{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T12:10:53Z","timestamp":1780056653833,"version":"3.54.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00521-026-11859-w","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:23:10Z","timestamp":1776842590000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid AEO\u2013MFO for optimal reactive power dispatch: addressing time-varying load demand and uncertainty in renewable energy sources"],"prefix":"10.1007","volume":"38","author":[{"given":"Amal Amin","family":"Mohamed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salah","family":"Kamel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed H.","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"issue":"11","key":"11859_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/math11112591","volume":"11","author":"SZ Almutairi","year":"2023","unstructured":"Almutairi SZ, Mohamed EA, El-Sousy FF (2023) A novel adaptive manta-ray foraging optimization for stochastic ORPD considering uncertainties of wind power and load demand. Mathematics 11(11):2591","journal-title":"Mathematics"},{"key":"11859_CR2","doi-asserted-by":"crossref","unstructured":"Londo\u00f1o DC, Villa-Acevedo WM, L\u00f3pez-Lezama JM (2019) Assessment of metaheuristic techniques applied to the optimal reactive power dispatch. In: Workshop on engineering applications. Springer, Cham, pp 250\u2013262","DOI":"10.1007\/978-3-030-31019-6_22"},{"key":"11859_CR3","unstructured":"International Renewable Energy Agency. Global energy transformation: a roadmap to 2050. https:\/\/www.irena.org\/publications\/2019\/Apr\/Global-energy-transformation-A-roadmap-to-20502019Edition. Accessed January 2020"},{"key":"11859_CR4","doi-asserted-by":"crossref","unstructured":"Naidji M, Boudour M (2016) Impact of increased distributed photovoltaic generation on radial distribution networks. Paper presented at: 2016 International Conference on Control, Decision and Information Technologies (CoDIT); St. Julian\u2019s, Malta, pp. 292\u2013295. April 6\u20138","DOI":"10.1109\/CoDIT.2016.7593576"},{"key":"11859_CR5","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s11708-017-0457-7","volume":"11","author":"SS Reddy","year":"2017","unstructured":"Reddy SS, Sandeep V, Jung CM (2017) Review of stochastic optimization methods for smart grid. Front Energy 11:197\u2013209","journal-title":"Front Energy"},{"key":"11859_CR6","doi-asserted-by":"crossref","unstructured":"Kamel S, Ramadan A, Ebeed M, Yu J, Xie K, Wu T (2019) Assessment integration of wind-based DG and DSTATCOM in Egyptian distribution grid considering load demand uncertainty. Paper presented at: 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia); Chengdu, China, pp. 1288\u20131293. May 21\u201324","DOI":"10.1109\/ISGT-Asia.2019.8881437"},{"key":"11859_CR7","doi-asserted-by":"publisher","first-page":"43","DOI":"10.13052\/jge1904-4720.7123","volume":"7","author":"SS Reddy","year":"2017","unstructured":"Reddy SS (2017) Optimization of renewable energy resources in hybrid energy systems. J Green Eng 7:43\u201360","journal-title":"J Green Eng"},{"key":"11859_CR8","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1016\/j.renene.2016.10.022","volume":"101","author":"SS Reddy","year":"2017","unstructured":"Reddy SS (2017) Optimal scheduling of thermal-wind-solar power system with storage. Renew Energy 101:1357\u20131368","journal-title":"Renew Energy"},{"issue":"1","key":"11859_CR9","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1049\/ip-gtd:20041150","volume":"152","author":"JM Ramos","year":"2005","unstructured":"Ramos JM, Exposito AG, Quintana VH (2005) Transmission power loss reduction by interior-point methods: implementation issues and practical experience. IEE Proc Gen Transm Distrib 152(1):90\u201398","journal-title":"IEE Proc Gen Transm Distrib"},{"issue":"4","key":"11859_CR10","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1109\/59.736232","volume":"13","author":"N Grudinin","year":"1998","unstructured":"Grudinin N (1998) Reactive power optimization using successive quadratic programming method. IEEE