{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:05:02Z","timestamp":1760234702296,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Neuroevolutionary models are used to predict magnetic hysteresis for barium hexaferrites (to predict magnetic hysteresis for barium hexaferrites). Magnetic hysteresis for a specific set of samples of barium hexaferrite doped with titanium were measured experimentally at room temperature and reported before. Neural networks are trained using these experimental data in order to generate magnetization and predict magnetic hysteresis for various concentrations of titanum. We present the prediction for various methods of neural calculations and the deviations from actual data results were negligible. Finally, the predictions of magnetic hysteresis are summerized for the titanume concentration between 0.0 and 1.0.<\/jats:p>","DOI":"10.3390\/sym13061079","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T21:58:32Z","timestamp":1623880712000},"page":"1079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Hysteresis Loop of Barium Hexaferrite Nanoparticles Based on Neuroevolutionary Models"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1198-9312","authenticated-orcid":false,"given":"Lina","family":"Alhmoud","sequence":"first","affiliation":[{"name":"Department of Electrical Power Engineering, Faculty of Engineering Technology, Yarmouk University, Irbid 21163, Jordan"}]},{"given":"Abdul Raouf","family":"Al Dairy","sequence":"additional","affiliation":[{"name":"Department of Physics, Yarmouk University, Irbid 21163, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4261-8127","authenticated-orcid":false,"given":"Hossam","family":"Faris","sequence":"additional","affiliation":[{"name":"King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"},{"name":"School of Computing and Informatics, Al Hussein Technical University, Amman 11831, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9265-9819","authenticated-orcid":false,"given":"Ibrahim","family":"Aljarah","sequence":"additional","affiliation":[{"name":"King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1111\/j.1551-2916.1999.tb20058.x","article-title":"The past, present, and future of ferrites","volume":"82","author":"Sugimoto","year":"1999","journal-title":"J. Am. Ceram. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1063\/1.1708357","article-title":"Recent ferrite magnet developments","volume":"37","author":"Cochardt","year":"1966","journal-title":"J. Appl. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.pmatsci.2012.04.001","article-title":"Hexagonal ferrites: A review of the synthesis, properties and applications of hexaferrite ceramics","volume":"57","author":"Pullar","year":"2012","journal-title":"Prog. Mater. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Koutzarova, T., Kolev, S., Ghelev, C., Grigorov, K., and Nedkov, I. (2009). Structural and magnetic properties and preparation techniques of nanosized M-type hexaferrite powders. Advances in Nanoscale Magnetism, Springer.","DOI":"10.1007\/978-3-540-69882-1_10"},{"key":"ref_5","unstructured":"Awadallah, A.M., and Sami, M. (2012). Effects of Preparation Conditions and Metal ion Substitutions for Barium and Iron on the Properties of M-Type Barium Hexaferrites. [Ph.D. Thesis, The University of Jordan]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1016\/j.cap.2009.03.002","article-title":"Synthesis, characterization, and magnetic properties of nanocrystalline BaFe12O19 Ferrite","volume":"9","author":"Li","year":"2009","journal-title":"Curr. Appl. Phys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.jpcs.2017.07.014","article-title":"Effect of gallium doping on electromagnetic properties of barium hexaferrite","volume":"111","author":"Trukhanov","year":"2017","journal-title":"J. Phys. Chem. Solid."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9010","DOI":"10.1039\/C7DT01708A","article-title":"Investigation into the structural features and microwave absorption of doped barium hexaferrites","volume":"46","author":"Trukhanov","year":"2017","journal-title":"Dalton Trans."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21295","DOI":"10.1016\/j.ceramint.2018.08.180","article-title":"Preparation and investigation of structure, magnetic and dielectric properties of (BaFe11.9Al0.1O19)1-x-(BaTiO3)x bicomponent ceramics","volume":"44","author":"Trukhanov","year":"2018","journal-title":"Ceram. Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.jmmm.2018.02.078","article-title":"Magnetic and dipole moments in indium doped barium hexaferrites","volume":"457","author":"Trukhanov","year":"2018","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.jallcom.2016.07.309","article-title":"Structure and magnetic properties of BaFe11.9In0.1O19 hexaferrite in a wide temperature range","volume":"689","author":"Trukhanov","year":"2016","journal-title":"J. Alloys Compd."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.jmmm.2018.05.036","article-title":"Features of crystal structure and dual ferroic properties of BaFe12-xMexO19 (Me = In3+ and Ga3+; x = 0.1\u20131.2)","volume":"464","author":"Turchenko","year":"2018","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1109\/20.996221","article-title":"A new neural-network-based scalar hysteresis model","volume":"38","author":"Kuczmann","year":"2002","journal-title":"IEEE Trans. Magn."