{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:59:04Z","timestamp":1783101544013,"version":"3.54.6"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s00521-021-06224-y","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T17:02:40Z","timestamp":1626800560000},"page":"16229-16250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6908-1038","authenticated-orcid":false,"given":"Jingwei","family":"Too","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majdi","family":"Mafarja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1443-9458","authenticated-orcid":false,"given":"Seyedali","family":"Mirjalili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"6224_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100663","volume":"54","author":"BH Nguyen","year":"2020","unstructured":"Nguyen BH, Xue B, Zhang M (2020) A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol Comput 54:100663. https:\/\/doi.org\/10.1016\/j.swevo.2020.100663","journal-title":"Swarm Evol Comput"},{"key":"6224_CR2","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.1007\/s00521-016-2204-0","volume":"28","author":"L Zhang","year":"2017","unstructured":"Zhang L, Shan L, Wang J (2017) Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion. Neural Comput Appl 28:2795\u20132808. https:\/\/doi.org\/10.1007\/s00521-016-2204-0","journal-title":"Neural Comput Appl"},{"key":"6224_CR3","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.asoc.2017.04.042","volume":"58","author":"B Ma","year":"2017","unstructured":"Ma B, Xia Y (2017) A tribe competition-based genetic algorithm for feature selection in pattern classification. Appl Soft Comput 58:328\u2013338. https:\/\/doi.org\/10.1016\/j.asoc.2017.04.042","journal-title":"Appl Soft Comput"},{"key":"6224_CR4","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.eswa.2017.04.017","volume":"82","author":"S Jiang","year":"2017","unstructured":"Jiang S, Chin K-S, Wang L et al (2017) Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert Syst Appl 82:216\u2013230. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.017","journal-title":"Expert Syst Appl"},{"key":"6224_CR5","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.engappai.2017.12.014","volume":"70","author":"M Labani","year":"2018","unstructured":"Labani M, Moradi P, Ahmadizar F, Jalili M (2018) A novel multivariate filter method for feature selection in text classification problems. Eng Appl Artif Intell 70:25\u201337. https:\/\/doi.org\/10.1016\/j.engappai.2017.12.014","journal-title":"Eng Appl Artif Intell"},{"key":"6224_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2014.02.013","volume":"45","author":"D Wang","year":"2014","unstructured":"Wang D, Zhang H, Liu R et al (2014) t-Test feature selection approach based on term frequency for text categorization. Pattern Recognit Lett 45:1\u201310. https:\/\/doi.org\/10.1016\/j.patrec.2014.02.013","journal-title":"Pattern Recognit Lett"},{"key":"6224_CR7","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.patrec.2018.06.005","volume":"112","author":"W Gao","year":"2018","unstructured":"Gao W, Hu L, Zhang P, He J (2018) Feature selection considering the composition of feature relevancy. Pattern Recognit Lett 112:70\u201374. https:\/\/doi.org\/10.1016\/j.patrec.2018.06.005","journal-title":"Pattern Recognit Lett"},{"key":"6224_CR8","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226\u20131238. https:\/\/doi.org\/10.1109\/TPAMI.2005.159","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6224_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106131","volume":"203","author":"AI Hammouri","year":"2020","unstructured":"Hammouri AI, Mafarja M, Al-Betar MA et al (2020) An improved dragonfly algorithm for feature selection. Knowl-Based Syst 203:106131. https:\/\/doi.org\/10.1016\/j.knosys.2020.106131","journal-title":"Knowl-Based Syst"},{"key":"6224_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2818-2","author":"H Faris","year":"2017","unstructured":"Faris H, Hassonah MA, Al-Zoubi AM et al (2017) A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-016-2818-2","journal-title":"Neural Comput Appl"},{"key":"6224_CR11","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1007\/978-0-387-30164-8_630","volume-title":"Encyclopedia of machine learning","author":"J Kennedy","year":"2011","unstructured":"Kennedy J (2011) Particle swarm optimization. Encyclopedia of machine learning. Springer, Boston, MA, pp 760\u2013766"},{"key":"6224_CR12","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 \u2013 a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341\u2013359. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J Glob Optim"},{"key":"6224_CR13","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Genetic algorithms. Sci Am 267:66\u201373","journal-title":"Sci Am"},{"key":"6224_CR14","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-642-12538-6_6","volume-title":"Nature inspired cooperative strategies for optimization (NICSO 2010)","author":"X-S Yang","year":"2010","unstructured":"Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65\u201374"},{"key":"6224_CR15","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/978-0-387-30164-8_22","volume-title":"Encyclopedia of machine learning","author":"M Dorigo","year":"2011","unstructured":"Dorigo M, Birattari M (2011) Ant colony optimization. Encyclopedia of machine learning. Springer, Boston, MA, pp 36\u201339"},{"key":"6224_CR16","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 Glob Optim 39:459\u2013471. https:\/\/doi.org\/10.1007\/s10898-007-9149-x","journal-title":"J Glob Optim"},{"key":"6224_CR17","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 179:2232\u20132248. https:\/\/doi.org\/10.1016\/j.ins.2009.03.004","journal-title":"Inf Sci"},{"key":"6224_CR18","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\u201367. https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv Eng Softw"},{"key":"6224_CR19","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","volume":"260","author":"MM Mafarja","year":"2017","unstructured":"Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302\u2013312. https:\/\/doi.org\/10.1016\/j.neucom.2017.04.053","journal-title":"Neurocomputing"},{"key":"6224_CR20","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1016\/j.enconman.2018.05.062","volume":"171","author":"M Abd Elaziz","year":"2018","unstructured":"Abd Elaziz M, Oliva D (2018) Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Convers Manag 171:1843\u20131859. https:\/\/doi.org\/10.1016\/j.enconman.2018.05.062","journal-title":"Energy Convers Manag"},{"key":"6224_CR21","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 (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22:1\u201315. https:\/\/doi.org\/10.1007\/s00500-016-2442-1","journal-title":"Soft Comput"},{"key":"6224_CR22","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1016\/j.apenergy.2018.09.118","volume":"231","author":"W Sun","year":"2018","unstructured":"Sun W, Zhang C (2018) Analysis and forecasting of the carbon price using multi\u2014resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm. Appl Energy 231:1354\u20131371. https:\/\/doi.org\/10.1016\/j.apenergy.2018.09.118","journal-title":"Appl Energy"},{"key":"6224_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-1769-z","author":"M Abdel-Basset","year":"2018","unstructured":"Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2018) An improved L\u00e9vy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust Comput. https:\/\/doi.org\/10.1007\/s10586-018-1769-z","journal-title":"Clust Comput"},{"key":"6224_CR24","doi-asserted-by":"crossref","unstructured":"Sharawi M, Zawbaa HM, Emary E, et al (2017) Feature selection approach based on whale optimization algorithm. In: 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI). pp 163\u2013168","DOI":"10.1109\/ICACI.2017.7974502"},{"key":"6224_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113377","volume":"152","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377. https:\/\/doi.org\/10.1016\/j.eswa.2020.113377","journal-title":"Expert Syst Appl"},{"key":"6224_CR26","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1007\/s10489-017-0894-3","volume":"47","author":"F Barani","year":"2017","unstructured":"Barani F, Mirhosseini M, Nezamabadi-pour H (2017) Application of binary quantum-inspired gravitational search algorithm in feature subset selection. Appl Intell 47:304\u2013318. https:\/\/doi.org\/10.1007\/s10489-017-0894-3","journal-title":"Appl Intell"},{"key":"6224_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113364","volume":"152","author":"N Neggaz","year":"2020","unstructured":"Neggaz N, Houssein EH, Hussain K (2020) An efficient henry gas solubility optimization for feature selection. Expert Syst Appl 