{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:23:57Z","timestamp":1780637037410,"version":"3.54.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CBET-1953222"],"award-info":[{"award-number":["CBET-1953222"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s00521-021-06885-9","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T19:18:44Z","timestamp":1641928724000},"page":"7711-7731","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":117,"title":["Adaptive grey wolf optimizer"],"prefix":"10.1007","volume":"34","author":[{"given":"Kazem","family":"Meidani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"AmirPouya","family":"Hemmasian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyedali","family":"Mirjalili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2952-8576","authenticated-orcid":false,"given":"Amir","family":"Barati Farimani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"6885_CR1","unstructured":"Soerensen JS, Johannesen L, Grove U, Lundhus K, Couderc JP, Graff C (2010) A comparison of IIR and wavelet filtering for noise reduction of the ECG. In: 2010 computing in cardiology (IEEE, 2010), pp. 489\u2013492"},{"key":"6885_CR2","first-page":"185","volume-title":"Computational intelligence for multimedia big data on the cloud with engineering applications. Intelligent data-centric systems","author":"M Abdel-Basset","year":"2018","unstructured":"Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018) Chapter 10 - metaheuristic algorithms: a comprehensive review. In: Sangaiah AK, Sheng M, Zhang Z (eds) Computational intelligence for multimedia big data on the cloud with engineering applications. Intelligent data-centric systems. Academic Press, London, pp 185\u2013231"},{"key":"6885_CR3","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, London"},{"key":"6885_CR4","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: proceedings of ICNN\u201995-international conference on neural networks, vol.\u00a04 (IEEE, 1995), vol.\u00a04, pp. 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"4","key":"6885_CR5","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution - a simple and efficient Heuristic for global optimization over continuous spaces. J Global Optim 11(4):341. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J Global Optim"},{"issue":"1","key":"6885_CR6","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1(1):67","journal-title":"IEEE Trans Evolut Comput"},{"issue":"3","key":"6885_CR7","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1287\/ijoc.1.3.190","volume":"1","author":"F Glover","year":"1989","unstructured":"Glover F (1989) Tabu search-part I. ORSA J Comput 1(3):190. https:\/\/doi.org\/10.1287\/ijoc.1.3.190","journal-title":"ORSA J Comput"},{"key":"6885_CR8","volume-title":"Handbook of metaheuristics","author":"F Glover","year":"2002","unstructured":"Glover F, Kochenberger G (2002) Iterated local search. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics. Kluwer, Netherlands"},{"key":"6885_CR9","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195099713.001.0001","volume-title":"Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms","author":"T B\u00e4ck","year":"1996","unstructured":"B\u00e4ck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford, UK"},{"issue":"6","key":"6885_CR10","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","volume":"12","author":"D Simon","year":"2008","unstructured":"Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702. https:\/\/doi.org\/10.1109\/TEVC.2008.919004","journal-title":"IEEE Trans Evolut Comput"},{"issue":"13","key":"6885_CR11","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232","journal-title":"Inf Sci"},{"issue":"2","key":"6885_CR12","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.advengsoft.2005.04.005","volume":"37","author":"OK Erol","year":"2006","unstructured":"Erol OK, Eksin I (2006) A new optimization method: big bang-big crunch. Adv Eng Softw 37(2):106. https:\/\/doi.org\/10.1016\/j.advengsoft.2005.04.005","journal-title":"Adv Eng Softw"},{"issue":"4","key":"6885_CR13","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo M, Birattari M, St\u00fctzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28","journal-title":"IEEE Comput Intell Mag"},{"issue":"3","key":"6885_CR14","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459","journal-title":"J Global Optim"},{"key":"6885_CR15","doi-asserted-by":"crossref","unstructured":"Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: proceedings of the sixth international symposium on micro machine and human science, pp. 39\u201343","DOI":"10.1109\/MHS.1995.494215"},{"key":"6885_CR16","doi-asserted-by":"crossref","unstructured":"Yang XS, Deb S (2009) Cuckoo search via L\u00e9vy flights. In: 2009 world