{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:29:50Z","timestamp":1758266990112,"version":"3.37.3"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"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":["Cluster Comput"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10586-022-03708-x","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T19:02:39Z","timestamp":1661540559000},"page":"1821-1843","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Boosting the training of neural networks through hybrid metaheuristics"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1980-1791","authenticated-orcid":false,"given":"Mohammed Azmi","family":"Al-Betar","sequence":"first","affiliation":[]},{"given":"Mohammed A.","family":"Awadallah","sequence":"additional","affiliation":[]},{"given":"Iyad Abu","family":"Doush","sequence":"additional","affiliation":[]},{"given":"Osama Ahmad","family":"Alomari","sequence":"additional","affiliation":[]},{"given":"Ammar Kamal","family":"Abasi","sequence":"additional","affiliation":[]},{"given":"Sharif Naser","family":"Makhadmeh","sequence":"additional","affiliation":[]},{"given":"Zaid Abdi Alkareem","family":"Alyasseri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"3708_CR1","volume-title":"Fundamentals of Artificial Neural Networks","author":"MH Hassoun","year":"1995","unstructured":"Hassoun, M.H., et al.: Fundamentals of Artificial Neural Networks. MIT press, Cambridge (1995)"},{"issue":"1","key":"3708_CR2","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/72.554195","volume":"8","author":"S Lawrence","year":"1997","unstructured":"Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98\u2013113 (1997)","journal-title":"IEEE Tran. Neural Netw."},{"issue":"4","key":"3708_CR3","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/45.329294","volume":"13","author":"G Bebis","year":"1994","unstructured":"Bebis, G., Georgiopoulos, M.: Feed-forward neural networks. IEEE Potentials 13(4), 27\u201331 (1994)","journal-title":"IEEE Potentials"},{"issue":"04","key":"3708_CR4","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1142\/S0129065709002002","volume":"19","author":"S Ghosh-Dastidar","year":"2009","unstructured":"Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. Int. J. Neural Syst. 19(04), 295\u2013308 (2009)","journal-title":"Int. J. Neural Syst."},{"key":"3708_CR5","first-page":"64","volume":"5","author":"LR Medsker","year":"2001","unstructured":"Medsker, L.R., Jain, L.C.: Recurrent neural networks. Des. Appl. 5, 64 (2001)","journal-title":"Des. Appl."},{"key":"3708_CR6","unstructured":"Orr, M.J.L. et\u00a0al.: Introduction to radial basis function networks (1996)"},{"issue":"7","key":"3708_CR7","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Yu Yong","year":"2019","unstructured":"Yong, Y., Si, X., Changhua, H., Zhang, J.: A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput. 31(7), 1235\u20131270 (2019)","journal-title":"Neural computation"},{"key":"3708_CR8","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press, Cambridge (2016)"},{"issue":"11","key":"3708_CR9","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/S0167-8655(02)00081-8","volume":"23","author":"A Verikas","year":"2002","unstructured":"Verikas, A., Bacauskiene, M.: Feature selection with neural networks. Pattern Recogn. Lett. 23(11), 1323\u20131335 (2002)","journal-title":"Pattern Recogn. Lett."},{"issue":"7","key":"3708_CR10","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1177\/004051750207200706","volume":"72","author":"FH She","year":"2002","unstructured":"She, F.H., Kong, L.X., Nahavandi, S., Kouzani, A.Z.: Intelligent animal fiber classification with artificial neural networks. Textile Res. J. 72(7), 594\u2013600 (2002)","journal-title":"Textile Res. J."