152:113364. https:\/\/doi.org\/10.1016\/j.eswa.2020.113364","journal-title":"Expert Syst Appl"},{"key":"6224_CR28","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0167-8655(89)90037-8","volume":"10","author":"W Siedlecki","year":"1989","unstructured":"Siedlecki W, Sklansky J (1989) A note on genetic algorithms for large-scale feature selection. Pattern Recognit Lett 10:335\u2013347. https:\/\/doi.org\/10.1016\/0167-8655(89)90037-8","journal-title":"Pattern Recognit Lett"},{"key":"6224_CR29","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.eswa.2005.09.024","volume":"31","author":"C-L Huang","year":"2006","unstructured":"Huang C-L, Wang C-J (2006) A GA-based feature selection and parameters optimizationfor support vector machines. Expert Syst Appl 31:231\u2013240. https:\/\/doi.org\/10.1016\/j.eswa.2005.09.024","journal-title":"Expert Syst Appl"},{"key":"6224_CR30","doi-asserted-by":"publisher","first-page":"12086","DOI":"10.1016\/j.eswa.2009.04.023","volume":"36","author":"S Nemati","year":"2009","unstructured":"Nemati S, Basiri ME, Ghasem-Aghaee N, Aghdam MH (2009) A novel ACO\u2013GA hybrid algorithm for feature selection in protein function prediction. Expert Syst Appl 36:12086\u201312094. https:\/\/doi.org\/10.1016\/j.eswa.2009.04.023","journal-title":"Expert Syst Appl"},{"key":"6224_CR31","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.patrec.2013.01.026","volume":"35","author":"C De Stefano","year":"2014","unstructured":"De Stefano C, Fontanella F, Marrocco C, Scotto di Freca A (2014) A GA-based feature selection approach with an application to handwritten character recognition. Pattern Recognit Lett 35:130\u2013141. https:\/\/doi.org\/10.1016\/j.patrec.2013.01.026","journal-title":"Pattern Recognit Lett"},{"key":"6224_CR32","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s10044-014-0425-3","volume":"18","author":"I Rejer","year":"2015","unstructured":"Rejer I (2015) Genetic algorithm with aggressive mutation for feature selection in BCI feature space. Pattern Anal Appl 18:485\u2013492. https:\/\/doi.org\/10.1007\/s10044-014-0425-3","journal-title":"Pattern Anal Appl"},{"key":"6224_CR33","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.compbiolchem.2007.09.005","volume":"32","author":"L-Y Chuang","year":"2008","unstructured":"Chuang L-Y, Chang H-W, Tu C-J, Yang C-H (2008) Improved binary PSO for feature selection using gene expression data. Comput Biol Chem 32:29\u201338. https:\/\/doi.org\/10.1016\/j.compbiolchem.2007.09.005","journal-title":"Comput Biol Chem"},{"key":"6224_CR34","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.ejor.2010.02.032","volume":"206","author":"A Unler","year":"2010","unstructured":"Unler A, Murat A (2010) A discrete particle swarm optimization method for feature selection in binary classification problems. Eur J Oper Res 206:528\u2013539. https:\/\/doi.org\/10.1016\/j.ejor.2010.02.032","journal-title":"Eur J Oper Res"},{"key":"6224_CR35","doi-asserted-by":"publisher","first-page":"12699","DOI":"10.1016\/j.eswa.2011.04.057","volume":"38","author":"L-Y Chuang","year":"2011","unstructured":"Chuang L-Y, Tsai S-W, Yang C-H (2011) Improved binary particle swarm optimization using catfish effect for feature selection. Expert Syst Appl 38:12699\u201312707. https:\/\/doi.org\/10.1016\/j.eswa.2011.04.057","journal-title":"Expert Syst Appl"},{"key":"6224_CR36","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.knosys.2018.05.042","volume":"158","author":"TY Tan","year":"2018","unstructured":"Tan TY, Zhang L, Neoh SC, Lim CP (2018) Intelligent skin cancer detection using enhanced particle swarm optimization. Knowl-Based Syst 158:118\u2013135. https:\/\/doi.org\/10.1016\/j.knosys.2018.05.042","journal-title":"Knowl-Based Syst"},{"key":"6224_CR37","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3390\/informatics6020021","volume":"6","author":"J Too","year":"2019","unstructured":"Too J, Abdullah AR, Mohd Saad N (2019) A new co-evolution binary particle swarm optimization with multiple inertia weight strategy for feature selection. Informatics 6:21. https:\/\/doi.org\/10.3390\/informatics6020021","journal-title":"Informatics"},{"key":"6224_CR38","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.patrec.2014.10.007","volume":"52","author":"H Banka","year":"2015","unstructured":"Banka H, Dara S (2015) A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. Pattern Recognit Lett 