congress on nature & biologically inspired computing, NaBIC 2009. (IEEE, 2009), pp. 210\u2013214","DOI":"10.1109\/NABIC.2009.5393690"},{"issue":"3","key":"6885_CR17","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MCS.2002.1004010","volume":"22","author":"K Passino","year":"2002","unstructured":"Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52. https:\/\/doi.org\/10.1109\/MCS.2002.1004010","journal-title":"IEEE Control Syst Mag"},{"key":"6885_CR18","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 SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46","journal-title":"Adv Eng Softw"},{"key":"6885_CR19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51","journal-title":"Adv Eng Softw"},{"key":"6885_CR20","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","journal-title":"Knowl Based Syst"},{"issue":"4","key":"6885_CR21","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053. https:\/\/doi.org\/10.1007\/s00521-015-1920-1","journal-title":"Neural Comput Appl"},{"key":"6885_CR22","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","volume":"105","author":"S Saremi","year":"2017","unstructured":"Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.01.004","journal-title":"Adv Eng Softw"},{"key":"6885_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2015.01.010","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw. https:\/\/doi.org\/10.1016\/j.advengsoft.2015.01.010","journal-title":"Adv Eng Softw"},{"key":"6885_CR24","doi-asserted-by":"publisher","first-page":"78012","DOI":"10.1109\/ACCESS.2019.2921793","volume":"7","author":"Q Tu","year":"2019","unstructured":"Tu Q, Chen X, Liu X (2019) Hierarchy strengthened grey wolf optimizer for numerical optimization and feature selection. IEEE Access 7:78012. https:\/\/doi.org\/10.1109\/ACCESS.2019.2921793","journal-title":"IEEE Access"},{"key":"6885_CR25","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.asoc.2017.06.044","volume":"60","author":"AA Heidari","year":"2017","unstructured":"Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with L\u00e9vy flight for optimization tasks. Appl Soft Comput 60:115. https:\/\/doi.org\/10.1016\/j.asoc.2017.06.044","journal-title":"Appl Soft Comput"},{"key":"6885_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.eswa.2018.04.012","volume":"107","author":"C Lu","year":"2018","unstructured":"Lu C, Gao L, Yi J (2018) Grey wolf optimizer with cellular topological structure. Exp Syst Appl 107:89. https:\/\/doi.org\/10.1016\/j.eswa.2018.04.012","journal-title":"Exp Syst Appl"},{"issue":"1","key":"6885_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13198-020-00995-8","volume":"12","author":"G Negi","year":"2021","unstructured":"Negi G, Kumar A, Pant S, Ram M (2021) GWO: a review and applications. Int J Syst Assur Eng Manag 12(1):1. https:\/\/doi.org\/10.1007\/s13198-020-00995-8","journal-title":"Int J Syst Assur Eng Manag"},{"issue":"2","key":"6885_CR28","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s00521-017-3272-5","volume":"30","author":"H Faris","year":"2018","unstructured":"Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30(2):413. https:\/\/doi.org\/10.1007\/s00521-017-3272-5","journal-title":"Neural Comput Appl"},{"key":"6885_CR29","doi-asserted-by":"crossref","unstructured":"Malik MRS, Mohideen ER, Ali L (2015) Weighted distance grey wolf optimizer for global optimization problems. In: 2015 IEEE international conference on computational intelligence and computing research (ICCIC)","DOI":"10.1109\/ICCIC.2015.7435714"},{"key":"6885_CR30","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/7950348","author":"N Mittal","year":"2016","unstructured":"Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comp Intell Soft Comput. https:\/\/doi.org\/10.1155\/2016\/7950348","journal-title":"Appl Comp Intell Soft Comput"},{"issue":"1","key":"6885_CR31","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/s00521-016-2357-x","volume":"28","author":"W Long","year":"2017","unstructured":"Long W, Liang X, Cai S, Jiao J, Zhang W (2017) A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems. Neural Comput Appl 28(1):421. https:\/\/doi.org\/10.1007\/s00521-016-2357-x","journal-title":"Neural Comput Appl"},{"key":"6885_CR32","doi-asserted-by":"publisher","unstructured":"Rodr\u00edguez L, Castillo O, Soria J (2016) Grey wolf optimizer with dynamic adaptation of parameters using fuzzy logic. In: 2016 IEEE congress on evolutionary computation (CEC), pp. 3116\u20133123. https:\/\/doi.org\/10.1109\/CEC.2016.7744183","DOI":"10.1109\/CEC.2016.7744183"},{"key":"6885_CR33","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.asoc.2017.03.048","volume":"57","author":"L Rodr\u00cdguez","year":"2017","unstructured":"Rodr\u00cdguez L, Castillo O, Soria J, Melin P, Valdez F, Gonzalez