},{"issue":"2","key":"3708_CR11","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3390\/bioengineering5020035","volume":"5","author":"S Savalia","year":"2018","unstructured":"Savalia, S., Emamian, V.: Cardiac arrhythmia classification by multi-layer perceptron and convolution neural networks. Bioengineering 5(2), 35 (2018)","journal-title":"Bioengineering"},{"issue":"20","key":"3708_CR12","doi-asserted-by":"publisher","first-page":"10429","DOI":"10.1007\/s00500-018-3598-7","volume":"23","author":"SG Meshram","year":"2019","unstructured":"Meshram, S.G., Ghorbani, M.A., Shamshirband, S., Karimi, V., Meshram, C.: River flow prediction using hybrid Psogsa algorithm based on feed-forward neural network. Soft Comput. 23(20), 10429\u201310438 (2019)","journal-title":"Soft Comput."},{"issue":"1","key":"3708_CR13","doi-asserted-by":"publisher","first-page":"35","DOI":"10.22452\/mjcs.vol33no1.3","volume":"33","author":"IA Doush","year":"2020","unstructured":"Doush, I.A., Sawalha, A.: Automatic music composition using genetic algorithm and artificial neural networks. Malays. J. Comput. Sci. 33(1), 35\u201351 (2020)","journal-title":"Malays. J. Comput. Sci."},{"key":"3708_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103373","volume":"102","author":"S Belciug","year":"2020","unstructured":"Belciug, S.: Logistic regression paradigm for training a single-hidden layer feedforward neural network. application to gene expression datasets for cancer research. J. Biomed. Inform. 102, 103373 (2020)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"3708_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00500-016-2442-1","volume":"22","author":"I Aljarah","year":"2018","unstructured":"Aljarah, I., Faris, H., Mirjalili, S.: Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. 22(1), 1\u201315 (2018)","journal-title":"Soft Comput."},{"issue":"6","key":"3708_CR16","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1007\/s12559-018-9588-3","volume":"10","author":"WAHM Ghanem","year":"2018","unstructured":"Ghanem, W.A.H.M., Jantan, A.: A cognitively inspired hybridization of artificial bee colony and dragonfly algorithms for training multi-layer perceptrons. Cogn. Comput. 10(6), 1096\u20131134 (2018)","journal-title":"Cogn. Comput."},{"issue":"3","key":"3708_CR17","first-page":"36","volume":"2","author":"E Valian","year":"2011","unstructured":"Valian, E., Mohanna, S., Tavakoli, S.: Improved cuckoo search algorithm for feedforward neural network training. Int. J. Artif. Intell. Appl. 2(3), 36\u201343 (2011)","journal-title":"Int. J. Artif. Intell. Appl."},{"issue":"22","key":"3708_CR18","first-page":"11125","volume":"218","author":"S Mirjalil","year":"2012","unstructured":"Mirjalil, S., Hashim, S.Z.M., Sardroudi, H.M.: Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput. 218(22), 11125\u201311137 (2012)","journal-title":"Appl. Math. Comput."},{"issue":"10","key":"3708_CR19","doi-asserted-by":"publisher","first-page":"2901","DOI":"10.1007\/s13042-018-00913-2","volume":"10","author":"H Faris","year":"2019","unstructured":"Faris, H., Mirjalili, S., Aljarah, I.: Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. Int. J. Mach. Learn. Cybern. 10(10), 2901\u20132920 (2019)","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"1","key":"3708_CR20","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/s10489-014-0645-7","volume":"43","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili, S.: How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl. Intell. 43(1), 150\u2013161 (2015)","journal-title":"Appl. Intell."},{"key":"3708_CR21","doi-asserted-by":"crossref","unstructured":"Jalali, S.M.J., Ahmadian, S., Kebria, P.M., Khosravi, A., Lim, C.P., Nahavandi, S.: Evolving artificial neural networks using butterfly optimization algorithm for data classification. In: International Conference on Neural Information Processing, pp. 596\u2013607. Springer (2019)","DOI":"10.1007\/978-3-030-36708-4_49"},{"key":"3708_CR22","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, S., Li, J., Li, Y.: A hybrid of artificial fish swarm algorithm and particle swarm optimization for feedforward neural network training. In: International Conference on Intelligent Systems and Knowledge Engineering 2007. Atlantis Press (2007)","DOI":"10.2991\/iske.2007.174"},{"key":"3708_CR23","doi-asserted-by":"crossref","unstructured":"Bairathi, D., Gopalani, D.: Salp swarm algorithm (ssa) for training feed-forward neural networks. In: Soft computing for problem solving, pp. 521\u2013534. Springer (2019)","DOI":"10.1007\/978-981-13-1592-3_41"},{"key":"3708_CR24","doi-asserted-by":"crossref","unstructured":"Yin, Y., Tu, Q., Chen, X.: Enhanced salp swarm algorithm based on random walk and its application to training feedforward neural networks. Soft Comput. 