52:94\u2013100. https:\/\/doi.org\/10.1016\/j.patrec.2014.10.007","journal-title":"Pattern Recognit Lett"},{"key":"6224_CR39","doi-asserted-by":"publisher","first-page":"85989","DOI":"10.1109\/ACCESS.2020.2992752","volume":"8","author":"B Ji","year":"2020","unstructured":"Ji B, Lu X, Sun G et al (2020) Bio-inspired feature selection: an improved binary particle swarm optimization approach. IEEE Access 8:85989\u201386002. https:\/\/doi.org\/10.1109\/ACCESS.2020.2992752","journal-title":"IEEE Access"},{"key":"6224_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.106031","volume":"88","author":"Y Xue","year":"2020","unstructured":"Xue Y, Tang T, Pang W, Liu AX (2020) Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers. Appl Soft Comput 88:106031. https:\/\/doi.org\/10.1016\/j.asoc.2019.106031","journal-title":"Appl Soft Comput"},{"key":"6224_CR41","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 AH, Mirjalili SZ et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163\u2013191. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.07.002","journal-title":"Adv Eng Softw"},{"key":"6224_CR42","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1158-6","author":"GI Sayed","year":"2018","unstructured":"Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-018-1158-6","journal-title":"Appl Intell"},{"key":"6224_CR43","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s00521-017-2869-z","volume":"29","author":"T Kaur","year":"2018","unstructured":"Kaur T, Saini BS, Gupta S (2018) A novel feature selection method for brain tumor MR image classification based on the Fisher criterion and parameter-free Bat optimization. Neural Comput Appl 29:193\u2013206. https:\/\/doi.org\/10.1007\/s00521-017-2869-z","journal-title":"Neural Comput Appl"},{"key":"6224_CR44","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.patcog.2017.08.013","volume":"73","author":"E Atashpaz-Gargari","year":"2018","unstructured":"Atashpaz-Gargari E, Reis MS, Braga-Neto UM et al (2018) A fast branch-and-bound algorithm for u-curve feature selection. Pattern Recognit 73:172\u2013188. https:\/\/doi.org\/10.1016\/j.patcog.2017.08.013","journal-title":"Pattern Recognit"},{"key":"6224_CR45","doi-asserted-by":"publisher","first-page":"2947","DOI":"10.1007\/s00521-017-2837-7","volume":"28","author":"R Sindhu","year":"2017","unstructured":"Sindhu R, Ngadiran R, Yacob YM et al (2017) Sine\u2013cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Comput Appl 28:2947\u20132958. https:\/\/doi.org\/10.1007\/s00521-017-2837-7","journal-title":"Neural Comput Appl"},{"key":"6224_CR46","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s00500-016-2385-6","volume":"22","author":"S Gu","year":"2018","unstructured":"Gu S, Cheng R, Jin Y (2018) Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput 22:811\u2013822. https:\/\/doi.org\/10.1007\/s00500-016-2385-6","journal-title":"Soft Comput"},{"key":"6224_CR47","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0150652","volume":"11","author":"HM Zawbaa","year":"2016","unstructured":"Zawbaa HM, Emary E, Grosan C (2016) feature selection via chaotic antlion optimization. PLoS ONE 11:e0150652. https:\/\/doi.org\/10.1371\/journal.pone.0150652","journal-title":"PLoS ONE"},{"key":"6224_CR48","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","volume":"83","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80\u201398. https:\/\/doi.org\/10.1016\/j.advengsoft.2015.01.010","journal-title":"Adv Eng Softw"},{"key":"6224_CR49","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.knosys.2018.08.003","volume":"161","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Aljarah I, Heidari AA et al (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl-Based Syst 161:185\u2013204. https:\/\/doi.org\/10.1016\/j.knosys.2018.08.003","journal-title":"Knowl-Based Syst"},{"key":"6224_CR50","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.asoc.2018.11.047","volume":"76","author":"Q Tu","year":"2019","unstructured":"Tu Q, Chen X, Liu X (2019) Multi-strategy ensemble grey wolf optimizer and its application to feature selection. Appl Soft Comput 76:16\u201330. https:\/\/doi.org\/10.1016\/j.asoc.2018.11.047","journal-title":"Appl Soft Comput"},{"key":"6224_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-020-00441-5","author":"J Too","year":"2020","unstructured":"Too J, Abdullah AR (2020) Opposition based competitive grey wolf optimizer for EMG feature selection. Evol Intell. https:\/\/doi.org\/10.1007\/s12065-020-00441-5","journal-title":"Evol Intell"},{"key":"6224_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106402","volume":"93","author":"J Gholami","year":"2020","unstructured":"Gholami J, Pourpanah F, Wang X (2020) Feature selection based on improved binary global harmony search for data classification. Appl Soft Comput 93:106402. https:\/\/doi.org\/10.1016\/j.asoc.2020.106402","journal-title":"Appl Soft Comput"},{"key":"6224_CR53","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\u201347. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.01.004","journal-title":"Adv Eng Softw"},{"key":"6224_CR54","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.eswa.2018.10.021","volume":"119","author":"A Zakeri","year":"2019","unstructured":"Zakeri A, Hokmabadi A (2019) Efficient feature selection method using real-valued grasshopper optimization algorithm. Expert Syst Appl 119:61\u201372. https:\/\/doi.org\/10.1016\/j.eswa.2018.10.021","journal-title":"Expert Syst Appl"},{"key":"6224_CR55","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/s10489-017-0897-0","volume":"47","author":"GI Sayed","year":"2017","unstructured":"Sayed GI, Hassanien AE (2017) Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images. Appl Intell 47:397\u2013408. https:\/\/doi.org\/10.1007\/s10489-017-0897-0","journal-title":"Appl Intell"},{"key":"6224_CR56","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.patrec.2016.03.014","volume":"77","author":"S AbdEl-Fattah Sayed","year":"2016","unstructured":"AbdEl-Fattah Sayed S, Nabil E, Badr A (2016) A binary clonal flower pollination algorithm for feature selection. Pattern Recognit Lett 77:21\u201327. https:\/\/doi.org\/10.1016\/j.patrec.2016.03.014","journal-title":"Pattern Recognit Lett"},{"key":"6224_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2017.04.019","volume":"83","author":"Y-P Chen","year":"2017","unstructured":"Chen Y-P, Li Y, Wang G et al (2017) A novel bacterial foraging optimization algorithm for feature selection. Expert Syst Appl 83:1\u201317. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.019","journal-title":"Expert Syst Appl"},{"key":"6224_CR58","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1007\/s10489-018-1334-8","volume":"49","author":"M Tubishat","year":"2019","unstructured":"Tubishat M, Abushariah MAM, Idris N, Aljarah I (2019) Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Appl Intell 49:1688\u20131707. https:\/\/doi.org\/10.1007\/s10489-018-1334-8","journal-title":"Appl Intell"},{"key":"6224_CR59","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.eswa.2018.08.027","volume":"114","author":"Y Sun","year":"2018","unstructured":"Sun Y, Wang X, Chen Y, Liu Z (2018) A modified whale optimization algorithm for large-scale global optimization problems. Expert Syst Appl 114:563\u2013577. https:\/\/doi.org\/10.1016\/j.eswa.2018.08.027","journal-title":"Expert Syst Appl"},{"key":"6224_CR60","doi-asserted-by":"publisher","first-page":"6168","DOI":"10.1109\/ACCESS.2017.2695498","volume":"5","author":"Y Ling","year":"2017","unstructured":"Ling Y, Zhou Y, Luo Q (2017) Levy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5:6168\u20136186. https:\/\/doi.org\/10.1109\/ACCESS.2017.2695498","journal-title":"IEEE Access"},{"key":"6224_CR61","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1016\/j.jestch.2018.11.013","volume":"22","author":"H K\u0131l\u0131\u00e7","year":"2019","unstructured":"K\u0131l\u0131\u00e7 H, Y\u00fczge\u00e7 U (2019) Tournament selection based antlion optimization algorithm for solving quadratic assignment problem. Eng Sci Technol Int J 22:673\u2013691. https:\/\/doi.org\/10.1016\/j.jestch.2018.11.013","journal-title":"Eng Sci Technol Int J"},{"key":"6224_CR62","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.eswa.2016.02.042","volume":"56","author":"S Gunasundari","year":"2016","unstructured":"Gunasundari S, Janakiraman S, Meenambal S (2016) Velocity bounded boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis. Expert Syst Appl 56:28\u201347. https:\/\/doi.org\/10.1016\/j.eswa.2016.02.042","journal-title":"Expert Syst Appl"},{"key":"6224_CR63","doi-asserted-by":"crossref","unstructured":"Zheng Y, Zhang B (2015) A simplified water wave optimization algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC). pp 