Trans Power Syst 13(4):1219\u20131225","journal-title":"IEEE Trans Power Syst"},{"issue":"2","key":"11859_CR11","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.epsr.2010.10.005","volume":"81","author":"AA El Ela","year":"2011","unstructured":"El Ela AA, Abido MA, Spea SR (2011) Differential evolution algorithm for optimal reactive power dispatch. Electr Power Syst Res 81(2):458\u2013464","journal-title":"Electr Power Syst Res"},{"key":"11859_CR12","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.asoc.2017.05.057","volume":"59","author":"RNS Mei","year":"2017","unstructured":"Mei RNS, Sulaiman MH, Mustaffa Z, Daniyal H (2017) Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl Soft Comput 59:210\u2013222","journal-title":"Appl Soft Comput"},{"key":"11859_CR13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.asoc.2017.01.012","volume":"53","author":"E Naderi","year":"2017","unstructured":"Naderi E, Narimani H, Fathi M, Narimani MR (2017) A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch. Appl Soft Comput 53:441\u2013456","journal-title":"Appl Soft Comput"},{"key":"11859_CR14","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/j.asoc.2017.04.048","volume":"57","author":"AA Heidari","year":"2017","unstructured":"Heidari AA, Abbaspour RA, Jordehi AR (2017) Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Appl Soft Comput 57:657\u2013671","journal-title":"Appl Soft Comput"},{"key":"11859_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.06.009","author":"K Nuaekaew","year":"2017","unstructured":"Nuaekaew K, Artrit P, Pholdee N, Bureerat S (2017) Optimal reactive power dispatch problem using a two- archive multi-objective grey wolf optimizer. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2017.06.009","journal-title":"Expert Syst Appl"},{"issue":"1","key":"11859_CR16","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1108\/COMPEL-02-2015-0030","volume":"35","author":"G Chen","year":"2016","unstructured":"Chen G, Liu L, Guo Y, Huang S (2016) Multi-objective enhanced PSO algorithm for optimizing power losses and voltage deviation in power systems. COMPEL Int J Comput Math Electr Electron Eng 35(1):350\u2013372","journal-title":"COMPEL Int J Comput Math Electr Electron Eng"},{"key":"11859_CR17","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.renene.2016.02.064","volume":"94","author":"S Shargh","year":"2016","unstructured":"Shargh S, Mohammadi-ivatloo B, Seyedi H, Abapour M (2016) Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties. Renew Energy 94:10\u201321","journal-title":"Renew Energy"},{"key":"11859_CR18","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.ijepes.2015.08.009","volume":"75","author":"SM Mohseni-Bonab","year":"2016","unstructured":"Mohseni-Bonab SM, Rabiee A, Mohammadi-Ivatloo B, Jalilzadeh S, Nojavan S (2016) A two-point estimate method for uncertainty modeling in multi-objective optimal reactive power dispatch problem. Int J Electr Power Energy Syst 75:194\u2013204","journal-title":"Int J Electr Power Energy Syst"},{"key":"11859_CR19","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1016\/j.ijepes.2014.09.008","volume":"64","author":"RH Liang","year":"2015","unstructured":"Liang RH, Wang JC, Chen YT, Tseng WT (2015) An enhanced firefly algorithm to multi-objective optimal active\/reactive power dispatch with uncertainties consideration. Int J Electr Power Energy Syst 64:1088\u20131097","journal-title":"Int J Electr Power Energy Syst"},{"issue":"1","key":"11859_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s44196-024-00415-w","volume":"17","author":"SB Pandya","year":"2024","unstructured":"Pandya SB, Kalita K, \u010cep R, Jangir P, Chohan JS, Abualigah L (2024) Multi-objective snow ablation optimization algorithm: an elementary vision for security-constrained optimal power flow problem incorporating wind energy source with FACTS devices. Int J Comput Intell Syst 17(1):33","journal-title":"Int J Comput Intell Syst"},{"issue":"2","key":"11859_CR21","first-page":"99","volume":"8","author":"TP Chang","year":"2010","unstructured":"Chang TP (2010) Investigation on frequency distribution of global radiation using different probability density functions. Int J Appl Sci Eng 8(2):99\u2013107","journal-title":"Int J Appl Sci Eng"},{"key":"11859_CR22","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1016\/j.energy.2018.03.002","volume":"150","author":"PP Biswas","year":"2018","unstructured":"Biswas PP, Suganthan PN, Qu BY, Amaratunga GA (2018) Multi objective economic-environmental power dispatch with stochastic wind-solar-small hydro power. Energy 150:1039\u20131057","journal-title":"Energy"},{"key":"11859_CR23","doi-asserted-by":"publisher","first-page":"9383","DOI":"10.1007\/s00521-019-04452-x","volume":"32","author":"W Zhao","year":"2020","unstructured":"Zhao W, Wang L, Zhang Z (2020) Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Comput Appl 32:9383\u20139425","journal-title":"Neural Comput Appl"},{"key":"11859_CR24","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","volume":"89","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228\u2013249","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"11859_CR25","doi-asserted-by":"publisher","DOI":"10.1002\/2050-7038.12374","volume":"30","author":"M Naidji","year":"2020","unstructured":"Naidji M, Boudour M (2020) Stochastic multi-objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system. Int Trans Electr Energ Syst 30(6):e12374","journal-title":"Int Trans Electr Energ Syst"},{"issue":"4","key":"11859_CR26","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1109\/59.99399","volume":"5","author":"M Bjelogrlic","year":"1990","unstructured":"Bjelogrlic M, Calovic MS, Ristanovic P, Babic BS (1990) Application of Newton\u2019s optimal power flow in voltage\/reactive power control. IEEE Trans Power Syst 5(4):1447\u20131454","journal-title":"IEEE Trans Power Syst"},{"issue":"2","key":"11859_CR27","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1109\/JSYST.2011.2162994","volume":"6","author":"A Soroudi","year":"2011","unstructured":"Soroudi A, Aien M, Ehsan M (2011) A probabilistic modeling of photo voltaic modules and wind power generation impact on distribution networks. IEEE Syst J 6(2):254\u2013259","journal-title":"IEEE Syst J"},{"issue":"4","key":"11859_CR28","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1049\/iet-gtd.2016.1545","volume":"11","author":"SM Mohseni-Bonab","year":"2017","unstructured":"Mohseni-Bonab SM, Rabiee A (2017) Optimal reactive power dispatch: a review, and a new stochastic voltage stability constrained multi-objective model at the presence of uncertain wind power generation. IET Gener Transm Distrib 11(4):815\u2013829","journal-title":"IET Gener Transm Distrib"},{"issue":"2","key":"11859_CR29","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/TEC.2007.914171","volume":"23","author":"J Hetzer","year":"2008","unstructured":"Hetzer J, David CY, Bhattarai K (2008) An economic dispatch model incorporating wind power. IEEE Trans Energy Convers 23(2):603\u2013611","journal-title":"IEEE Trans Energy Convers"},{"key":"11859_CR30","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1016\/j.enconman.2017.06.071","volume":"148","author":"PP Biswas","year":"2017","unstructured":"Biswas PP, Suganthan PN, Amaratunga GA (2017) Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers Manag 148:1194\u20131207","journal-title":"Energy Convers Manag"},{"issue":"4","key":"11859_CR31","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1109\/JSYST.2014.2325967","volume":"9","author":"SS Reddy","year":"2014","unstructured":"Reddy SS, Bijwe PR, Abhyankar AR (2014) Real-time economic dispatch considering renewable power generation variability and uncertainty over scheduling period. IEEE Syst J 9(4):1440\u20131451","journal-title":"IEEE Syst J"},{"issue":"1","key":"11859_CR32","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1109\/TPWRS.2009.2030276","volume":"25","author":"YM Atwa","year":"2009","unstructured":"Atwa YM, El-Saadany EF, Salama MMA, Seethapathy R (2009) Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 25(1):360\u2013370","journal-title":"IEEE Trans Power