CR93","doi-asserted-by":"publisher","first-page":"115676","DOI":"10.1016\/j.cma.2022.115676","volume":"403","author":"G Hu","year":"2023","unstructured":"Hu, G., Yang, R., Qin, X. & Wei, G. MCSA: Multi-strategy boosted chameleon-inspired optimization algorithm for engineering applications. Comput. Methods Appl. Mech. Eng. 403, 115676 (2023).","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"21515_CR94","doi-asserted-by":"publisher","first-page":"116582","DOI":"10.1016\/j.cma.2023.116582","volume":"419","author":"M Ghasemi","year":"2024","unstructured":"Ghasemi, M. et al. Optimization based on performance of lungs in body: lungs performance-based optimization (LPO). Comput. Methods Appl. Mech. Eng. 419, 116582 (2024).","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"21515_CR95","doi-asserted-by":"crossref","unstructured":"Ghasemi, M. et al. Geyser inspired algorithm: A new geological-inspired meta-heuristic for real-parameter and constrained engineering optimization. J. Bionic Eng. 1\u201335 (2023).","DOI":"10.1007\/s42235-023-00437-8"},{"key":"21515_CR96","doi-asserted-by":"crossref","unstructured":"Hussain, H. A., Mansor, Z. & Shukur, Z. Comprehensive survey and research directions on blockchain Iot access control. Int. J. Adv. Comput. Sci. Appl. 12 (2021).","DOI":"10.14569\/IJACSA.2021.0120530"},{"key":"21515_CR97","doi-asserted-by":"publisher","first-page":"100693","DOI":"10.1016\/j.swevo.2020.100693","volume":"56","author":"A Kumar","year":"2020","unstructured":"Kumar, A. et al. A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm Evol. Comput. 56, 100693 (2020).","journal-title":"Swarm Evol. Comput."},{"key":"21515_CR98","unstructured":"Sugantthan, P. N. Guidelines for real-world single-objective constrained optimisation competition. PN-Suganthan\/2020-RW-Constrained-Optimisation. (2020)."},{"key":"21515_CR99","doi-asserted-by":"publisher","first-page":"107442","DOI":"10.1016\/j.asoc.2021.107442","volume":"108","author":"VH Cant\u00fa","year":"2021","unstructured":"Cant\u00fa, V. H., Azzaro-Pantel, C. & Ponsich, A. Constraint-handling techniques within differential evolution for solving process engineering problems. Appl. Soft Comput. 108, 107442 (2021).","journal-title":"Appl. Soft Comput."},{"key":"21515_CR100","doi-asserted-by":"crossref","unstructured":"Wen, X., Wu, G., Fan, M., Wang, R. & Suganthan, P. N. Voting-mechanism based ensemble constraint handling technique for real-world single-objective constrained optimization. In 2020 IEEE Congr Evol. Comput., 1\u20138 (2020).","DOI":"10.1109\/CEC48606.2020.9185632"},{"key":"21515_CR101","doi-asserted-by":"crossref","unstructured":"Gurrola-Ramos, J., Hern\u00e0ndez-Aguirre, A. & Dalmau-Cede\u00f1o, O. COLSHADE for real-world single-objective constrained optimization problems. In 2020 IEEE Congr Evol. Comput., 1\u20138 (2020).","DOI":"10.1109\/CEC48606.2020.9185583"},{"key":"21515_CR102","doi-asserted-by":"crossref","unstructured":"Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K. & Ryan, M. J. Improved multi-operator differential evolution algorithm for solving unconstrained problems. In 2020 IEEE Congr Evol. Comput., 1\u20138 (2020).","DOI":"10.1109\/CEC48606.2020.9185577"},{"key":"21515_CR103","doi-asserted-by":"crossref","unstructured":"Hellwig, M. & Beyer, H-G. A modified matrix adaptation evolution strategy with restarts for constrained real-world problems. In 2020 IEEE Congr Evol. Comput., 1\u20138 (2020).","DOI":"10.1109\/CEC48606.2020.9185566"},{"key":"21515_CR104","doi-asserted-by":"publisher","first-page":"11537","DOI":"10.1016\/J.AEJ.2022.05.018","volume":"61","author":"SS Band","year":"2022","unstructured":"Band, S. S. et al. Colonial competitive evolutionary Rao algorithm for optimal engineering design. Alexandria Eng. J. 61, 11537\u201311563. https:\/\/doi.org\/10.1016\/J.AEJ.2022.05.018 (2022).","journal-title":"Alexandria Eng. J."