{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T11:47:51Z","timestamp":1769168871688,"version":"3.49.0"},"reference-count":137,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:00:00Z","timestamp":1665187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:00:00Z","timestamp":1665187200000},"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":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04201-z","type":"journal-article","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T06:03:50Z","timestamp":1665209030000},"page":"13224-13260","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7481-4854","authenticated-orcid":false,"given":"Bilal H.","family":"Abed-alguni","sequence":"first","affiliation":[]},{"given":"Noor Aldeen","family":"Alawad","sequence":"additional","affiliation":[]},{"given":"Mohammed Azmi","family":"Al-Betar","sequence":"additional","affiliation":[]},{"given":"David","family":"Paul","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"key":"4201_CR1","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.knosys.2015.05.014","volume":"86","author":"V Bol\u00f3n-Canedo","year":"2015","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-maro\u00f1o N, Alonso-Betanzos A (2015) Recent advances and emerging challenges of feature selection in the context of big data. Knowledge-based systems 86:33\u201345","journal-title":"Knowledge-based systems"},{"issue":"3","key":"4201_CR2","first-page":"37","volume":"17","author":"U Fayyad","year":"1996","unstructured":"Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI magazine 17(3):37","journal-title":"AI magazine"},{"issue":"Mar","key":"4201_CR3","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157\u20131182","journal-title":"J Mach Learn Res"},{"issue":"3","key":"4201_CR4","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.patcog.2008.08.001","volume":"42","author":"J Hua","year":"2009","unstructured":"Hua J, Tembe WD, Dougherty ER (2009) Performance of feature-selection methods in the classification of high-dimension data. Pattern Recognit 42(3):409\u2013424","journal-title":"Pattern Recognit"},{"issue":"16-18","key":"4201_CR5","doi-asserted-by":"publisher","first-page":"3580","DOI":"10.1016\/j.neucom.2008.12.035","volume":"72","author":"V G\u00f3mez-Verdejo","year":"2009","unstructured":"G\u00f3mez-Verdejo V, Verleysen M, Fleury J (2009) Information-theoretic feature selection for functional data classification. Neurocomputing 72(16-18):3580\u20133589","journal-title":"Neurocomputing"},{"key":"4201_CR6","doi-asserted-by":"crossref","unstructured":"Al-Abdallah RZ, Jaradat AS, Doush IA, Jaradat YA (2017) A binary classifier based on firefly algorithm. Jordanian J Comput Inf Technol (JJCIT), vol 3(3)","DOI":"10.5455\/jjcit.71-1501152301"},{"key":"4201_CR7","doi-asserted-by":"crossref","unstructured":"Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng, (4):491\u2013502","DOI":"10.1109\/TKDE.2005.66"},{"issue":"8","key":"4201_CR8","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1109\/TPAMI.2008.155","volume":"31","author":"S Boutemedjet","year":"2008","unstructured":"Boutemedjet S, Bouguila N, Ziou D (2008) A hybrid feature extraction selection approach for high-dimensional non-gaussian data clustering. IEEE Trans Pattern Anal Mach Intell 31(8):1429\u20131443","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"4201_CR9","first-page":"52","volume":"9","author":"S ElMustafa","year":"2017","unstructured":"ElMustafa S, Jaradat A, Doush IA, Mansour N (2017) Community detection using intelligent water drops optimisation algorithm. Int J Reasoning-Based Intell Syst 9(1):52\u201365","journal-title":"Int J Reasoning-Based Intell Syst"},{"issue":"9","key":"4201_CR10","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1109\/TIP.2008.2001050","volume":"17","author":"H Ke","year":"2008","unstructured":"Ke H, Aviyente S (2008) Wavelet feature selection for image classification. IEEE Trans Image Process 17(9):1709\u20131720","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"4201_CR11","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1016\/j.sigpro.2012.10.022","volume":"93","author":"Bo Chen","year":"2013","unstructured":"Chen Bo, Chen L, Chen Y (2013) Efficient ant colony optimization for image feature selection. Signal Process 93(6):1566\u20131576","journal-title":"Signal Process"},{"issue":"2","key":"4201_CR12","first-page":"1","volume":"5","author":"R Sawalha","year":"2012","unstructured":"Sawalha R, Doush IA (2012) Face recognition using harmony search-based selected features. Int J Hybrid Inf Technol 5(2):1\u201316","journal-title":"Int J Hybrid Inf Technol"},{"issue":"1","key":"4201_CR13","first-page":"99","volume":"24","author":"A AbuNaser","year":"2015","unstructured":"AbuNaser A, Doush IA, Mansour N, Alshattnawi S (2015) Underwater image enhancement using particle swarm optimization. J Intell Syst 24(1):99\u2013115","journal-title":"J Intell Syst"},{"issue":"1","key":"4201_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2006.04.001","volume":"33","author":"W Shang","year":"2007","unstructured":"Shang W, Huang H, Zhu H, Lin Y, Qu Y, Wang Z (2007) A novel feature selection algorithm for text categorization. Expert Syst Appl 33(1):1\u20135","journal-title":"Expert Syst Appl"},{"issue":"1","key":"4201_CR15","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1145\/1007730.1007741","volume":"6","author":"Z Zheng","year":"2004","unstructured":"Zheng Z, Wu X, Srihari R (2004) Feature selection for text categorization on imbalanced data. ACM Sigkdd Explorations Newsletter 6(1):80\u201389","journal-title":"ACM Sigkdd Explorations Newsletter"},{"key":"4201_CR16","unstructured":"Liu H, Setiono R (1995) Chi2: feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE international conference on tools with artificial intelligence. IEEE, pp 388\u2013391"},{"key":"4201_CR17","unstructured":"Quinlan JR (2014) C4. 