Syst"},{"key":"11859_CR33","unstructured":"The MathWorks Inc (2016) Natick, Massachusetts, United States"},{"key":"11859_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.114194","volume":"388","author":"W Zhao","year":"2022","unstructured":"Zhao W, Wang L, Mirjalili S (2022) Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 388:114194","journal-title":"Comput Methods Appl Mech Eng"},{"key":"11859_CR35","doi-asserted-by":"publisher","first-page":"73182","DOI":"10.1109\/ACCESS.2019.2918753","volume":"7","author":"W Zhao","year":"2019","unstructured":"Zhao W, Wang L, Zhang Z (2019) Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access 7:73182\u201373206","journal-title":"IEEE Access"},{"key":"11859_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105190","volume":"191","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191:105190","journal-title":"Knowl Based Syst"},{"key":"11859_CR37","doi-asserted-by":"publisher","first-page":"162059","DOI":"10.1109\/ACCESS.2021.3133286","volume":"9","author":"M Dehghani","year":"2021","unstructured":"Dehghani M, Hub\u00e1lovsk\u00fd \u0160, Trojovsk\u00fd P (2021) Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems. IEEE Access 9:162059\u2013162080","journal-title":"IEEE Access"},{"issue":"7","key":"11859_CR38","doi-asserted-by":"publisher","first-page":"7305","DOI":"10.1007\/s11227-022-04959-6","volume":"79","author":"J Xue","year":"2023","unstructured":"Xue J, Shen B (2023) Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J Supercomput 79(7):7305\u20137336","journal-title":"J Supercomput"},{"key":"11859_CR39","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1016\/j.aej.2023.04.002","volume":"72","author":"MH Hassan","year":"2023","unstructured":"Hassan MH, Kamel S, Jurado F, Ebeed M, Elnaggar MF (2023) Economic load dispatch solution of large-scale power systems using an enhanced beluga whale optimizer. Alex Eng J 72:573\u2013591","journal-title":"Alex Eng J"},{"key":"11859_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2020.164559","volume":"208","author":"M Premkumar","year":"2020","unstructured":"Premkumar M, Babu TS, Umashankar S, Sowmya R (2020) A new metaphor-less algorithms for the photovoltaic cell parameter estimation. Optik 208:164559. https:\/\/doi.org\/10.1016\/j.ijleo.2020.164559","journal-title":"Optik"},{"key":"11859_CR41","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.asoc.2018.11.042","volume":"75","author":"PP Biswas","year":"2019","unstructured":"Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GAJ (2019) Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach. Appl Soft Comput 75:616\u2013632. https:\/\/doi.org\/10.1016\/j.asoc.2018.11.042","journal-title":"Appl Soft Comput"},{"key":"11859_CR42","unstructured":"\u201cPower systems test case archive. University of Washington, Seattle.\u201d https:\/\/labs.ece.uw.edu\/pstca\/. Accessed 30 Sep 2020"},{"key":"11859_CR43","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.asoc.2016.02.041","volume":"43","author":"A Rajan","year":"2016","unstructured":"Rajan A, Malakar T (2016) Exchange market algorithm based optimum reactive power dispatch. Appl Soft Comput 43:320\u2013336. https:\/\/doi.org\/10.1016\/j.asoc.2016.02.041","journal-title":"Appl Soft Comput"},{"key":"11859_CR44","doi-asserted-by":"publisher","first-page":"168721","DOI":"10.1109\/ACCESS.2020.3022846","volume":"8","author":"M Ebeed","year":"2020","unstructured":"Ebeed M, Ali A, Mosaad MI, Kamel S (2020) An improved lightning attachment procedure optimizer for optimal reactive power dispatch with uncertainty in renewable energy resources. IEEE Access 8:168721\u2013168731","journal-title":"IEEE Access"},{"issue":"3","key":"11859_CR45","doi-asserted-by":"publisher","DOI":"10.3390\/math10030346","volume":"10","author":"AM Abd-El Wahab","year":"2022","unstructured":"Abd-El Wahab AM, Kamel S, Hassan MH, Mosaad MI, AbdulFattah TA (2022) Optimal reactive power dispatch using a chaotic turbulent flow of water-based optimization algorithm. Mathematics 10(3):346","journal-title":"Mathematics"},{"key":"11859_CR46","doi-asserted-by":"publisher","first-page":"215399","DOI":"10.1109\/ACCESS.2020.3039571","volume":"8","author":"NH