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/20.822542","article-title":"Neural networks for the prediction of magnetic transformer core characteristics","volume":"36","author":"Nussbaum","year":"2000","journal-title":"IEEE Trans. Magn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1109\/20.123878","article-title":"A state space approach and formulation for the solution of nonlinear 3-D transient eddy current problems","volume":"28","author":"Mohammed","year":"1992","journal-title":"IEEE Trans. Magn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/0304-8853(96)00122-9","article-title":"A neural network for the prediction of performance parameters of transformer cores","volume":"160","author":"Nussbaum","year":"1996","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_17","unstructured":"Xu, Q.F., and Refsum, A. (1993, January 7\u201310). Analysis of some numerical models of hysteresis loops. Proceedings of the 1993 2nd International Conference on Advances in Power System Control, Operation and Management, APSCOM-93, Hong Kong, China."},{"key":"ref_18","first-page":"369","article-title":"INTERNATI9NAL SEMINAR\u2019DAY OF\u2019DIFFRACTION\u201994 SAINT PETERSBURG, MAY 30-JUNE 3, 1994","volume":"11","author":"Eval","year":"1994","journal-title":"Nondestr. Test. Eval."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/S0921-4526(97)00316-5","article-title":"The use of neural networks in magnetic hysteresis identification","volume":"233","author":"Saliah","year":"1997","journal-title":"Phys. B Condens. Matter"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.jmmm.2006.01.137","article-title":"Prediction of hysteresis loop in magnetic cores using neural network and genetic algorithm","volume":"305","author":"Kucuk","year":"2006","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1108\/03321640210423289","article-title":"Neural network model of magnetic hysteresis","volume":"21","author":"Kuczmann","year":"2002","journal-title":"COMPEL Int. J. Comput. Math. Electr. Electron. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Akbarzadeh, V., Davoudpour, M., and Sadeghian, A. (2008, January 15\u201318). Neural network modeling of magnetic hysteresis. Proceedings of the 2008 IEEE International Conference on Emerging Technologies and Factory Automation, Hamburg, Germany.","DOI":"10.1109\/ETFA.2008.4638563"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dairy, A.R.A., Al-Hmoud, L.A., and Khatatbeh, H.A. (2019). Magnetic and structural properties of barium hexaferrite nanoparticles doped with titanium. Symmetry, 11.","DOI":"10.3390\/sym11060732"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Faris, H., Aljarah, I., and Alqatawna, J.F. (2015, January 3\u20135). Optimizing feedforward neural networks using krill herd algorithm for e-mail spam detection. Proceedings of the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Amman, Jordan.","DOI":"10.1109\/AEECT.2015.7360576"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1650033","DOI":"10.1142\/S0218213016500330","article-title":"Optimizing the learning process of feedforward neural networks using lightning search algorithm","volume":"25","author":"Faris","year":"2016","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1007\/s13042-018-00913-2","article-title":"Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme","volume":"10","author":"Faris","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1007\/s10586-019-02913-5","article-title":"Evolving neural networks using bird swarm algorithm for data classification and regression applications","volume":"22","author":"Aljarah","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_28","unstructured":"Al-Badarneh, I., Habib, M., Aljarah, I., and Faris, H. (2020). Neuro-evolutionary models for imbalanced classification problems. J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_29","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"117","DOI":"10.14257\/ijgdc.2013.6.6.10","article-title":"A review of convergence analysis of particle swarm optimization","volume":"6","author":"Dong","year":"2013","journal-title":"Int. J. Grid Distrib. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1166\/jbic.2012.1002","article-title":"A brief historical review of particle swarm optimization (PSO)","volume":"1","author":"Esperanza","year":"2012","journal-title":"J. Bioinform. Intell. Control"},{"key":"ref_32","first-page":"180","article-title":"Analysis of particle swarm optimization algorithm","volume":"3","author":"Qinghai","year":"2010","journal-title":"Comput. Inf. Sci."},{"key":"ref_33","first-page":"451","article-title":"Genetic algorithm: Review and application","volume":"2","author":"Kumar","year":"2010","journal-title":"Int. J. Inf. Technol. Knowl. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/2.294849","article-title":"Genetic algorithms: A survey","volume":"27","author":"Srinivas","year":"1994","journal-title":"Computer"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Faris, H., Aljarah, I., Mirjalili, S., Castillo, P.A., and Guerv\u00f3s, J.J.M. (2016, January 9\u201311). EvoloPy: An Open-source Nature-inspired Optimization Framework in Python. Proceedings of the IJCCI (ECTA), Porto, Portugal.","DOI":"10.5220\/0006048201710177"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/6\/1079\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:17:10Z","timestamp":1760163430000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/6\/1079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,16]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["sym13061079"],"URL":"https:\/\/doi.org\/10.3390\/sym13061079","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2021,6,16]]}}}