5: programs for machine learning. Elsevier"},{"issue":"1","key":"4201_CR18","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Machine learning 1(1):81\u2013106","journal-title":"Machine learning"},{"issue":"1","key":"4201_CR19","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1186\/1471-2105-12-345","volume":"12","author":"KK Kandaswamy","year":"2011","unstructured":"Kandaswamy KK, Pugalenthi G, Hazrati MK, Kalies K-U, Martinetz T (2011) Blprot: prediction of bioluminescent proteins based on support vector machine and relieff feature selection. BMC soinformatics 12(1):345","journal-title":"BMC soinformatics"},{"issue":"1","key":"4201_CR20","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/A:1025667309714","volume":"53","author":"M Robnik-\u0160ikonja","year":"2003","unstructured":"Robnik-\u0160ikonja M, Kononenko I (2003) Theoretical and empirical analysis of relieff and rrelieff. Mach Learn 53(1):23\u201369","journal-title":"Mach Learn"},{"key":"4201_CR21","doi-asserted-by":"crossref","unstructured":"Le TT, Urbanowicz RJ, Moore JH, McKinney BA (2019) Statistical inference relief (stir) feature selection. Bioinformatics 35(8):1358\u20131365","DOI":"10.1093\/bioinformatics\/bty788"},{"issue":"5","key":"4201_CR22","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1109\/JBHI.2018.2872811","volume":"23","author":"Z Huang","year":"2018","unstructured":"Huang Z, Yang C, Zhou X, Huang T (2018) A hybrid feature selection method based on binary state transition algorithm and relieff. IEEE J Biomed Health Inf 23(5):1888\u20131898","journal-title":"IEEE J Biomed Health Inf"},{"issue":"4","key":"4201_CR23","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1109\/TFUZZ.2010.2047947","volume":"18","author":"Z Deng","year":"2010","unstructured":"Deng Z, Chung F-L, Wang S (2010) Robust relief-feature weighting, margin maximization, and fuzzy optimization. IEEE Trans Fuzzy Syst 18(4):726\u2013744","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1-2","key":"4201_CR24","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1-2):273\u2013324","journal-title":"Artif Intell"},{"key":"4201_CR25","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.inffus.2018.08.002","volume":"48","author":"A-Z Ala\u2019M","year":"2019","unstructured":"Hossam Faris, Ala\u2019M A-Z, Heidari AA, Aljarah I, Mafarja M, Hassonah MA, Fujita H (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Inf Fusion 48:67\u2013 83","journal-title":"Inf Fusion"},{"key":"4201_CR26","doi-asserted-by":"crossref","unstructured":"Chantar H, Mafarja M, Alsawalqah H, Heidari AA, Aljarah I, Faris H (2019) Feature selection using binary grey wolf optimizer with elite-based crossover for arabic text classification. Neural Comput Appl:1\u201320","DOI":"10.1007\/s00521-019-04368-6"},{"key":"4201_CR27","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.swevo.2015.06.002","volume":"25","author":"I Zelinka","year":"2015","unstructured":"Zelinka I (2015) A survey on evolutionary algorithms dynamics and its complexity\u2013mutual relations, past, present and future. Swarm Evolution Comput 25:2\u201314","journal-title":"Swarm Evolution Comput"},{"issue":"3","key":"4201_CR28","doi-asserted-by":"publisher","first-page":"2265","DOI":"10.1007\/s10462-019-09733-4","volume":"53","author":"FS Gharehchopogh","year":"2020","unstructured":"Gharehchopogh FS, Shayanfar H, Gholizadeh H (2020) A comprehensive survey on symbiotic organisms search algorithms. Artif Intell Rev 53(3):2265\u20132312","journal-title":"Artif Intell Rev"},{"key":"4201_CR29","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","volume":"62","author":"M Mafarja","year":"2017","unstructured":"Mafarja M, Mirjalili S (2017) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441\u2013453","journal-title":"Appl Soft Comput"},{"key":"4201_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2019.03.004","volume":"48","author":"GF Soleimanian","year":"2019","unstructured":"Soleimanian GF, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evolution Comput 48:1\u201324","journal-title":"Swarm Evolution Comput"},{"issue":"3","key":"4201_CR31","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1007\/s12065-021-00590-1","volume":"15","author":"FS Gharehchopogh","year":"2022","unstructured":"Gharehchopogh FS, Maleki I, Dizaji ZA (2022) Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evolution Intell 15(3):1777\u20131808","journal-title":"Evolution Intell"},{"issue":"6","key":"4201_CR32","first-page":"1342","volume":"23","author":"B Turkoglu","year":"2020","unstructured":"Turkoglu B, Kaya E (2020) Training multi-layer perceptron with artificial algae algorithm. Eng Sci Technol Int J 23(6):1342\u2013 1350","journal-title":"Eng Sci Technol Int J"},{"issue":"4","key":"4201_CR33","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1007\/s13042-022-01518-6","volume":"13","author":"B Turkoglu","year":"2022","unstructured":"Turkoglu B, Uymaz SA, Kaya E (2022) Clustering analysis through artificial algae algorithm. Int J Mach Learn Cybern 13(4):1179\u20131196","journal-title":"Int J Mach Learn