Khan","year":"2020","unstructured":"Khan NH, Wang Y, Tian D, Jamal R, Ebeed M, Deng Q (2020) Fractional PSOGSA algorithm approach to solve optimal reactive power dispatch problems with uncertainty of renewable energy resources. IEEE Access 8:215399\u2013215413","journal-title":"IEEE Access"},{"issue":"17","key":"11859_CR47","doi-asserted-by":"publisher","first-page":"4316","DOI":"10.3390\/en13174316","volume":"13","author":"M Ebeed","year":"2020","unstructured":"Ebeed M, Alhejji A, Kamel S, Jurado F (2020) Solving the optimal reactive power dispatch using marine predators algorithm considering the uncertainties in load and wind-solar generation systems. Energies 13(17):4316","journal-title":"Energies"},{"key":"11859_CR48","doi-asserted-by":"crossref","unstructured":"Ali MH, Soliman AMA, Abdeen M, Kandil T, Abdelaziz AY, El-Shahat A (2023) A novel stochastic optimizer solving optimal reactive power dispatch problem considering renewable energy resources","DOI":"10.3390\/en16041562"},{"issue":"c","key":"11859_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TSG.2019.2924025","volume":"PP","author":"V Bui","year":"2019","unstructured":"Bui V, Member S, Hussain A, Member S (2019) Double deep qlearning-based distributed operation of battery energy storage system considering uncertainties. IEEE Trans Smart Grid PP(c):1. https:\/\/doi.org\/10.1109\/TSG.2019.2924025","journal-title":"IEEE Trans Smart Grid"},{"key":"11859_CR50","doi-asserted-by":"crossref","unstructured":"Mohseni-Bonab SM, Rabiee A, Mohammadi-Ivatloo B (2017) Multi-objective optimal reactive power dispatch considering uncertainties in the wind integrated power systems. In: Reactive power control in AC power systems. Springer, pp 475\u2013513","DOI":"10.1007\/978-3-319-51118-4_12"},{"key":"11859_CR51","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.ijepes.2013.04.011","volume":"53","author":"B Mandal","year":"2013","unstructured":"Mandal B, Roy PK (2013) Optimal reactive power dispatch using quasi-oppositional teaching learning-based optimization. Int J Electr Power Energy Syst 53:123\u2013134","journal-title":"Int J Electr Power Energy Syst"},{"key":"11859_CR52","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.engappai.2017.10.019","volume":"68","author":"PP Biswas","year":"2018","unstructured":"Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GAJ (2018) 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","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"11859_CR53","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1162\/evco.1994.2.3.221","volume":"2","author":"N Srinivas","year":"1994","unstructured":"Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221\u2013248. https:\/\/doi.org\/10.1162\/evco.1994.2.3.221","journal-title":"Evol Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11859-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-11859-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11859-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:50:00Z","timestamp":1780055400000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-11859-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,22]]},"references-count":53,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["11859"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-11859-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,22]]},"assertion":[{"value":"8 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 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":"The authors declare that there is no conflict of interest regarding the publication of this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors. Declaration of Generative AI and AI-Assisted Technologies in the Writing Process: While preparing this work, the authors used ChatGPT to enhance language and readability. After using this tool, the authors reviewed and edited the content as necessary and take full responsibility for the publication\u2019s content.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"338"}}