},{"key":"21515_CR105","doi-asserted-by":"crossref","unstructured":"Kumar, A., Das, S. & Zelinka, I. A self-adaptive spherical search algorithm for real-world constrained optimization problems. In Proc. 2020 Genet. Evol. Comput. Conf. Companion, 13\u20134 (2020).","DOI":"10.1145\/3377929.3398186"},{"key":"21515_CR106","doi-asserted-by":"publisher","first-page":"108640","DOI":"10.1109\/ACCESS.2021.3101939","volume":"9","author":"J Xu","year":"2021","unstructured":"Xu, J. & Xu, L. Optimal stochastic process optimizer: A new metaheuristic algorithm with adaptive exploration-exploitation property. IEEE Access. 9, 108640\u2013108664 (2021).","journal-title":"IEEE Access."},{"key":"21515_CR107","doi-asserted-by":"crossref","unstructured":"Kumar, A., Das, S. & Zelinka, I. A modified covariance matrix adaptation evolution strategy for real-world constrained optimization problems. In Proc. 2020 Genet. Evol. Comput. Conf. Companion, 11\u20132 (2020).","DOI":"10.1145\/3377929.3398185"},{"key":"21515_CR108","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/TPWRS.2010.2051168","volume":"26","author":"RD Zimmerman","year":"2010","unstructured":"Zimmerman, R. D., Murillo-S\u00e1nchez, C. E. & Thomas, R. J. Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26, 12\u201319 (2010).","journal-title":"IEEE Trans. Power Syst."},{"key":"21515_CR109","doi-asserted-by":"publisher","unstructured":"Ghasemi, M., Ghavidel, S., Ghanbarian, M. M. & Gitizadeh, M. Multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm. Inf. Sci. 294. https:\/\/doi.org\/10.1016\/j.ins.2014.09.051 (2015).","DOI":"10.1016\/j.ins.2014.09.051"},{"key":"21515_CR110","doi-asserted-by":"publisher","unstructured":"Ghasemi, M., Ghavidel, S., Gitizadeh, M. & Akbari, E. An improved teaching-learning-based optimization algorithm using L\u00e9vy mutation strategy for non-smooth optimal power flow. Int. J. Electr. Power Energy Syst. 65 https:\/\/doi.org\/10.1016\/j.ijepes.2014.10.027 (2015).","DOI":"10.1016\/j.ijepes.2014.10.027"},{"key":"21515_CR111","doi-asserted-by":"publisher","DOI":"10.1002\/etep.2536","author":"M Ghasemi","year":"2018","unstructured":"Ghasemi, M., Ghavidel, S., Aghaei, J., Akbari, E. & Li, L. CFA optimizer: A new and powerful algorithm inspired by Franklin\u2019s and Coulomb\u2019s laws theory for solving the economic load dispatch problems. Int. Trans. Electr. Energy Syst. https:\/\/doi.org\/10.1002\/etep.2536 (2018).","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"21515_CR112","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.ijepes.2016.01.036","volume":"80","author":"SS Reddy","year":"2016","unstructured":"Reddy, S. S. & Rathnam, C. S. Optimal power flow using glowworm swarm optimization. Int. J. Electr. Power Energy Syst. 80, 128\u2013139 (2016).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"21515_CR113","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.ijepes.2015.05.011","volume":"73","author":"A Ramesh Kumar","year":"2015","unstructured":"Ramesh Kumar, A. & Premalatha, L. Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. Int. J. Electr. Power Energy Syst. 73, 393\u2013399. https:\/\/doi.org\/10.1016\/j.ijepes.2015.05.011 (2015).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"21515_CR114","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1080\/15325000691001458","volume":"34","author":"W Ongsakul","year":"2006","unstructured":"Ongsakul, W. & Tantimaporn, T. Optimal power flow by improved evolutionary programming. Electr. Power Compon. Syst. 34, 79\u201395. https:\/\/doi.org\/10.1080\/15325000691001458 (2006).","journal-title":"Electr. Power Compon. Syst."},{"key":"21515_CR115","doi-asserted-by":"crossref","unstructured":"Guvenc, U., Bakir, H., Duman, S. & Ozkaya, B. Optimal power flow using Manta ray foraging optimization. In Int. Conf. Artif. Intell. Appl. Math. Eng. 136\u2013149 (2020).","DOI":"10.1007\/978-3-030-79357-9_14"},{"key":"21515_CR116","doi-asserted-by":"publisher","first-page":"6117","DOI":"10.3390\/en14196117","volume":"14","author":"AK Khamees","year":"2021","unstructured":"Khamees, A. K., Abdelaziz, A. Y., Eskaros, M. R., El-Shahat, A. & Attia, M. A. Optimal power flow solution of wind-integrated power system using novel metaheuristic method. Energies 14, 6117 (2021).","journal-title":"Energies"},{"key":"21515_CR117","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1002\/etep.494","volume":"21","author":"J Hazra","year":"2011","unstructured":"Hazra, J. & Sinha, A. K. A multi-objective optimal power flow using particle swarm optimization. Eur. Trans. Electr. Power. 