Cybern"},{"issue":"43","key":"4201_CR34","doi-asserted-by":"publisher","first-page":"32169","DOI":"10.1007\/s11042-020-09639-2","volume":"79","author":"N Rahnema","year":"2020","unstructured":"Rahnema N, Gharehchopogh FS (2020) An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering. Multimed Tools Appl 79(43):32169\u201332194","journal-title":"Multimed Tools Appl"},{"key":"4201_CR35","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","journal-title":"Appl Soft Comput"},{"key":"4201_CR36","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","journal-title":"Adv Eng Softw"},{"key":"4201_CR37","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","volume":"260","author":"M Mafarja","year":"2017","unstructured":"Mafarja M, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302\u2013312","journal-title":"Neurocomputing"},{"key":"4201_CR38","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","volume":"62","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441\u2013453","journal-title":"Appl Soft Comput"},{"key":"4201_CR39","doi-asserted-by":"crossref","unstructured":"Mafarja M, Jaber I, Ahmed S, Thaher T (2019) Whale optimisation algorithm for high-dimensional small-instance feature selection. Int J Parallel Emergent Distributed Syst:1\u2013 17","DOI":"10.1109\/ISIICT.2018.8613293"},{"key":"4201_CR40","doi-asserted-by":"crossref","unstructured":"Hussien AG, Hassanien AE, Houssein EH, Bhattacharyya S, Amin M (2019) S-shaped binary whale optimization algorithm for feature selection. In: Bhattacharyya S, Mukherjee A, Bhaumik H, Das S, Yoshida K (eds) Recent trends in signal and image processing. Springer Singapore, pp 79\u201387, Singapore","DOI":"10.1007\/978-981-10-8863-6_9"},{"key":"4201_CR41","doi-asserted-by":"crossref","unstructured":"Zaman HRR, Gharehchopogh FS (2021) An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Eng Comput:1\u201335","DOI":"10.1007\/s00366-021-01431-6"},{"issue":"3","key":"4201_CR42","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 Optimization 39(3):459\u2013471","journal-title":"J Global Optimization"},{"issue":"1","key":"4201_CR43","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/3477.484436","volume":"26","author":"M Dorigo","year":"1996","unstructured":"Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybernetics) 26(1):29\u201341","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybernetics)"},{"key":"4201_CR44","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.eswa.2018.12.033","volume":"122","author":"H Turabieh","year":"2018","unstructured":"Turabieh H, Mafarja M, Li X (2018) Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. Expert Syst Appl 122:27\u201342","journal-title":"Expert Syst Appl"},{"key":"4201_CR45","doi-asserted-by":"crossref","unstructured":"Taradeh M, Mafarja M, Heidari AA, Faris H, Aljarah I, Mirjalili S, Fujita H (2019) An evolutionary gravitational search-based feature selection. Inf Sci","DOI":"10.1016\/j.ins.2019.05.038"},{"key":"4201_CR46","doi-asserted-by":"crossref","unstructured":"Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw","DOI":"10.1016\/j.advengsoft.2017.07.002"},{"key":"4201_CR47","doi-asserted-by":"publisher","first-page":"112898","DOI":"10.1016\/j.eswa.2019.112898","volume":"140","author":"H Faris","year":"2020","unstructured":"Faris H, Heidari AA, Ala\u2019M A-Z, Mafarja M, Aljarah I, Eshtay M, Mirjalili S (2020) Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Syst Appl 140:112898","journal-title":"Expert Syst Appl"},{"key":"4201_CR48","doi-asserted-by":"publisher","first-page":"113103","DOI":"10.1016\/j.eswa.2019.113103","volume":"145","author":"N Neggaz","year":"2020","unstructured":"Neggaz N, Ewees AA, Elaziz MA, Mafarja M (2020) Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Applications 145:113103","journal-title":"Expert Syst Applications"},{"issue":"1","key":"4201_CR49","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 Evolution Computat 1(1):67\u201382","journal-title":"IEEE Trans Evolution Computat"},{"key":"4201_CR50","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","volume":"96","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120\u2013133","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"4201_CR51","first-page":"195","volume":"9","author":"S Gholizadeh","year":"2019","unstructured":"Gholizadeh S, Sojoudizadeh R (2019) Modified sine-cosine algorithm for sizing optimization of truss structures with discrete design variables. Iran Univ Sci Technol 9(2):195\u2013212","journal-title":"Iran Univ Sci Technol"},{"issue":"4","key":"4201_CR52","doi-asserted-by":"publisher","first-page":"3669","DOI":"10.1007\/s13369-018-3617-0","volume":"44","author":"MA Tawhid","year":"2019","unstructured":"Tawhid MA, Savsani P (2019) Discrete sine-cosine algorithm (dsca) with local search for solving traveling salesman problem. Arabian J Sci Eng 44(4):3669\u20133679","journal-title":"Arabian J Sci Eng"},{"issue":"4","key":"4201_CR53","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.jksuci.2019.07.003","volume":"32","author":"M Belazzoug","year":"2019","unstructured":"Belazzoug M, Touahria M, Nouioua F, Brahimi M (2019) An improved sine cosine algorithm to select features for text categorization. J King Saud Univ-Comput Inf Sci 32(4):454\u2013464","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"19","key":"4201_CR54","doi-asserted-by":"publisher","first-page":"25761","DOI":"10.1007\/s11042-018-5815-x","volume":"77","author":"D Oliva","year":"2018","unstructured":"Oliva D, Hinojosa S, Elaziz MA, Ortega-s\u00e1nchez N (2018) Context based image segmentation