{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T01:27:02Z","timestamp":1786670822526,"version":"build-2736575974"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":91,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Difficult Optimization problems can be efficiently solved through nature-inspired optimization algorithms, which remain problem-independent and are computational models at a conceptual level. In this work, a Jaya algorithm will be offered for tackling the Optimal Power Flow (OPF) problem. The basic idea of the Jaya algorithm for optimization problems is that the candidate solutions should move closer to the global optimum solution without converging at suboptimal solutions. Like other nature-inspired optimization techniques, the Jaya method is parameter-free and does not require any algorithm-specific control factors, such as learning or mutant parameters. The parameter freedom of an optimization algorithm not only improves its simplicity, but it also overcomes the challenge of adjusting optimization algorithm parameters, which affects performance as indicated in the literature, and can be expensive for some computations. The OPF problem aims to optimize the different objective functions using the control variables of the power systems. These include improving the voltage stability, decreasing the cost and emissions, and reducing power loss. For comparison purposes, the Jaya optimization technique for the OPF problem has been tested on the IEEE 30-Bus, 57-Bus and IEEE 118 Bus systems. The results were compared to previously reported outcomes of optimization approaches. The result of the computational study reveals the effectiveness of the Jaya optimization technique when compared with the other reported techniques. For instance, when the Jaya algorithm was applied to the IEEE 30-bus system, the fuel cost was reduced by approximately 11.31% compared to the initial operating condition.<\/jats:p>","DOI":"10.1038\/s41598-026-48197-9","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T16:55:59Z","timestamp":1778864159000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parameter-free optimization algorithm effective and precise solution of the optimal power flow problem"],"prefix":"10.1038","volume":"16","author":[{"given":"Saket","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neeraj","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd","family":"Zuhaib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marwan Ahmad Abdullah","family":"Sufyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Upma","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandeep","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Bhuria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankita","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"issue":"7","key":"48197_CR1","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/S0142-0615(01)00067-9","volume":"24","author":"MA Abido","year":"2002","unstructured":"Abido, M. A. Optimal power flow using particle swarm optimization. Int. J. Electr. Power Energy Syst. 24(7), 563\u2013571. https:\/\/doi.org\/10.1016\/S0142-0615(01)00067-9 (2002).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"48197_CR2","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.enconman.2014.06.088","volume":"87","author":"HREH Bouchekara","year":"2014","unstructured":"Bouchekara, H. R. E. H., Abido, M. A. & Chaib, A. E. Optimal power flow using the league championship algorithm: A case study of the Algerian power system. Energy Convers. Manage. 87, 58\u201370. https:\/\/doi.org\/10.1016\/j.enconman.2014.06.088 (2014).","journal-title":"Energy Convers. Manage."},{"key":"48197_CR3","doi-asserted-by":"publisher","first-page":"17881","DOI":"10.1007\/s00521-024-10312-0","volume":"36","author":"U Mittal","year":"2024","unstructured":"Mittal, U., Nangia, U. & Jain, N. K. An in-depth examination of artificial intelligence-based methods for optimal power flow solutions. Neural Comput. Appl. 36, 17881\u201317929. https:\/\/doi.org\/10.1007\/s00521-024-10312-0 (2024).","journal-title":"Neural Comput. Appl."},{"key":"48197_CR4","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s40565-014-0089-4","volume":"2","author":"M Niu","year":"2014","unstructured":"Niu, M., Wan, C. & Xu, Z. A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems. J. Mod. Power Syst. Clean. Energy. 