24, 14791 (2020)","DOI":"10.1007\/s00500-020-04832-9"},{"key":"3708_CR25","doi-asserted-by":"crossref","unstructured":"Alboaneen D.A., Tianfield H., Zhang, Y.: Glowworm swarm optimisation for training multi-layer perceptrons. In: Proceedings of the Fourth IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 131\u2013138 (2017)","DOI":"10.1145\/3148055.3148075"},{"key":"3708_CR26","unstructured":"Montana, D.J., Davis, L.: Training feedforward neural networks using genetic algorithms. In: IJCAI, vol. 89, pp. 762\u2013767 (1989)"},{"key":"3708_CR27","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1007\/s00366-019-00882-2","volume":"37","author":"H Moayedi","year":"2019","unstructured":"Moayedi, H., Nguyen, H., Foong, L.K.: Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network. Eng. Comput. 37, 1265 (2019)","journal-title":"Eng. Comput."},{"issue":"17","key":"3708_CR28","doi-asserted-by":"publisher","first-page":"7941","DOI":"10.1007\/s00500-018-3424-2","volume":"23","author":"AA Heidari","year":"2019","unstructured":"Heidari, A.A., Faris, H., Aljarah, I., Mirjalili, S.: An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Comput. 23(17), 7941\u20137958 (2019)","journal-title":"Soft Comput."},{"key":"3708_CR29","doi-asserted-by":"crossref","unstructured":"Faris, H., Aljarah, I., Alqatawna, J.: Optimizing feedforward neural networks using krill herd algorithm for e-mail spam detection. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1\u20135. IEEE (2015)","DOI":"10.1109\/AEECT.2015.7360576"},{"issue":"2","key":"3708_CR30","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s10489-017-0967-3","volume":"48","author":"H Faris","year":"2018","unstructured":"Faris, H., Aljarah, I., Mirjalili, S.: Improved monarch butterfly optimization for unconstrained global search and neural network training. Appl. Intell. 48(2), 445\u2013464 (2018)","journal-title":"Appl. Intell."},{"issue":"8","key":"3708_CR31","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.1007\/s00521-015-1847-6","volume":"26","author":"SZ Mirjalili","year":"2015","unstructured":"Mirjalili, S.Z., Saremi, S., Mirjalili, S.M.: Designing evolutionary feedforward neural networks using social spider optimization algorithm. Neural Comput. Appl. 26(8), 1919\u20131928 (2015)","journal-title":"Neural Comput. Appl."},{"issue":"3","key":"3708_CR32","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s00521-007-0084-z","volume":"16","author":"K Socha","year":"2007","unstructured":"Socha, K., Blum, C.: An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput. Appl. 16(3), 235\u2013247 (2007)","journal-title":"Neural Comput. Appl."},{"key":"3708_CR33","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.ins.2014.08.050","volume":"294","author":"NS Jaddi","year":"2015","unstructured":"Jaddi, N.S., Abdullah, S., Hamdan, A.R.: Multi-population cooperative bat algorithm-based optimization of artificial neural network model. Inf. Sci. 294, 628\u2013644 (2015)","journal-title":"Inf. Sci."