21, 1028\u20131045. https:\/\/doi.org\/10.1002\/etep.494 (2011).","journal-title":"Eur. Trans. Electr. Power"},{"key":"21515_CR118","doi-asserted-by":"publisher","first-page":"3036","DOI":"10.1016\/j.enconman.2008.06.014","volume":"49","author":"S Sayah","year":"2008","unstructured":"Sayah, S. & Zehar, K. Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers. Manag. 49, 3036\u20133042. https:\/\/doi.org\/10.1016\/j.enconman.2008.06.014 (2008).","journal-title":"Energy Convers. Manag"},{"key":"21515_CR119","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s00500-016-2319-3","volume":"22","author":"H Pulluri","year":"2018","unstructured":"Pulluri, H., Naresh, R. & Sharma, V. A solution network based on stud krill herd algorithm for optimal power flow problems. Soft Comput. 22, 159\u2013176. https:\/\/doi.org\/10.1007\/s00500-016-2319-3 (2018).","journal-title":"Soft Comput."},{"key":"21515_CR120","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1080\/15325008.2015.1061620","volume":"43","author":"J Radosavljevi\u0107","year":"2015","unstructured":"Radosavljevi\u0107, J., Klimenta, D., Jevti\u0107, M. & Arsi\u0107, N. Optimal power flow using a hybrid optimization algorithm of particle swarm optimization and gravitational search algorithm. Electr. Power Compon. Syst. 43, 1958\u20131970. https:\/\/doi.org\/10.1080\/15325008.2015.1061620 (2015).","journal-title":"Electr. Power Compon. Syst."},{"key":"21515_CR121","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijepes.2015.12.021","volume":"79","author":"K Abaci","year":"2016","unstructured":"Abaci, K. & Yamacli, V. Differential search algorithm for solving multi-objective optimal power flow problem. Int. J. Electr. Power Energy Syst. 79, 1\u201310. https:\/\/doi.org\/10.1016\/j.ijepes.2015.12.021 (2016).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"21515_CR122","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.ijepes.2014.07.010","volume":"64","author":"R Roy","year":"2015","unstructured":"Roy, R. & Jadhav, H. T. Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm. Int. J. Electr. Power Energy Syst. 64, 562\u2013578. https:\/\/doi.org\/10.1016\/j.ijepes.2014.07.010 (2015).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"21515_CR123","doi-asserted-by":"publisher","first-page":"100031","DOI":"10.1016\/j.prime.2022.100031","volume":"2","author":"L Jebaraj","year":"2022","unstructured":"Jebaraj, L. & Sakthivel, S. A new swarm intelligence optimization approach to solve power flow optimization problem incorporating conflicting and fuel cost based objective functions. E-Prime-Advances Electr. Eng. Electron. Energy. 2, 100031 (2022).","journal-title":"E-Prime-Advances Electr. Eng. Electron. Energy"},{"key":"21515_CR124","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.engappai.2017.10.019","volume":"68","author":"PP Biswas","year":"2018","unstructured":"Biswas, P. P., Suganthan, P. N., Mallipeddi, R. & Amaratunga, G. A. J. Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Eng. Appl. Artif. Intell. 68, 81\u2013100. https:\/\/doi.org\/10.1016\/j.engappai.2017.10.019 (2018).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"21515_CR125","doi-asserted-by":"publisher","first-page":"7193","DOI":"10.3390\/app12147193","volume":"12","author":"AS Alghamdi","year":"2022","unstructured":"Alghamdi, A. S. A hybrid firefly\u2013JAYA algorithm for the optimal power flow problem considering wind and solar power generations. Appl. Sci. 12, 7193 (2022).","journal-title":"Appl. Sci."