2, 289. https:\/\/doi.org\/10.1007\/s40565-014-0089-4 (2014).","journal-title":"J. Mod. Power Syst. Clean. Energy"},{"key":"48197_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2025.110230","volume":"123","author":"U Mittal","year":"2025","unstructured":"Mittal, U., Nangia, U., Jain, N. K. & Gupta, S. Optimal power flow solutions for normal and critical loading scenarios using hybrid Rao-2 sine cosine algorithm. Comput. Electr. Eng. 123, 110230. https:\/\/doi.org\/10.1016\/j.compeleceng.2025.110230 (2025).","journal-title":"Comput. Electr. Eng."},{"issue":"14","key":"48197_CR6","doi-asserted-by":"publisher","first-page":"2672","DOI":"10.1049\/rpg2.13113","volume":"18","author":"NH Khan","year":"2024","unstructured":"Khan, N. H. et al. Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm. IET Renew. Power Gener. 18(14), 2672\u20132693. https:\/\/doi.org\/10.1049\/rpg2.13113 (2024).","journal-title":"IET Renew. Power Gener."},{"key":"48197_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2025.104553","volume":"26","author":"R Jamal","year":"2025","unstructured":"Jamal, R., Khan, N. H., Ebeed, M. & Zeinoddini-Meymand, H. An improved pelican optimization algorithm for solving stochastic optimal power flow problem of power systems considering uncertainty of renewable energy resources. Results Eng. 26, 104553. https:\/\/doi.org\/10.1016\/j.rineng.2025.104553 (2025).","journal-title":"Results Eng."},{"issue":"4","key":"48197_CR8","doi-asserted-by":"publisher","first-page":"3823","DOI":"10.1007\/s00202-023-02188-5","volume":"106","author":"AM Abd-El Wahab","year":"2024","unstructured":"Abd-El Wahab, A. M., Kamel, S., Sultan, H. M., Hassan, M. H. & Ruiz-Rodr\u00edguez, F. J. Optimizing reactive power dispatch in electrical networks using a hybrid artificial rabbits and gradient-based optimization. Electr. Eng. 106 (4), 3823\u20133851. https:\/\/doi.org\/10.1007\/s00202-023-02188-5 (2024).","journal-title":"Electr. Eng."},{"key":"48197_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s00202-024-02937-0","author":"H Merah","year":"2025","unstructured":"Merah, H. et al. Solving single- and multi-objective optimal power flow problems using the spider wasp optimization algorithm. Electr Eng. https:\/\/doi.org\/10.1007\/s00202-024-02937-0 (2025).","journal-title":"Electr Eng."},{"key":"48197_CR10","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.asoc.2014.08.056","volume":"24","author":"HREH Bouchekara","year":"2014","unstructured":"Bouchekara, H. R. E. H. Optimal power flow using black-hole-based optimization approach. Appl. Soft Comput. J. 24, 879\u2013888. https:\/\/doi.org\/10.1016\/j.asoc.2014.08.056 (2014).","journal-title":"Appl. Soft Comput. J."},{"key":"48197_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-014-1164-x","volume":"39","author":"AM Abusorrah","year":"2014","unstructured":"Abusorrah, A. M. The application of the linear adaptive genetic algorithm to optimal power flow problem. Arab. J. Sci. Eng. 39, 4901. https:\/\/doi.org\/10.1007\/s13369-014-1164-x (2014).","journal-title":"Arab. J. Sci. Eng."},{"key":"48197_CR12","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.epsr.2016.09.025","volume":"142","author":"AAA 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."},{"issue":"6","key":"48197_CR13","doi-asserted-by":"publisher","first-page":"1540","DOI":"10.1016\/j.jestch.2017.12.009","volume":"20","author":"A Saha","year":"2017","unstructured":"Saha, A., Das, P. & Chakraborty, A. K. Engineering science and technology, an international journal water evaporation algorithm\u202f: A new metaheuristic algorithm towards the solution of optimal power flow. Eng. Sci. Technol. Int. J. 20(6), 1540\u20131552. https:\/\/doi.org\/10.1016\/j.jestch.2017.12.009 (2017).","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"48197_CR14","doi-asserted-by":"publisher","first-page":"3571","DOI":"10.1007\/s00521-016-2265-0","volume":"28","author":"S Duman","year":"2017","unstructured":"Duman, S. Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Comput. Appl. 28, 3571\u20133585. https:\/\/doi.org\/10.1007\/s00521-016-2265-0 (2017).","journal-title":"Neural Comput. Appl."},{"key":"48197_CR15","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.asoc.2017.12.026","volume":"64","author":"AA El-Fergany","year":"2018","unstructured":"El-Fergany, A. A. & Hasanien, H. M. Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons. Appl. Soft Comput. 64, 307\u2013316. https:\/\/doi.org\/10.1016\/j.asoc.2017.12.026 (2018).","journal-title":"Appl. Soft Comput."