},{"issue":"3","key":"3708_CR34","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1111\/exsy.12146","volume":"33","author":"Y Zhang","year":"2016","unstructured":"Zhang, Y., Phillips, P., Wang, S., Ji, G., Yang, J., Jianguo, W.: Fruit classification by biogeography-based optimization and feedforward neural network. Expert Systems 33(3), 239\u2013253 (2016)","journal-title":"Expert Systems"},{"issue":"06","key":"3708_CR35","doi-asserted-by":"publisher","first-page":"1650033","DOI":"10.1142\/S0218213016500330","volume":"25","author":"H Faris","year":"2016","unstructured":"Faris, H., Aljarah, I., Al-Madi, N., Mirjalili, S.: Optimizing the learning process of feedforward neural networks using lightning search algorithm. Int. J. Artif. Intell. Tools 25(06), 1650033 (2016)","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"3708_CR36","first-page":"23","volume-title":"Nature-Inspired Optimizers","author":"AA Heidari","year":"2020","unstructured":"Heidari, A.A., Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M.: Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds.) Nature-Inspired Optimizers, pp. 23\u201346. Springer, Cham (2020)"},{"key":"3708_CR37","doi-asserted-by":"publisher","unstructured":"Wu, H., Zhou, Y., Luo, Q., Basset, M.A.: Training feedforward neural networks using symbiotic organisms search algorithm. Comput. Intell. Neurosci. https:\/\/doi.org\/10.1155\/2016\/9063065 (2016)","DOI":"10.1155\/2016\/9063065"},{"issue":"5","key":"3708_CR38","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TSMCC.2011.2174356","volume":"42","author":"MA Al-Betar","year":"2012","unstructured":"Al-Betar, M.A., Khader, A.T., Zaman, M.: University course timetabling using a hybrid harmony search metaheuristic algorithm. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(5), 664\u2013681 (2012)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"issue":"6","key":"3708_CR39","doi-asserted-by":"publisher","first-page":"4135","DOI":"10.1016\/j.asoc.2011.02.032","volume":"11","author":"C Blum","year":"2011","unstructured":"Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. 11(6), 4135\u20134151 (2011)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"3708_CR40","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/TSMCB.2005.856143","volume":"36","author":"Y-S Ong","year":"2006","unstructured":"Ong, Y.-S., Lim, M.-H., Zhu, N., Wong, K.-W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(1), 141\u2013152 (2006)","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"3708_CR41","doi-asserted-by":"publisher","first-page":"106040","DOI":"10.1016\/j.cie.2019.106040","volume":"137","author":"T Dokeroglu","year":"2019","unstructured":"Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., Cosar, A.: A survey on new generation metaheuristic algorithms. Comput. Ind. Eng. 137, 106040 (2019)","journal-title":"Comput. Ind. Eng."},{"key":"3708_CR42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-05094-1","volume-title":"Introduction to Evolutionary Computing","author":"AE Eiben","year":"2003","unstructured":"Eiben, A.E., Smith, J.E., et al.: Introduction to Evolutionary Computing, vol. 53. Springer, Berlin (2003)"},{"issue":"1","key":"3708_CR43","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1111\/itor.12001","volume":"22","author":"K S\u00f6rensen","year":"2015","unstructured":"S\u00f6rensen, K.: Metaheuristics\u2019 the metaphor exposed. Int. Trans. Opera. Res. 22(1), 3\u201318 (2015)","journal-title":"Int. Trans. Opera. Res."},{"key":"3708_CR44","volume-title":"The selfish gene","author":"R Dawkins","year":"1967","unstructured":"Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (1967)"},{"issue":"24","key":"3708_CR45","doi-asserted-by":"publisher","first-page":"13489","DOI":"10.1007\/s00500-019-03887-7","volume":"23","author":"MA Al-Betar","year":"2019","unstructured":"Al-Betar, M.A., Aljarah, I., Awadallah, M.A., Faris, H., Mirjalili, S.: Adaptive $$\\beta$$-hill climbing for optimization. Soft Comput. 23(24), 13489\u201313512 (2019)","journal-title":"Soft Comput."},{"issue":"6","key":"3708_CR46","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.1109\/TNNLS.2016.2542866","volume":"28","author":"K Sun","year":"2016","unstructured":"Sun, K., Huang, S.-H., Shan-Hill-Wong, D., Jang, S.-S.: Design and application of a variable selection method for multilayer perceptron neural network with lasso. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1386\u20131396 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"3708_CR47","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s00521-016-2328-2","volume":"28","author":"MA Al-Betar","year":"2017","unstructured":"Al-Betar, M.A.: $$\\beta$$-hill climbing: an exploratory local search. Neural Comput. Appl. 28(1), 153\u2013168 (2017)","journal-title":"Neural Comput. Appl."},{"key":"3708_CR48","doi-asserted-by":"publisher","first-page":"7637","DOI":"10.1007\/s12652-020-02484-z","volume":"12","author":"MA Al-Betar","year":"2020","unstructured":"Al-Betar, M.A., Hammouri, A.I., Awadallah, M.A., Doush, I.A.: Binary $$\\beta$$-hill climbing optimizer with s-shape transfer function for feature selection. J. Ambient Intell. Humaniz. Comput. 12, 7637 (2020)","journal-title":"J. Ambient Intell. Humaniz .Comput."},{"key":"3708_CR49","doi-asserted-by":"publisher","first-page":"6467","DOI":"10.1007\/s00521-020-05409-1","volume":"33","author":"S Ahmed","year":"2020","unstructured":"Ahmed, S., Ghosh, K.K., Garcia-Hernandez, L., Abraham, A., Sarkar, R.: Improved coral reefs optimization with adaptive $$\\beta$$-hill climbing for feature selection. Neural Comput. Appl. 33, 6467 (2020)","journal-title":"Neural Comput. Appl."},{"key":"3708_CR50","doi-asserted-by":"publisher","first-page":"3405","DOI":"10.1007\/s12652-019-01543-4","volume":"11","author":"M Alweshah","year":"2019","unstructured":"Alweshah, M., Al-Daradkeh, A., Al-Betar, M.A., Almomani, A., Oqeili, S.: $$\\beta$$-hill climbing algorithm with probabilistic neural network for classification problems. J. Ambient Intell. Humaniz. Comput. 11, 3405 (2019)","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"issue":"12","key":"3708_CR51","doi-asserted-by":"publisher","first-page":"7439","DOI":"10.1007\/s13369-018-3098-1","volume":"43","author":"MA Al-Betar","year":"2018","unstructured":"Al-Betar, M.A., Awadallah, M.A., Doush, I.A., Alsukhni, E., ALkhraisat, H.: A non-convex economic dispatch problem with valve loading effect using a new modified $$\\beta$$-hill climbing local search algorithm. Arabian J. Sci. Eng. 43(12), 7439\u20137456 (2018)","journal-title":"Arabian J. Sci. Eng."},{"key":"3708_CR52","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s12652-020-02047-2","volume":"12","author":"MA Al-Betar","year":"2021","unstructured":"Al-Betar, M.A.: A $$\\beta$$-hill climbing optimizer for examination timetabling problem. J. Ambient Intell. Humaniz. Comput. 12, 653\u2013666 (2021)","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"issue":"4","key":"3708_CR53","first-page":"559","volume":"28","author":"E Alsukni","year":"2019","unstructured":"Alsukni, E., Arabeyyat, O.S., Awadallah, M.A., Alsamarraie, L., Abu-Doush, I., Al-Betar, M.A.: Multiple-reservoir scheduling using $$\\beta$$-hill climbing algorithm. J. Intell. Syst. 28(4), 559\u2013570 (2019)","journal-title":"J. Intell. Syst."},{"key":"3708_CR54","doi-asserted-by":"publisher","first-page":"55405","DOI":"10.1109\/ACCESS.2018.2871557","volume":"6","author":"AA Alzaidi","year":"2018","unstructured":"Alzaidi, A.A., Ahmad, M., Doja, M.N., Al Solami, E., Beg, M.M.S.: A new 1d chaotic map and $$\\beta$$-hill climbing for generating substitution-boxes. IEEE Access 6, 55405\u201355418 (2018)","journal-title":"IEEE Access"},{"key":"3708_CR55","doi-asserted-by":"crossref","unstructured":"Al-Betar, M.A., Awadallah, M.A., Bolaji, A.L., Alijla, B.O.: $$\\beta$$-hill climbing algorithm for sudoku game. In: 2017 Palestinian International Conference on Information and Communication Technology (PICICT), pp. 84\u201388. IEEE (2017)","DOI":"10.1109\/PICICT.2017.11"},{"issue":"11","key":"3708_CR56","doi-asserted-by":"publisher","first-page":"4429","DOI":"10.1007\/s10489-018-1207-1","volume":"48","author":"OA Alomari","year":"2018","unstructured":"Alomari, O.A., Khader, A.T., Al-Betar, M.A., Awadallah, M.A.: A novel gene selection method using modified mrmr and hybrid bat-inspired algorithm with $$\\beta$$-hill climbing. Appl. Intell. 