CI, Martinez GE, Soto J (2017) A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl Soft Comput 57:315. https:\/\/doi.org\/10.1016\/j.asoc.2017.03.048","journal-title":"Appl Soft Comput"},{"issue":"1","key":"6885_CR34","doi-asserted-by":"publisher","first-page":"1256083","DOI":"10.1080\/23311916.2016.1256083","volume":"3","author":"K Dudani","year":"2016","unstructured":"Dudani K, Chudasama A (2016) Partial discharge detection in transformer using adaptive grey wolf optimizer based acoustic emission technique. Cogent Eng 3(1):1256083. https:\/\/doi.org\/10.1080\/23311916.2016.1256083","journal-title":"Cogent Eng"},{"key":"6885_CR35","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.engappai.2017.10.024","volume":"68","author":"W Long","year":"2018","unstructured":"Long W, Jiao J, Liang X, Tang M (2018) An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Eng Appl Artif Intell 68:63. https:\/\/doi.org\/10.1016\/j.engappai.2017.10.024","journal-title":"Eng Appl Artif Intell"},{"key":"6885_CR36","doi-asserted-by":"publisher","first-page":"113810","DOI":"10.1109\/ACCESS.2019.2934994","volume":"7","author":"W Long","year":"2019","unstructured":"Long W, Jiao J, Liang X, Cai S, Xu M (2019) A random opposition-based learning grey wolf optimizer. IEEE Access 7:113810. https:\/\/doi.org\/10.1109\/ACCESS.2019.2934994","journal-title":"IEEE Access"},{"key":"6885_CR37","doi-asserted-by":"crossref","unstructured":"Sharma S, Salgotra R, Singh U (2017) An enhanced grey wolf optimizer for numerical optimization. In: 2017 international conference on innovations in information, embedded and communication systems (ICIIECS)","DOI":"10.1109\/ICIIECS.2017.8275908"},{"key":"6885_CR38","doi-asserted-by":"publisher","first-page":"113907","DOI":"10.1016\/j.eswa.2020.113917","volume":"166","author":"MH Nadimi-Shahraki","year":"2021","unstructured":"Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Exp Syst Appl 166:113907. https:\/\/doi.org\/10.1016\/j.eswa.2020.113917","journal-title":"Exp Syst Appl"},{"key":"6885_CR39","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/481360","author":"S Zhang","year":"2015","unstructured":"Zhang S, Zhou Y (2015) Grey wolf optimizer based on powell local optimization method for clustering analysis. Discrete Dyn Nat Soc. https:\/\/doi.org\/10.1155\/2015\/481360","journal-title":"Discrete Dyn Nat Soc"},{"key":"6885_CR40","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.enconman.2015.04.005","volume":"98","author":"B Mahdad","year":"2015","unstructured":"Mahdad B, Srairi K (2015) Blackout risk prevention in a smart grid based flexible optimal strategy using Grey Wolf-pattern search algorithms. Energy Convers Manag 98:411. https:\/\/doi.org\/10.1016\/j.enconman.2015.04.005","journal-title":"Energy Convers Manag"},{"issue":"5","key":"6885_CR41","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1007\/s00521-014-1806-7","volume":"26","author":"S Saremi","year":"2015","unstructured":"Saremi S, Mirjalili SZ, Mirjalili SM (2015) Evolutionary population dynamics and grey wolf optimizer. Neural Comput Appl 26(5):1257. https:\/\/doi.org\/10.1007\/s00521-014-1806-7","journal-title":"Neural Comput Appl"},{"key":"6885_CR42","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1016\/j.energy.2016.05.105","volume":"111","author":"T Jayabarathi","year":"2016","unstructured":"Jayabarathi T, Raghunathan T, Adarsh B, Suganthan PN (2016) Economic dispatch using hybrid grey wolf optimizer. Energy 111:630. https:\/\/doi.org\/10.1016\/j.energy.2016.05.105","journal-title":"Energy"},{"issue":"1","key":"6885_CR43","doi-asserted-by":"publisher","first-page":"7181","DOI":"10.1038\/s41598-019-43546-3","volume":"9","author":"JS Wang","year":"2019","unstructured":"Wang JS, Li SX (2019) An improved grey wolf optimizer based on differential evolution and elimination mechanism. Sci Rep 9(1):7181. https:\/\/doi.org\/10.1038\/s41598-019-43546-3","journal-title":"Sci Rep"},{"issue":"4","key":"6885_CR44","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1016\/j.jestch.2016.07.004","volume":"19","author":"D Guha","year":"2016","unstructured":"Guha D, Roy PK, Banerjee S (2016) Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm. Eng Sci Technol Int J 19(4):1693. https:\/\/doi.org\/10.1016\/j.jestch.2016.07.004","journal-title":"Eng Sci Technol Int J"},{"key":"6885_CR45","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.jocs.2018.06.008","volume":"27","author":"PJ Gaidhane","year":"2018","unstructured":"Gaidhane PJ, Nigam MJ (2018) A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems. J Comput Sci 27:284. https:\/\/doi.org\/10.1016\/j.jocs.2018.06.008","journal-title":"J Comput Sci"},{"key":"6885_CR46","doi-asserted-by":"publisher","first-page":"68764","DOI":"10.1109\/ACCESS.2019.2917803","volume":"7","author":"AA