807\u2013813","DOI":"10.1109\/CEC.2015.7256974"},{"key":"6224_CR64","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","volume":"43","author":"B Xue","year":"2013","unstructured":"Xue B, Zhang M, Browne WN (2013) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 43:1656\u20131671. https:\/\/doi.org\/10.1109\/TSMCB.2012.2227469","journal-title":"IEEE Trans Cybern"},{"key":"6224_CR65","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-018-0695-2","author":"E Emary","year":"2018","unstructured":"Emary E, Zawbaa HM (2018) Feature selection via L\u00e8vy Antlion optimization. Pattern Anal Appl. https:\/\/doi.org\/10.1007\/s10044-018-0695-2","journal-title":"Pattern Anal Appl"},{"key":"6224_CR66","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.asoc.2013.09.018","volume":"18","author":"B Xue","year":"2014","unstructured":"Xue B, Zhang M, Browne WN (2014) Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl Soft Comput 18:261\u2013276. https:\/\/doi.org\/10.1016\/j.asoc.2013.09.018","journal-title":"Appl Soft Comput"},{"key":"6224_CR67","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05210-0","author":"M Alweshah","year":"2020","unstructured":"Alweshah M, Khalaileh SA, Gupta BB et al (2020) The monarch butterfly optimization algorithm for solving feature selection problems. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05210-0","journal-title":"Neural Comput Appl"},{"key":"6224_CR68","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.jksuci.2018.06.003","volume":"32","author":"AhE Hegazy","year":"2020","unstructured":"Hegazy AhE, Makhlouf MA, El-Tawel GhS (2020) Improved salp swarm algorithm for feature selection. J King Saud Univ Comput Inf Sci 32:335\u2013344. https:\/\/doi.org\/10.1016\/j.jksuci.2018.06.003","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"6224_CR69","doi-asserted-by":"publisher","first-page":"66989","DOI":"10.1109\/ACCESS.2020.2986232","volume":"8","author":"IM El-Hasnony","year":"2020","unstructured":"El-Hasnony IM, Barakat SI, Elhoseny M, Mostafa RR (2020) Improved feature selection model for big data analytics. IEEE Access 8:66989\u201367004. https:\/\/doi.org\/10.1109\/ACCESS.2020.2986232","journal-title":"IEEE Access"},{"key":"6224_CR70","unstructured":"Datasets | Feature Selection @ ASU. http:\/\/featureselection.asu.edu\/datasets.php. Accessed from 3 Oct 2019"},{"key":"6224_CR71","first-page":"19","volume":"7","author":"R Rao","year":"2016","unstructured":"Rao R (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19\u201334","journal-title":"Int J Ind Eng Comput"},{"key":"6224_CR72","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. https:\/\/doi.org\/10.1016\/j.knosys.2015.07.006","journal-title":"Knowl-Based Syst"},{"key":"6224_CR73","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 (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1\u201312. https:\/\/doi.org\/10.1016\/j.compstruc.2016.03.001","journal-title":"Comput Struct"},{"key":"6224_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.113301","volume":"224","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Jin Z, Mirjalili S (2020) Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models. Energy Convers Manag 224:113301. https:\/\/doi.org\/10.1016\/j.enconman.2020.113301","journal-title":"Energy Convers Manag"},{"key":"6224_CR75","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1109\/TCYB.2014.2322602","volume":"45","author":"R Cheng","year":"2015","unstructured":"Cheng R, Jin Y (2015) A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern 45:191\u2013204. https:\/\/doi.org\/10.1109\/TCYB.2014.2322602","journal-title":"IEEE Trans Cybern"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06224-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06224-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06224-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T08:04:12Z","timestamp":1699257852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06224-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,20]]},"references-count":75,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["6224"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06224-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,20]]},"assertion":[{"value":"19 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare they have no conflict of interest.","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.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}