48(11), 4429\u20134447 (2018)","journal-title":"Appl. Intell."},{"issue":"2","key":"3708_CR57","first-page":"159","volume":"32","author":"BH Abed-alguni","year":"2020","unstructured":"Abed-alguni, B.H., Alkhateeb, F.: Intelligent hybrid cuckoo search and $$\\beta$$-hill climbing algorithm. J. King Saud Univ. Comput. Inf. Sci. 32(2), 159\u2013173 (2020)","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"1","key":"3708_CR58","first-page":"1","volume":"30","author":"IA Doush","year":"2020","unstructured":"Doush, I.A., Santos, E.: Best polynomial harmony search with best $$\\beta$$-hill climbing algorithm. J. Intell. Syst. 30(1), 1\u201317 (2020)","journal-title":"J. Intell. Syst."},{"key":"3708_CR59","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-981-33-4191-3_6","volume-title":"Evolutionary Data Clustering: Algorithms and Applications","author":"AK Abasi","year":"2021","unstructured":"Abasi, A.K., Khader, A.T., Al-Betar, M.A., Alyasseri, Z.A.A., Makhadmeh, S.N., Al-laham, M., Naim, S.: A hybrid salp swarm algorithm with $$\\beta$$-hill climbing algorithm for text documents clustering. In: Aljarah, I., Faris, H., Mirjalili, S. (eds.) Evolutionary Data Clustering: Algorithms and Applications, p. 129. Springer, Singapore (2021)"},{"key":"3708_CR60","doi-asserted-by":"publisher","first-page":"107393","DOI":"10.1016\/j.patcog.2020.107393","volume":"105","author":"ZAA Alyasseri","year":"2020","unstructured":"Alyasseri, Z.A.A., Khader, A.T., Al-Betar, M.A., Alomari, O.A.: Person identification using EEG channel selection with hybrid flower pollination algorithm. Pattern Recogn. 105, 107393 (2020)","journal-title":"Pattern Recogn."},{"key":"3708_CR61","doi-asserted-by":"publisher","first-page":"9330","DOI":"10.1007\/s11227-019-03083-2","volume":"76","author":"MI Jarrah","year":"2020","unstructured":"Jarrah, M.I., Jaya, A.S.M., Alqattan, Z.N., Azam, M.A., Abdullah, R., Jarrah, H., Abu-Khadrah, A.I.: A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization. J. Supercomput. 76, 9330 (2020)","journal-title":"J. Supercomput."},{"key":"3708_CR62","doi-asserted-by":"publisher","first-page":"12127","DOI":"10.1007\/s00521-019-04284-9","volume":"32","author":"MA Al-Betar","year":"2020","unstructured":"Al-Betar, M.A., Awadallah, M.A., Krishan, M.M.: A non-convex economic load dispatch problem with valve loading effect using a hybrid grey wolf optimizer. Neural Comput. Appl. 32, 12127\u201312154 (2020)","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"3708_CR63","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.3233\/JIFS-201755","volume":"40","author":"K Sun","year":"2021","unstructured":"Sun, K., Jia, H., Li, Y., Jiang, Z.: Hybrid improved slime mould algorithm with adaptive $$\\beta$$ hill climbing for numerical optimization. J. Intell. Fuzzy Syst. 40(1), 1667\u20131679 (2021)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"3708_CR64","doi-asserted-by":"crossref","unstructured":"Sarkar, R.: An improved salp swarm algorithm based on adaptive $$\\beta$$-hill climbing for stock market prediction. In: Machine Learning and Metaheuristics Algorithms, and Applications: Second Symposium, SoMMA 2020, Chennai, India, October 14\u201317, 2020, Revised Selected Papers, vol. 1366, p. 107. Springer (2021)","DOI":"10.1007\/978-981-16-0419-5_9"},{"issue":"8","key":"3708_CR65","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.1080\/01431160802549278","volume":"30","author":"D Stathakis","year":"2009","unstructured":"Stathakis, D.: How many hidden layers and nodes? Int. J. Remote Sens. 30(8), 2133\u20132147 (2009)","journal-title":"Int. J. Remote Sens."},{"issue":"4","key":"3708_CR66","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1007\/s10586-019-02913-5","volume":"22","author":"I Aljarah","year":"2019","unstructured":"Aljarah, I., Faris, H., Mirjalili, S., Al-Madi, N., Sheta, A., Mafarja, M.: Evolving neural networks using bird swarm algorithm for data classification and regression applications. Cluster Comput. 22(4), 1317\u20131345 (2019)","journal-title":"Cluster Comput."