using antlion optimization and sine cosine algorithm. Multimed Tools Appl 77(19):25761\u201325797","journal-title":"Multimed Tools Appl"},{"key":"4201_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2018.02.011","volume":"43","author":"H Nenavath","year":"2018","unstructured":"Nenavath H, Jatoth RK, Das S (2018) A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evolution Computat 43:1\u201330","journal-title":"Swarm Evolution Computat"},{"issue":"8","key":"4201_CR56","doi-asserted-by":"publisher","first-page":"4041","DOI":"10.1007\/s13369-017-2790-x","volume":"43","author":"KS Reddy","year":"2018","unstructured":"Reddy KS, Kumar PL, Panigrahi BK, Kumar R (2018) A new binary variant of sine\u2013cosine algorithm: development and application to solve profit-based unit commitment problem. Arabian J Sci Eng 43 (8):4041\u20134056","journal-title":"Arabian J Sci Eng"},{"issue":"5","key":"4201_CR57","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1007\/s13369-017-2458-6","volume":"42","author":"OE Turgut","year":"2017","unstructured":"Turgut OE (2017) Thermal and economical optimization of a shell and tube evaporator using hybrid backtracking search\u2014sine\u2013cosine algorithm. Arabian J Sci Eng 42(5):2105\u20132123","journal-title":"Arabian J Sci Eng"},{"issue":"1","key":"4201_CR58","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13640-020-0491-y","volume":"2020","author":"H Zhang","year":"2020","unstructured":"Zhang H, Gao Z, Zhang J, Liu J, Nie Z, Zhang J (2020) Hybridizing extended ant lion optimizer with sine cosine algorithm approach for abrupt motion tracking. EURASIP J Image Video Process 2020(1):4","journal-title":"EURASIP J Image Video Process"},{"key":"4201_CR59","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.eswa.2017.08.038","volume":"91","author":"S Li","year":"2018","unstructured":"Li S, Fang H, Liu X (2018) Parameter optimization of support vector regression based on sine cosine algorithm. Expert Syst Appl 91:63\u201377","journal-title":"Expert Syst Appl"},{"key":"4201_CR60","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.eswa.2018.10.050","volume":"119","author":"S Gupta","year":"2019","unstructured":"Gupta S, Deep K (2019) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210\u2013230","journal-title":"Expert Syst Appl"},{"issue":"10","key":"4201_CR61","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, Hanin Zahri NA, Hariharan M (2017) Sine\u2013cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Comput Appl 28 (10):2947\u20132958","journal-title":"Neural Comput Appl"},{"key":"4201_CR62","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.eswa.2018.11.032","volume":"123","author":"W Long","year":"2019","unstructured":"Long W, Wu T, Liang X, Xu S (2019) Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Expert Syst Appl 123:108\u2013126","journal-title":"Expert Syst Appl"},{"key":"4201_CR63","doi-asserted-by":"publisher","first-page":"113113","DOI":"10.1016\/j.eswa.2019.113113","volume":"144","author":"AA Heidari","year":"2020","unstructured":"Hao Chen, Heidari AA, Zhao X, Zhang L, Chen H (2020) Advanced orthogonal learning-driven multi-swarm sine cosine optimization: framework and case studies. Expert Syst Appl 144:113113","journal-title":"Expert Syst Appl"},{"key":"4201_CR64","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1016\/j.enconman.2019.05.057","volume":"195","author":"S Jiao","year":"2019","unstructured":"Huiling Chen, Jiao S, Heidari AA, Wang M, Chen X, Zhao X (2019) An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Conversion Manag 195:927\u2013942","journal-title":"Energy Conversion Manag"},{"issue":"11","key":"4201_CR65","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.3390\/en12112189","volume":"12","author":"S Liu","year":"2019","unstructured":"Liu S, Feng Z-K, Niu W-J, Zhang H-R, Song Z-G (2019) Peak operation problem solving for hydropower reservoirs by elite-guide sine cosine algorithm with gaussian local search and random mutation. Energies 12(11):2189","journal-title":"Energies"},{"key":"4201_CR66","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1016\/j.asoc.2017.09.039","volume":"62","author":"H Nenavath","year":"2018","unstructured":"Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput 62:1019\u20131043","journal-title":"Appl Soft Comput"},{"key":"4201_CR67","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.asoc.2018.09.019","volume":"73","author":"SN Chegini","year":"2018","unstructured":"Chegini SN, Bagheri A, Najafi F (2018) Psoscalf: a new hybrid pso based on sine cosine algorithm and levy flight for solving optimization problems. Appl Soft Comput 73:697\u2013726","journal-title":"Appl Soft Comput"},{"key":"4201_CR68","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.knosys.2018.12.008","volume":"165","author":"S Gupta","year":"2019","unstructured":"Gupta S, Deep K (2019) Improved sine cosine algorithm with crossover scheme for global optimization. Knowl-Based Syst 165:374\u2013406","journal-title":"Knowl-Based Syst"},{"key":"4201_CR69","doi-asserted-by":"crossref","unstructured":"Al-Betar MA, Awadallah MA, Abu R, Assaleh K (2022) Economic load dispatch using memetic sine cosine algorithm. J Ambient Intell Humanized Comput:1\u201329","DOI":"10.1007\/s12652-022-03731-1"},{"issue":"24","key":"4201_CR70","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 MA, Aljarah I, Awadallah MA, Faris H, Mirjalili S (2019) Adaptive \u03b2-hill climbing for optimization. Soft Comput 23(24):13489\u201313512","journal-title":"Soft