},{"key":"48197_CR16","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. Electrical power and energy systems 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":"48197_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2025.110922","volume":"131","author":"M Lahdeb","year":"2026","unstructured":"Lahdeb, M. et al. Hybrid brown-bear and hippopotamus optimization with quasi-opposition-based learning for optimal power flow with renewable energy integration.. Comput. Electr. Eng. 131, 110922 (2026).","journal-title":"Comput. Electr. Eng."},{"issue":"1","key":"48197_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-09976-y","volume":"15","author":"A Abaza","year":"2025","unstructured":"Abaza, A., El-Sehiemy, R. A., Elbarabry, Z. & Barakat, A. F. Generic optimal power flow solution associated with technical improvements and emission reduction by multi-objective ARO algorithm. Sci. Rep. 15(1), 26524 (2025).","journal-title":"Sci. Rep."},{"key":"48197_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.128163","author":"S Gupta","year":"2025","unstructured":"Gupta, S. et al. Optimal power flow solution using novel optimization technique: A case study. Expert Syst. Appl. https:\/\/doi.org\/10.1016\/j.eswa.2025.128163 (2025).","journal-title":"Expert Syst. Appl."},{"key":"48197_CR20","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1016\/j.egyr.2023.12.053","volume":"11","author":"M Ebeed","year":"2024","unstructured":"Ebeed, M. et al. A modified artificial hummingbird algorithm for solving optimal power flow problem in power systems. Energy Rep. 11, 982\u20131005 (2024).","journal-title":"Energy Rep."},{"key":"48197_CR21","doi-asserted-by":"publisher","first-page":"131684","DOI":"10.1016\/j.energy.2024.131684","volume":"301","author":"R Jamal","year":"2024","unstructured":"Jamal, R. et al. Chaotic-quasi-oppositional-phasor based multi populations gorilla troop optimizer for optimal power flow solution. Energy 301, 131684 (2024).","journal-title":"Energy"},{"issue":"11","key":"48197_CR22","doi-asserted-by":"publisher","first-page":"15974","DOI":"10.1007\/s11227-024-06043-7","volume":"80","author":"U Mittal","year":"2024","unstructured":"Mittal, U., Nangia, U., Jain, N. K. & Gupta, S. Optimal power flow solution using a learning-based sine\u2013cosine algorithm. J. Supercomput. 80(11), 15974\u201316012 (2024).","journal-title":"J. Supercomput."},{"key":"48197_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jestch.2023.101551","volume":"47","author":"M Ghasemi","year":"2023","unstructured":"Ghasemi, M., Trojovsk\u00fd, P., Trojovsk\u00e1, E. & Zare, M. Gaussian bare-bones L\u00e9vy circulatory system-based optimization for power flow in the presence of renewable units. Eng. Sci. Technol. Int. J. 47, 101551. https:\/\/doi.org\/10.1016\/j.jestch.2023.101551 (2023).","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"48197_CR24","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"},{"issue":"12","key":"48197_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2024.103784","volume":"16","author":"MAM Shaheen","year":"2025","unstructured":"Shaheen, M. A. M., El-Sehiemy, R. A. & Elattar, E. E. Optimal power flow in power systems with renewable energy resources uncertainty including geothermal power plants. Ain Shams Eng. J. 16(12), 103784. https:\/\/doi.org\/10.1016\/j.asej.2024.103784 (2025).","journal-title":"Ain Shams Eng. J."},{"issue":"21","key":"48197_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/en15218303","volume":"15","author":"MAM Shaheen","year":"2022","unstructured":"Shaheen, M. A. M., El-Sehiemy, R. A. & Elattar, E. E. Solution of probabilistic optimal power flow incorporating renewable energy uncertainty using a novel circle search algorithm. Energies 15(21), 8303. https:\/\/doi.org\/10.3390\/en15218303 (2022).","journal-title":"Energies"},{"issue":"1","key":"48197_CR27","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert, D. H. & Macready, W. G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67\u201382 (1997).","journal-title":"IEEE Trans. Evol. Comput."},{"key":"48197_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/etep.2743","author":"MA Taher","year":"2018","unstructured":"Taher, M. A., Kamel, S., Jurado, F. & Ebeed, M. An improved moth-flame optimization algorithm for solving optimal power flow problem. Int. Trans. ElectrEnerg Syst. https:\/\/doi.org\/10.1002\/etep.2743 (2018).","journal-title":"Int. Trans. ElectrEnerg Syst."