},{"key":"3708_CR67","doi-asserted-by":"crossref","unstructured":"Yang, X.-S.: Flower pollination algorithm for global optimization. In: International Conference on Unconventional Computing and Natural Computation, pp. 240\u2013249. Springer (2012)","DOI":"10.1007\/978-3-642-32894-7_27"},{"key":"3708_CR68","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-319-67669-2_5","volume-title":"Nature-Inspired Algorithms and Applied Optimization","author":"ZAA Alyasseri","year":"2018","unstructured":"Alyasseri, Z.A.A., Khader, A.T., Al-Betar, M.A., Awadallah, M.A., Yang, X.S.: Variants of the flower pollination algorithm: a review. In: Yang, X.S. (ed.) Nature-Inspired Algorithms and Applied Optimization, pp. 91\u2013118. Springer, Cham (2018)"},{"key":"3708_CR69","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163\u2013191 (2017)","journal-title":"Adv. Eng. Softw."},{"key":"3708_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","volume":"169","author":"A Askarzadeh","year":"2016","unstructured":"Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1\u201312 (2016)","journal-title":"Comput. Struct."},{"key":"3708_CR71","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":"3708_CR72","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN\u201995-International Conference on Neural Networks, vol. 4, pp. 1942\u20131948. IEEE (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"1","key":"3708_CR73","first-page":"19","volume":"7","author":"R Rao","year":"2016","unstructured":"Rao, R.: 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":"3708_CR74","unstructured":"UCI Machine Learning Repository. https:\/\/archive.ics.uci.edu\/ml\/index.php (2021). Accessed 6 June 2021"},{"key":"3708_CR75","unstructured":"Wdaa, A.S.I., Sttar, A.: Differential evolution for neural networks learning enhancement. PhD thesis, Universiti Teknologi Malaysia Johor Bahru (2008)"},{"key":"3708_CR76","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ins.2014.01.038","volume":"269","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Let a biogeography-based optimizer train your multi-layer perceptron. Inf. Sci. 269, 188\u2013209 (2014)","journal-title":"Inf. Sci."},{"issue":"16","key":"3708_CR77","doi-asserted-by":"publisher","first-page":"2156","DOI":"10.1016\/j.patrec.2008.08.001","volume":"29","author":"J-R Cano","year":"2008","unstructured":"Cano, J.-R., Garcia, S., Herrera, F.: Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes. Pattern Recogn. Lett. 29(16), 2156\u20132164 (2008)","journal-title":"Pattern Recogn. Lett."},{"issue":"5","key":"3708_CR78","doi-asserted-by":"publisher","first-page":"054603","DOI":"10.1103\/PhysRevFluids.4.054603","volume":"4","author":"PA Srinivasan","year":"2019","unstructured":"Srinivasan, P.A., Guastoni, L., Azizpour, H.: PHILIPP Schlatter, and Ricardo Vinuesa Predictions of turbulent shear flows using deep neural networks. Phys. Rev. Fluids. 4(5), 054603 (2019)","journal-title":"Phys. Rev. Fluids."},{"key":"3708_CR79","doi-asserted-by":"crossref","unstructured":"Zeng, X., Yeung, D.S.: Sensitivity analysis of multilayer perceptron to input and weight perturbations. IEEE Trans. Neural Netw. 12(6), 1358\u20131366 (2001)","DOI":"10.1109\/72.963772"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03708-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-022-03708-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03708-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T17:36:39Z","timestamp":1727890599000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-022-03708-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,26]]},"references-count":79,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["3708"],"URL":"https:\/\/doi.org\/10.1007\/s10586-022-03708-x","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2022,8,26]]},"assertion":[{"value":"19 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2022","order":4,"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"}}]}}