Comput"},{"key":"4201_CR71","doi-asserted-by":"crossref","unstructured":"Hafez AI, Zawbaa HM, Emary E, Hassanien AE (2016) Sine cosine optimization algorithm for feature selection. In: 2016 International symposium on innovations in intelligent systems and applications (INISTA). IEEE, pp 1\u20135","DOI":"10.1109\/INISTA.2016.7571853"},{"key":"4201_CR72","doi-asserted-by":"crossref","unstructured":"Eid MM, El-kenawy E-SM, Ibrahim A (2021) A binary sine cosine-modified whale optimization algorithm for feature selection. In: 2021 National computing colleges conference (NCCC). IEEE, pp 1\u20136","DOI":"10.1109\/NCCC49330.2021.9428794"},{"key":"4201_CR73","doi-asserted-by":"publisher","first-page":"114778","DOI":"10.1016\/j.eswa.2021.114778","volume":"176","author":"K Hussain","year":"2021","unstructured":"Hussain K, Neggaz N, Zhu W, Houssein EH (2021) An efficient hybrid sine-cosine harris hawks optimization for low and high-dimensional feature selection. Expert Syst Appl 176:114778","journal-title":"Expert Syst Appl"},{"key":"4201_CR74","doi-asserted-by":"crossref","unstructured":"Elaziz MEA, Ewees AA, Oliva D, Duan P, Xiong S (2017) A hybrid method of sine cosine algorithm and differential evolution for feature selection. In: International conference on neural information processing. Springer, pp 145\u2013155","DOI":"10.1007\/978-3-319-70139-4_15"},{"key":"4201_CR75","doi-asserted-by":"crossref","unstructured":"Abualigah L, Dulaimi AJ (2021) A novel feature selection method for data mining tasks using hybrid sine cosine algorithm and genetic algorithm. Cluster Comput:1\u201316","DOI":"10.1007\/s10586-021-03254-y"},{"key":"4201_CR76","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.eswa.2017.07.043","volume":"90","author":"MA Elaziz","year":"2017","unstructured":"Elaziz MA, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484\u2013500","journal-title":"Expert Syst Appl"},{"key":"4201_CR77","doi-asserted-by":"crossref","unstructured":"Sindhu R, Ngadiran R, Yacob YM, Zahri NAH, Hariharan M, Polat K (2019) A hybrid sca inspired bbo for feature selection problems. Math Prob Eng:2019","DOI":"10.1155\/2019\/9517568"},{"issue":"1","key":"4201_CR78","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s11047-019-09769-z","volume":"20","author":"L Kumar","year":"2021","unstructured":"Kumar L, Bharti KK (2021) A novel hybrid bpso\u2013sca approach for feature selection. Natural Comput 20(1):39\u201361","journal-title":"Natural Comput"},{"key":"4201_CR79","doi-asserted-by":"crossref","unstructured":"El-Kenawy E-SM, Eid MM, Saber M, Ibrahim A (2020) Mbgwo-sfs: modified binary grey wolf optimizer based on stochastic fractal search for feature selection, vol 8","DOI":"10.1109\/ACCESS.2020.3001151"},{"key":"4201_CR80","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","journal-title":"Adv Eng Softw"},{"key":"4201_CR81","doi-asserted-by":"crossref","unstructured":"Elaziz MA, Nabil N, Ewees AA, Lu S (2019) Automatic data clustering based on hybrid atom search optimization and sine-cosine algorithm. In: 2019 IEEE congress on evolutionary computation (CEC). IEEE, pp 2315\u20132322","DOI":"10.1109\/CEC.2019.8790361"},{"key":"4201_CR82","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.asoc.2015.03.003","volume":"31","author":"SA Uymaz","year":"2015","unstructured":"Uymaz SA, Tezel G, Yel E (2015) Artificial algae algorithm (aaa) for nonlinear global optimization. Appl Soft Comput 31:153\u2013 171","journal-title":"Appl Soft Comput"},{"key":"4201_CR83","doi-asserted-by":"publisher","first-page":"108630","DOI":"10.1016\/j.asoc.2022.108630","volume":"120","author":"B Turkoglu","year":"2022","unstructured":"Turkoglu B, Uymaz SA, Kaya E (2022) Binary artificial algae algorithm for feature selection. Appl Soft Comput 120:108630","journal-title":"Appl Soft Comput"},{"key":"4201_CR84","doi-asserted-by":"publisher","first-page":"106711","DOI":"10.1016\/j.knosys.2020.106711","volume":"213","author":"F MiarNaeimi","year":"2021","unstructured":"MiarNaeimi F, Azizyan G, Rashki M (2021) Horse herd optimization algorithm: a nature-inspired algorithm for high-dimensional optimization problems. Knowl-Based Syst 213:106711","journal-title":"Knowl-Based Syst"},{"key":"4201_CR85","doi-asserted-by":"publisher","first-page":"105152","DOI":"10.1016\/j.compbiomed.2021.105152","volume":"141","author":"MA Awadallah","year":"2022","unstructured":"Awadallah MA, Hammouri AI, Al-Betar MA, Braik MS, Elaziz MA (2022) Binary horse herd optimization algorithm with crossover operators for feature selection. Comput Bio Med 141:105152","journal-title":"Comput Bio Med"},{"key":"4201_CR86","doi-asserted-by":"publisher","first-page":"103249","DOI":"10.1016\/j.engappai.2019.103249","volume":"87","author":"V Hayyolalam","year":"2020","unstructured":"Hayyolalam V, Kazem AAP (2020) Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 87:103249","journal-title":"Eng Appl Artif Intell"},{"key":"4201_CR87","doi-asserted-by":"publisher","first-page":"107638","DOI":"10.1016\/j.knosys.2021.107638","volume":"235","author":"G Hu","year":"2022","unstructured":"Hu G, Du B, Wang X, Wei G (2022) An enhanced black widow optimization algorithm for feature selection. Knowl-Based Syst 235:107638","journal-title":"Knowl-Based Syst"},{"key":"4201_CR88","doi-asserted-by":"publisher","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","volume":"149","author":"M Khishe","year":"2020","unstructured":"Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338","journal-title":"Expert Syst