Alomoush","year":"2019","unstructured":"Alomoush AA, Alsewari AA, Alamri HS, Aloufi K, Zamli KZ (2019) Hybrid harmony search algorithm with grey wolf optimizer and modified opposition-based learning. IEEE Access 7:68764. https:\/\/doi.org\/10.1109\/ACCESS.2019.2917803","journal-title":"IEEE Access"},{"key":"6885_CR47","doi-asserted-by":"publisher","DOI":"10.1145\/2996355","author":"A Aleti","year":"2016","unstructured":"Aleti A, Moser I (2016) A systematic literature review of adaptive parameter control methods for evolutionary algorithms. ACM Comput Surv. https:\/\/doi.org\/10.1145\/2996355","journal-title":"ACM Comput Surv"},{"issue":"6","key":"6885_CR48","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1109\/TSMCB.2009.2015956","volume":"39","author":"ZH Zhan","year":"2009","unstructured":"Zhan ZH, Zhang J, Li Y, Chung HSH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B (Cybernetics) 39(6):1362. https:\/\/doi.org\/10.1109\/TSMCB.2009.2015956","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybernetics)"},{"key":"6885_CR49","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/j.asoc.2015.10.039","volume":"38","author":"MK Naik","year":"2016","unstructured":"Naik MK, Panda R (2016) A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl Soft Comput 38:661. https:\/\/doi.org\/10.1016\/j.asoc.2015.10.039","journal-title":"Appl Soft Comput"},{"key":"6885_CR50","unstructured":"You K, Long M, Wang J, Jordan MI (2019) How does learning rate decay help modern neural networks?"},{"key":"6885_CR51","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7189653","author":"F Yan","year":"2019","unstructured":"Yan F, Xu J, Yun K (2019) Dynamically dimensioned search grey wolf optimizer based on positional interaction information. Complexity. https:\/\/doi.org\/10.1155\/2019\/7189653","journal-title":"Complexity"},{"key":"6885_CR52","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"6885_CR53","unstructured":"Kingma DP, Ba J (2017) Adam: a method for stochastic optimization"},{"key":"6885_CR54","unstructured":"Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: proceedings of the 30th international conference on international conference on machine learning - Vol 28 (JMLR.org, 2013), ICML\u201913, p. III-1139-III-1147"},{"issue":"4","key":"6885_CR55","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1080\/00207160108805080","volume":"77","author":"J Digalakis","year":"2001","unstructured":"Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77(4):481. https:\/\/doi.org\/10.1080\/00207160108805080","journal-title":"Int J Comput Math"},{"key":"6885_CR56","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.asoc.2018.05.006","volume":"69","author":"MH Qais","year":"2018","unstructured":"Qais MH, Hasanien HM, Alghuwainem S (2018) Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems. Appl Soft Comput 69:504. https:\/\/doi.org\/10.1016\/j.asoc.2018.05.006","journal-title":"Appl Soft Comput"},{"key":"6885_CR57","unstructured":"Zielinski K, Peters D, Laur R (2005) Stopping criteria for single-objective optimization"},{"key":"6885_CR58","first-page":"51","volume":"31","author":"K Zielinski","year":"2007","unstructured":"Zielinski K, Laur R (2007) Stopping criteria for a constrained single-objective particle swarm optimization algorithm. Informatica (Slovenia) 31:51","journal-title":"Informatica (Slovenia)"},{"key":"6885_CR59","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.fluid.2016.06.037","volume":"427","author":"JA Fern\u00e1ndez-Vargas","year":"2016","unstructured":"Fern\u00e1ndez-Vargas JA, Bonilla-Petriciolet A, Rangaiah GP, Fateen SEK (2016) Performance analysis of stopping criteria of population-based metaheuristics for global optimization in phase equilibrium calculations and modeling. Fluid Phase Equilibria 427:104. https:\/\/doi.org\/10.1016\/j.fluid.2016.06.037","journal-title":"Fluid Phase Equilibria"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06885-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06885-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06885-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T12:19:44Z","timestamp":1650284384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06885-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,11]]},"references-count":59,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["6885"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06885-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,11]]},"assertion":[{"value":"17 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2022","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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The code of the algorithm can be accessed from:\u00a0.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}