},{"issue":"1","key":"48197_CR29","first-page":"19","volume":"7","author":"RV Rao","year":"2016","unstructured":"Rao, R. V. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput 7(1), 19\u201334 (2016).","journal-title":"Int. J. Ind. Eng. Comput"},{"key":"48197_CR30","doi-asserted-by":"publisher","unstructured":"Lee, K. Y., Park, Y. M. & Ortiz, J. L. A united approach to optimal real and reactive power dispatch.\u00a0IEEE Trans. Power Apparatus Syst.\u00a0PAS-104(5), 1147\u20131153 (1985). https:\/\/doi.org\/10.1109\/TPAS.1985.323466","DOI":"10.1109\/TPAS.1985.323466"},{"issue":"1","key":"48197_CR31","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.ijepes.2013.04.021","volume":"53","author":"M Rezaei Adaryani","year":"2013","unstructured":"Rezaei Adaryani, M. & Karami, A. Artificial bee colony algorithm for solving multi-objective optimal power flow problem. Int. J. Electr. Power Energy Syst. 53 (1), 219\u2013230. https:\/\/doi.org\/10.1016\/j.ijepes.2013.04.021 (2013).","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"48197_CR32","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":"48197_CR33","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"},{"issue":"13","key":"48197_CR34","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(13), 1548\u20131559. https:\/\/doi.org\/10.1080\/15325008.2015.1041625 (2015).","journal-title":"Electr. Power Compon. Syst."},{"key":"48197_CR35","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.ins.2014.09.051","volume":"294","author":"M Ghasemi","year":"2015","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, 286\u2013304. https:\/\/doi.org\/10.1016\/j.ins.2014.09.051 (2015).","journal-title":"Inf. Sci."},{"key":"48197_CR36","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":"48197_CR37","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1016\/j.egyr.2023.12.053","volume":"11","author":"M Ebeed","year":"2024","unstructured":"Ebeed, M. et al. A modified artificial hummingbird algorithm for solving optimal power flow problem in power systems. Energy Rep. 11, 982\u20131005. https:\/\/doi.org\/10.1016\/j.egyr.2023.12.053 (2024).","journal-title":"Energy Rep."},{"key":"48197_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.131684","volume":"301","author":"R Jamal","year":"2024","unstructured":"Jamal, R. et al. Chaotic-quasi-oppositional-phasor based multi populations gorilla troop optimizer for optimal power flow solution. Energy 301, 131684. https:\/\/doi.org\/10.1016\/j.energy.2024.131684 (2024).","journal-title":"Energy"},{"key":"48197_CR39","doi-asserted-by":"publisher","first-page":"3883","DOI":"10.1016\/j.egyr.2024.09.020","volume":"12","author":"NH Khan","year":"2024","unstructured":"Khan, N. H. et al. Solving optimal power flow frameworks using modified artificial rabbit optimizer. Energy Rep. 12, 3883\u20133903. https:\/\/doi.org\/10.1016\/j.egyr.2024.09.020 (2024).","journal-title":"Energy Rep."},{"issue":"10","key":"48197_CR40","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1049\/piee.1968.0260","volume":"115","author":"LL Freris.","year":"1968","unstructured":"Freris., L. L. & Sasson, A. M. Investigation of the load-flow problem. Proc. Inst. Electr. Eng. 115(10), 1459\u201370. https:\/\/doi.org\/10.1049\/piee.1968.0260 (1968).","journal-title":"Proc. Inst. Electr. Eng."},{"key":"48197_CR41","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.epsr.2016.06.006","volume":"140","author":"V Roberge","year":"2016","unstructured":"Roberge, V., Tarbouchi, M. & Okou, F. Optimal power flow based on parallel metaheuristics for graphics processing units. Electr. Power Syst. Res. 140, 344\u2013353. https:\/\/doi.org\/10.1016\/j.epsr.2016.06.006 (2016).","journal-title":"Electr. Power Syst. Res."},{"key":"48197_CR42","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."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-48197-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-48197-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-48197-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:36:55Z","timestamp":1786667815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-48197-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,15]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["48197"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-48197-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,15]]},"assertion":[{"value":"15 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"25461"}}