Appl"},{"issue":"8","key":"4201_CR89","doi-asserted-by":"publisher","first-page":"6427","DOI":"10.1007\/s00521-021-06775-0","volume":"34","author":"E Pashaei","year":"2022","unstructured":"Pashaei E, Pashaei E (2022) An efficient binary chimp optimization algorithm for feature selection in biomedical data classification. Neural Comput Appl 34(8):6427\u20136451","journal-title":"Neural Comput Appl"},{"key":"4201_CR90","doi-asserted-by":"publisher","first-page":"114864","DOI":"10.1016\/j.eswa.2021.114864","volume":"177","author":"Y Yang","year":"2021","unstructured":"Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864","journal-title":"Expert Syst Appl"},{"issue":"1","key":"4201_CR91","doi-asserted-by":"publisher","first-page":"557","DOI":"10.32604\/cmc.2022.019611","volume":"70","author":"RM Devi","year":"2022","unstructured":"Devi RM, Premkumar M, Jangir P, Kumar BS, Alrowaili D, Nisar KS (2022) Bhgso: binary hunger games search optimization algorithm for feature selection problem. CMC-Comput Materials Continua 70(1):557\u2013579","journal-title":"CMC-Comput Materials Continua"},{"key":"4201_CR92","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2012.09.002","volume":"9","author":"S Mirjalili","year":"2013","unstructured":"Mirjalili S, Lewis A (2013) S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evolution Computat 9:1\u201314","journal-title":"Swarm Evolution Computat"},{"issue":"4","key":"4201_CR93","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\u20131073","journal-title":"Neural Comput Appl"},{"key":"4201_CR94","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neucom.2016.03.101","volume":"213","author":"E Emary","year":"2016","unstructured":"Emary E, Zawbaa HM, Hassanien AE (2016) Binary ant lion approaches for feature selection. Neurocomputing 213:54\u201365","journal-title":"Neurocomputing"},{"key":"4201_CR95","doi-asserted-by":"crossref","unstructured":"Abed-alguni BH, Alawad NA, Barhoush M, Hammad R (2021) Exploratory cuckoo search for solving single-objective optimization problems. Soft Comput:1\u201314","DOI":"10.1007\/s00500-021-05939-3"},{"key":"4201_CR96","doi-asserted-by":"crossref","unstructured":"Alawad NA, Abed-alguni BH (2020) Discrete island-based cuckoo search with highly disruptive polynomial mutation and opposition-based learning strategy for scheduling of workflow applications in cloud environments. Arabian J Sci Eng:1\u201321","DOI":"10.1007\/s13369-020-05141-x"},{"key":"4201_CR97","doi-asserted-by":"crossref","unstructured":"Alkhateeb F, Abed-alguni BH, Al-rousan MH (2021) Discrete hybrid cuckoo search and simulated annealing algorithm for solving the job shop scheduling problem. J Supercomput:1\u201328","DOI":"10.1007\/s11227-021-04050-6"},{"key":"4201_CR98","doi-asserted-by":"crossref","unstructured":"Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC\u201906). IEEE, vol 1, pp 695\u2013701","DOI":"10.1109\/CIMCA.2005.1631345"},{"key":"4201_CR99","doi-asserted-by":"crossref","unstructured":"Shishavan ST, Gharehchopogh FS (2022) An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks. Multimed Tools Appl:1\u201327","DOI":"10.1007\/s11042-022-12409-x"},{"issue":"2","key":"4201_CR100","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1007\/s10489-019-01521-5","volume":"50","author":"J Luo","year":"2020","unstructured":"Luo J, Liu Z (2020) Novel grey wolf optimization based on modified differential evolution for numerical function optimization. Appl Intell 50(2):468\u2013486","journal-title":"Appl Intell"},{"issue":"5","key":"4201_CR101","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 MA, Idris N, Aljarah I (2019) Improved whale optimization algorithm for feature selection in arabic sentiment analysis. Appl Intell 49(5):1688\u20131707","journal-title":"Appl Intell"},{"key":"4201_CR102","doi-asserted-by":"publisher","first-page":"106131","DOI":"10.1016\/j.knosys.2020.106131","volume":"203","author":"AI Hammouri","year":"2020","unstructured":"Hammouri AI, Mafarja M, Al-Betar MA, Awadallah MA, Abu-Doush I (2020) An improved dragonfly algorithm for feature selection. Knowl-Based Syst 203:106131","journal-title":"Knowl-Based Syst"},{"key":"4201_CR103","doi-asserted-by":"crossref","unstructured":"Abed-alguni BH, Paul D, Hammad R (2022) Improved salp swarm algorithm for solving single-objective continuous optimization problem. Appl Intell:1\u201320","DOI":"10.1007\/s10489-022-03269-x"},{"issue":"4","key":"4201_CR104","first-page":"683","volume":"28","author":"F Alkhateeb","year":"2019","unstructured":"Alkhateeb F, Abed-Alguni BH (2019) A hybrid cuckoo search and simulated annealing algorithm. J Intell Syst 28(4):683\u2013698","journal-title":"J Intell Syst"},{"issue":"1","key":"4201_CR105","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/j.amc.2006.10.047","volume":"188","author":"K Deep","year":"2007","unstructured":"Deep K, Thakur M (2007) A new crossover operator for real coded genetic algorithms. Appl Math Computat 188(1):895\u2013911","journal-title":"Appl Math Computat"},{"key":"4201_CR106","doi-asserted-by":"crossref","unstructured":"Boudt K, Galanos A, Payseur S, Zivot E (2019) Multivariate garch models for large-scale applications: a survey. In: Handbook of statistics. Elsevier, vol 41, pp 193\u2013242","DOI":"10.1016\/bs.host.2019.01.001"},{"key":"4201_CR107","first-page":"1","volume":"235","author":"S Taghian","year":"2019","unstructured":"Taghian S, Nadimi-Shahraki MH (2019) Binary sine cosine algorithms for feature selection from medical data. Adv Comput Int J 