},{"key":"21515_CR126","doi-asserted-by":"publisher","first-page":"8113","DOI":"10.3390\/su13148113","volume":"13","author":"SSM Ghoneim","year":"2021","unstructured":"Ghoneim, S. S. M., Kotb, M. F., Hasanien, H. M., Alharthi, M. M. & El-Fergany, A. A. Cost minimizations and performance enhancements of power systems using spherical prune differential evolution algorithm including modal analysis. Sustainability 13, 8113 (2021).","journal-title":"Sustainability"},{"key":"21515_CR127","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.asoc.2018.01.039","volume":"65","author":"W Warid","year":"2018","unstructured":"Warid, W., Hizam, H., Mariun, N. & Abdul Wahab, N. I. A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution. Appl. Soft Comput. 65, 360\u2013373. https:\/\/doi.org\/10.1016\/j.asoc.2018.01.039 (2018).","journal-title":"Appl. Soft Comput."},{"key":"21515_CR128","doi-asserted-by":"publisher","unstructured":"Bentouati, B., Khelifi, A., Shaheen, A. M. & El-Sehiemy, R. A. An enhanced moth-swarm algorithm for efficient energy management based multi dimensions OPF problem. J. Ambient Intell. Humaniz. Comput. 1\u201321. https:\/\/doi.org\/10.1007\/s12652-020-02692-7 (2020).","DOI":"10.1007\/s12652-020-02692-7"},{"key":"21515_CR129","doi-asserted-by":"publisher","first-page":"116817","DOI":"10.1016\/j.energy.2019.116817","volume":"193","author":"RA El Sehiemy","year":"2020","unstructured":"El Sehiemy, R. A., Selim, F., Bentouati, B. & Abido, M. A. A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems. Energy 193, 116817 (2020).","journal-title":"Energy"},{"key":"21515_CR130","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.energy.2014.10.007","volume":"78","author":"M Ghasemi","year":"2014","unstructured":"Ghasemi, M., Ghavidel, S., Ghanbarian, M. M., Gharibzadeh, M. & Azizi Vahed, A. Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm. Energy 78, 276\u2013289. https:\/\/doi.org\/10.1016\/j.energy.2014.10.007 (2014).","journal-title":"Energy"},{"key":"21515_CR131","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1016\/j.egypro.2017.05.232","volume":"117","author":"C Shilaja","year":"2017","unstructured":"Shilaja, C. & Ravi, K. Optimal power flow using hybrid DA-APSO algorithm in renewable energy resources. Energy Procedia. 117, 1085\u20131092 (2017).","journal-title":"Energy Procedia"},{"key":"21515_CR132","unstructured":"Ouafa, H., Linda, S. & Tarek, B. Multi-objective optimal power flow considering the fuel cost, emission, voltage deviation and power losses using multi-objective dragonfly algorithm. In Proc. Int. Conf. Recent Adv. Electr. Syst. Tunusia, (2017)."},{"key":"21515_CR133","doi-asserted-by":"publisher","first-page":"2831","DOI":"10.3390\/en14102831","volume":"14","author":"S Gupta","year":"2021","unstructured":"Gupta, S. et al. A hybrid Jaya\u2013Powell\u2019s pattern search algorithm for multi-objective optimal power flow incorporating distributed generation. Energies 14, 2831 (2021).","journal-title":"Energies"},{"key":"21515_CR134","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1016\/j.energy.2019.02.009","volume":"172","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Wang, S., Tang, Q., Zhou, Y. & Zeng, T. An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems. Energy 172, 945\u2013957 (2019).","journal-title":"Energy"},{"key":"21515_CR135","doi-asserted-by":"crossref","unstructured":"Abbassi, R., Saidi, S., Hammami, M. & Chebbi, S. Analysis of renewable energy power systems: Reliability and flexibility during unbalanced network fault. In Handb. Res. Adv. Intell. Control Eng. Autom., 651\u201386 (IGI Global, 2015).","DOI":"10.4018\/978-1-4666-7248-2.ch024"},{"key":"21515_CR136","doi-asserted-by":"publisher","unstructured":"Ghasemi, M., Aghaei, J., Akbari, E., Ghavidel, S. & Li, L. A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems. Energy 107 https:\/\/doi.org\/10.1016\/j.energy.2016.04.002 (2016).","DOI":"10.1016\/j.energy.2016.04.002"},{"key":"21515_CR137","doi-asserted-by":"publisher","first-page":"120211","DOI":"10.1016\/j.energy.2021.120211","volume":"225","author":"A Meng","year":"2021","unstructured":"Meng, A. et al. A high-performance crisscross search based Grey Wolf Optimizer for solving optimal power flow problem. Energy 225, 120211 (2021).","journal-title":"Energy"},{"key":"21515_CR138","doi-asserted-by":"publisher","unstructured":"Ghasemi, M., Taghizadeh, M., Ghavidel, S., Aghaei, J. & Abbasian, A. Solving optimal reactive power dispatch problem using a novel teaching-learning-based optimization algorithm. Eng. Appl. Artif. Intell. 