235:1\u201310","journal-title":"Adv Comput Int J"},{"key":"4201_CR108","unstructured":"Lichman M et al (2013) Uci machine learning repository, 2013. http:\/\/archive.ics.uci.edu\/ml, vol 40. Accessed 14 April 2022"},{"key":"4201_CR109","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.knosys.2017.12.037","volume":"145","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Ala\u2019M A-Z, Mirjalili S (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl-Based Syst 145:25\u201345","journal-title":"Knowl-Based Syst"},{"key":"4201_CR110","doi-asserted-by":"publisher","first-page":"107629","DOI":"10.1016\/j.knosys.2021.107629","volume":"235","author":"M Alweshah","year":"2022","unstructured":"Alweshah M, Alkhalaileh S, Al-Betar MA, Bakar AA (2022) Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowl-Based Syst 235:107629","journal-title":"Knowl-Based Syst"},{"key":"4201_CR111","doi-asserted-by":"publisher","first-page":"113122","DOI":"10.1016\/j.eswa.2019.113122","volume":"145","author":"M Tubishat","year":"2020","unstructured":"Tubishat M, Idris N, Shuib L, Abushariah MA, Mirjalili S (2020) Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Syst Appl 145:113122","journal-title":"Expert Syst Appl"},{"key":"4201_CR112","doi-asserted-by":"publisher","first-page":"121127","DOI":"10.1109\/ACCESS.2020.3006473","volume":"8","author":"R Sihwail","year":"2020","unstructured":"Sihwail R, Omar K, Ariffin KAZ, Tubishat M (2020) Improved harris hawks optimization using elite opposition-based learning and novel search mechanism for feature selection. IEEE Access 8:121127\u2013121145","journal-title":"IEEE Access"},{"key":"4201_CR113","doi-asserted-by":"publisher","first-page":"105866","DOI":"10.1016\/j.asoc.2019.105866","volume":"86","author":"M Aladeemy","year":"2020","unstructured":"Aladeemy M, Adwan L, Booth A, Khasawneh MT, Poranki S (2020) New feature selection methods based on opposition-based learning and self-adaptive cohort intelligence for predicting patient no-shows. Appl Soft Comput 86:105866","journal-title":"Appl Soft Comput"},{"issue":"1","key":"4201_CR114","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s10878-021-00809-y","volume":"44","author":"X Ji","year":"2022","unstructured":"Ji X, Liao B, Yang S (2022) A variable neighborhood search algorithm for human resource selection and optimization problem in the home appliance manufacturing industry. J Combinatorial Optimization 44(1):223\u2013241","journal-title":"J Combinatorial Optimization"},{"issue":"5","key":"4201_CR115","doi-asserted-by":"publisher","first-page":"bbab097","DOI":"10.1093\/bib\/bbab097","volume":"22","author":"C Qu","year":"2021","unstructured":"Qu C, Zhang L, Li J, Deng F, Tang Y, Zeng X, Peng X (2021) Improving feature selection performance for classification of gene expression data using harris hawks optimizer with variable neighborhood learning. Brief Bioinform 22(5):bbab097","journal-title":"Brief Bioinform"},{"issue":"6","key":"4201_CR116","doi-asserted-by":"publisher","first-page":"68","DOI":"10.3390\/computation9060068","volume":"9","author":"ZM Elgamal","year":"2021","unstructured":"Elgamal ZM, Yasin NM, Sabri AQM, Sihwail R, Tubishat M, Jarrah H (2021) Improved equilibrium optimization algorithm using elite opposition-based learning and new local search strategy for feature selection in medical datasets. Computation 9(6):68","journal-title":"Computation"},{"key":"4201_CR117","doi-asserted-by":"publisher","first-page":"113873","DOI":"10.1016\/j.eswa.2020.113873","volume":"164","author":"M Tubishat","year":"2021","unstructured":"Tubishat M, Ja\u2019afar S, Alswaitti M, Mirjalili S, Idris N, Ismail MA, Omar MS (2021) Dynamic salp swarm algorithm for feature selection. Expert Syst Appl 164:113873","journal-title":"Expert Syst Appl"},{"issue":"11","key":"4201_CR118","doi-asserted-by":"publisher","first-page":"8542","DOI":"10.1007\/s10489-021-02288-4","volume":"51","author":"X Wu","year":"2021","unstructured":"Wu X, Chen H, Li T, Wan J (2021) Semi-supervised feature selection with minimal redundancy based on local adaptive. Appl Intell 51(11):8542\u20138563","journal-title":"Appl Intell"},{"key":"4201_CR119","doi-asserted-by":"publisher","first-page":"39496","DOI":"10.1109\/ACCESS.2019.2906757","volume":"7","author":"SJ Abdul Kadir","year":"2019","unstructured":"Qasem Al-Tashi, Abdul Kadir SJ, Rais HM, Mirjalili S, Alhussian H (2019) Binary optimization using hybrid grey wolf optimization for feature selection. Ieee Access 7:39496\u201339508","journal-title":"Ieee Access"},{"issue":"10","key":"4201_CR120","doi-asserted-by":"publisher","first-page":"3485","DOI":"10.3390\/en15103485","volume":"15","author":"L Sun","year":"2022","unstructured":"Sun L, Qin H, Przystupa K, Cui Y, Kochan O, Skowron M, Su J (2022) A hybrid feature selection framework using improved sine cosine algorithm with metaheuristic techniques. Energies 15 (10):3485","journal-title":"Energies"},{"issue":"4","key":"4201_CR121","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.jksuci.2019.07.003","volume":"32","author":"M Belazzoug","year":"2020","unstructured":"Belazzoug M, Touahria M, Nouioua F, Brahimi M (2020) An improved sine cosine algorithm to select features for text categorization. J King Saud Univ-Comput Inf Sci 32(4):454\u2013464","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"7","key":"4201_CR122","doi-asserted-by":"publisher","first-page":"4824","DOI":"10.1007\/s10489-020-02038-y","volume":"51","author":"A Hosseinalipour","year":"2021","unstructured":"Hosseinalipour A, Gharehchopogh