39 https:\/\/doi.org\/10.1016\/j.engappai.2014.12.001 (2015).","DOI":"10.1016\/j.engappai.2014.12.001"},{"key":"21515_CR139","doi-asserted-by":"publisher","unstructured":"Ghasemi, M., Ghavidel, S., Ghanbarian, M. M. & Habibi, A. A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables. Appl. Soft Comput. J. 22 https:\/\/doi.org\/10.1016\/j.asoc.2014.05.006 (2014).","DOI":"10.1016\/j.asoc.2014.05.006"},{"key":"21515_CR140","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.3390\/math9131532","volume":"9","author":"MH Hassan","year":"2021","unstructured":"Hassan, M. H., Kamel, S., Selim, A., Khurshaid, T. & Dom\u00ednguez-Garc\u00eda, J. L. A modified Rao-2 algorithm for optimal power flow incorporating renewable energy sources. Mathematics 9, 1532 (2021).","journal-title":"Mathematics"},{"key":"21515_CR141","unstructured":"Alanazi, A. Optimal power flow considering solar and wind energy systems via modified cuckoo optimization algorithm. J. North. Basic. Appl. Sci. 8 (2023)."},{"key":"21515_CR142","doi-asserted-by":"crossref","unstructured":"Alghamdi, A. S., Zohdy, M. A. & Aldoihi, S. Enhancing renewable energy integration: A Gaussian-Bare-Bones levy cheetah optimization approach to optimal power flow in electrical networks. Comput. Model. Eng. Sci. 1\u201310 (2024).","DOI":"10.32604\/cmes.2024.048839"},{"key":"21515_CR143","doi-asserted-by":"publisher","first-page":"2975","DOI":"10.3390\/electronics10232975","volume":"10","author":"MH Nadimi-Shahraki","year":"2021","unstructured":"Nadimi-Shahraki, M. H. et al. EWOA-OPF: effective Whale optimization algorithm to solve optimal power flow problem. Electronics 10, 2975 (2021).","journal-title":"Electronics"},{"key":"21515_CR144","doi-asserted-by":"publisher","first-page":"14635","DOI":"10.1038\/s41598-023-41749-3","volume":"13","author":"A Zahedibialvaei","year":"2023","unstructured":"Zahedibialvaei, A. et al. An enhanced turbulent flow of water-based optimization for optimal power flow of power system integrated wind turbine and solar photovoltaic generators. Sci. Rep. 13, 14635 (2023).","journal-title":"Sci. Rep."},{"key":"21515_CR145","doi-asserted-by":"publisher","first-page":"43107","DOI":"10.1109\/ACCESS.2021.3060710","volume":"9","author":"AM Shaheen","year":"2021","unstructured":"Shaheen, A. M., El-Sehiemy, R. A., Elattar, E. E. & Abd-Elrazek, A. S. A modified crow search optimizer for solving non-linear OPF problem with emissions. IEEE Access. 9, 43107\u201343120 (2021).","journal-title":"IEEE Access."},{"key":"21515_CR146","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.conengprac.2017.02.010","volume":"61","author":"W Bai","year":"2017","unstructured":"Bai, W., Eke, I. & Lee, K. Y. An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem. Control Eng. Pract. 61, 163\u2013172 (2017).","journal-title":"Control Eng. Pract."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21515-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21515-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21515-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T02:03:55Z","timestamp":1762049035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21515-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,28]]},"references-count":146,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["21515"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-21515-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5078281\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,28]]},"assertion":[{"value":"12 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"37629"}}