FS, Masdari M, Khademi A (2021) A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology. Appl Intell 51(7):4824\u20134859","journal-title":"Appl Intell"},{"key":"4201_CR123","doi-asserted-by":"publisher","first-page":"107408","DOI":"10.1016\/j.cie.2021.107408","volume":"158","author":"B Abdollahzadeh","year":"2021","unstructured":"Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Industr Eng 158:107408","journal-title":"Comput Industr Eng"},{"issue":"10","key":"4201_CR124","doi-asserted-by":"publisher","first-page":"5887","DOI":"10.1002\/int.22535","volume":"36","author":"B Abdollahzadeh","year":"2021","unstructured":"Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36 (10):5887\u20135958","journal-title":"Int J Intell Syst"},{"issue":"8","key":"4201_CR125","doi-asserted-by":"publisher","first-page":"9117","DOI":"10.1007\/s10489-021-02845-x","volume":"52","author":"T Banerjee","year":"2022","unstructured":"Banerjee T, Sinha S, Choudhury P (2022) Long term and short term forecasting of horticultural produce based on the lstm network model. Appl Intell 52(8):9117\u20139147","journal-title":"Appl Intell"},{"key":"4201_CR126","doi-asserted-by":"publisher","first-page":"107942","DOI":"10.1016\/j.asoc.2021.107942","volume":"113","author":"J Li","year":"2021","unstructured":"Li J, Gao Y, Wang K, Sun Y (2021) A dual opposition-based learning for differential evolution with protective mechanism for engineering optimization problems. Appl Soft Comput 113:107942","journal-title":"Appl Soft Comput"},{"issue":"3","key":"4201_CR127","doi-asserted-by":"publisher","first-page":"3998","DOI":"10.1007\/s11227-021-04015-9","volume":"78","author":"MJ Goldanloo","year":"2022","unstructured":"Goldanloo MJ, Gharehchopogh FS (2022) A hybrid obl-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems. J Supercomput 78(3):3998\u20134031","journal-title":"J Supercomput"},{"key":"4201_CR128","doi-asserted-by":"publisher","first-page":"109191","DOI":"10.1016\/j.asoc.2022.109191","volume":"125","author":"G-H Wu","year":"2022","unstructured":"Wu G-H, Cheng C-Y, Pourhejazy P, Fang B-L (2022) Variable neighborhood-based cuckoo search for production routing with time window and setup times. Appl Soft Comput 125:109191","journal-title":"Appl Soft Comput"},{"issue":"2","key":"4201_CR129","first-page":"41","volume":"16","author":"BH Abed-alguni","year":"2018","unstructured":"Abed-alguni BH, Ottom MA (2018) Double delayed Q-learning. Int J Artif IntellTM 16(2):41\u201359","journal-title":"Int J Artif IntellTM"},{"issue":"1","key":"4201_CR130","first-page":"71","volume":"14","author":"BH Abed-Alguni","year":"2016","unstructured":"Abed-Alguni BH, Paul DJ, Chalup SK, Henskens FA (2016) A comparison study of cooperative Q-learning algorithms for independent learners. Int J Artif IntellTM 14(1):71\u201393","journal-title":"Int J Artif IntellTM"},{"issue":"12","key":"4201_CR131","doi-asserted-by":"publisher","first-page":"6771","DOI":"10.1007\/s13369-017-2873-8","volume":"43","author":"BH Abed-alguni","year":"2018","unstructured":"Abed-alguni BH (2018) Action-selection method for reinforcement learning based on cuckoo search algorithm. Arabian J Sci Eng 43(12):6771\u20136785","journal-title":"Arabian J Sci Eng"},{"issue":"1","key":"4201_CR132","first-page":"56","volume":"3","author":"BH Abed-Alguni","year":"2017","unstructured":"Abed-Alguni BH (2017) Bat Q-learning algorithm. Jordanian J Comput Inf Technol(JJCIT) 3 (1):56\u201377","journal-title":"Jordanian J Comput Inf Technol(JJCIT)"},{"key":"4201_CR133","doi-asserted-by":"crossref","unstructured":"Abed-alguni BH, Paul D (2022) Island-based cuckoo search with elite opposition-based learning and multiple mutation methods for solving optimization problems. Soft Comput:1\u201320","DOI":"10.21203\/rs.3.rs-773831\/v1"},{"key":"4201_CR134","doi-asserted-by":"crossref","unstructured":"Abed-alguni BH, Alawad NA (2021) Distributed grey wolf optimizer for scheduling of workflow applications in cloud environment. Appl Soft Comput J:1\u201337","DOI":"10.1016\/j.asoc.2021.107113"},{"key":"4201_CR135","unstructured":"Abed-alguni BH, Barhoush M (2018) Distributed grey wolf optimizer for numerical optimization problems. Jordanian J Comput Inf Technol (JJCIT), vol 4(03)"},{"issue":"1","key":"4201_CR136","first-page":"57","volume":"17","author":"BH Abed-Alguni","year":"2019","unstructured":"Abed-Alguni BH (2019) Island-based cuckoo search with highly disruptive polynomial mutation. Int J Artif Intell 17(1):57\u201382","journal-title":"Int J Artif Intell"},{"key":"4201_CR137","doi-asserted-by":"crossref","unstructured":"Abed-Alguni BH, Klaib AF, Nahar KM (2019) Island-based whale optimization algorithm for continuous optimization problems. Int J Reasoning-Based Intell Syst:1\u201311","DOI":"10.1504\/IJRIS.2019.103525"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04201-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04201-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04201-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:41:29Z","timestamp":1744206089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04201-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,8]]},"references-count":137,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4201"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04201-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,8]]},"assertion":[{"value":"21 September 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2022","order":2,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}