{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:11:06Z","timestamp":1757617866528,"version":"3.44.0"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s13042-025-02641-w","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T14:42:39Z","timestamp":1745851359000},"page":"6683-6715","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-strategy improvement of crayfish optimization algorithm to solve high-dimensional feature selection"],"prefix":"10.1007","volume":"16","author":[{"given":"Xiaoming","family":"Shi","sequence":"first","affiliation":[]},{"given":"Heming","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Honghua","family":"Rao","sequence":"additional","affiliation":[]},{"given":"Fangkai","family":"You","sequence":"additional","affiliation":[]},{"given":"Laith","family":"Abualigah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"2641_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.asoc.2017.01.039","volume":"55","author":"T Bartz-Beielstein","year":"2017","unstructured":"Bartz-Beielstein T, Zaefferer M (2017) Model-based methods for continuous and discrete global optimization. Appl Soft Comput 55:154\u2013167. https:\/\/doi.org\/10.1016\/j.asoc.2017.01.039","journal-title":"Appl Soft Comput"},{"key":"2641_CR2","doi-asserted-by":"publisher","unstructured":"Raghavendran PS, Ragul S, Asokan R, Loganathan AK, Muthusamy S, Mishra OP, Sundararajan SCM (2023) A new method for chest X-ray images categorization using transfer learning and CovidNet_2020 employing convolution neural network.\u00a0Soft Computing,\u00a027(19), 14241\u201314251. https:\/\/doi.org\/10.1007\/s00500-023-08874-7","DOI":"10.1007\/s00500-023-08874-7"},{"key":"2641_CR3","doi-asserted-by":"publisher","unstructured":"Subramaniam K, Palanisamy N, Sinnaswamy RA, Muthusamy S, Mishra OP, Loganathan AK, Sundararajan SC M (2023) A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques. Soft computing 27(19): 14219\u201314240. https:\/\/doi.org\/10.1007\/s00500-023-08561-7","DOI":"10.1007\/s00500-023-08561-7"},{"key":"2641_CR4","doi-asserted-by":"publisher","unstructured":"Thangavel K, Palanisamy N, Muthusamy S, Mishra OP, Sundararajan SCM, Panchal H, Ramamoorthi P (2023) A novel method for image captioning using multimodal feature fusion employing mask RNN and LSTM models. Soft Computing 27(19): 14205\u201314218. https:\/\/doi.org\/10.1007\/s00500-023-08448-7","DOI":"10.1007\/s00500-023-08448-7"},{"issue":"2","key":"2641_CR5","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s11277-023-10454-9","volume":"131","author":"KG Krishnasamy","year":"2023","unstructured":"Krishnasamy KG, Periasamy S, Periasamy K, Prasanna Moorthy V, Thangavel G, Lamba R, Muthusamy S (2023) A pair-task heuristic for scheduling tasks in heterogeneous multi-cloud environment. Wireless Pers Commun 131(2):773\u2013804. https:\/\/doi.org\/10.1007\/s11277-023-10454-9","journal-title":"Wireless Pers Commun"},{"issue":"1","key":"2641_CR6","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s11277-023-10446-9","volume":"131","author":"BBC Batcha","year":"2023","unstructured":"Batcha BBC, Singaravelu R, Ramachandran M, Muthusamy S, Panchal H, Thangaraj K, Ravindaran A (2023) A novel security algorithm RPBB31 for securing the social media analyzed data using machine learning algorithms. Wireless Pers Commun 131(1):581\u2013608. https:\/\/doi.org\/10.1007\/s11277-023-10446-9","journal-title":"Wireless Pers Commun"},{"key":"2641_CR7","doi-asserted-by":"publisher","unstructured":"Rakkiannan T, Ekambaram G, Palanisamy N, Ramasamy RR, Muthusamy S, Loganathan AK, Ravindaran A (2023) An automated network slicing at edge with software defined networking and network function virtualization: a federated learning approach. Wireless Personal Communications 131(1): 639\u2013658. https:\/\/doi.org\/10.1007\/s11277-023-10450-z","DOI":"10.1007\/s11277-023-10450-z"},{"key":"2641_CR8","doi-asserted-by":"crossref","unstructured":"Bennet MA, Mishra OP, Muthusamy S (2023) Modeling of upper limb and prediction of various yoga postures using artificial neural networks. In: 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 503\u2013508). IEEE. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10104630","DOI":"10.1109\/ICSCDS56580.2023.10104630"},{"key":"2641_CR9","doi-asserted-by":"publisher","unstructured":"Kathamuthu ND, Subramaniam S, Le QH, Muthusamy S, Panchal H, Sundararajan SCM, Zahra MMA (2023) A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. Adv Eng Softw 175: 103317. https:\/\/doi.org\/10.1016\/j.advengsoft.2022.103317","DOI":"10.1016\/j.advengsoft.2022.103317"},{"key":"2641_CR10","doi-asserted-by":"crossref","unstructured":"Subramanian M, Shanmugavadivel K, Muthusamy S, Bajaj M, Rubanenko O, Danylchenko D (2022) Development of A Surveillance System to Detect Forest Fire and Smoke Using Deep Neural Networks. In: 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek) (pp. 1\u20136). IEEE. https:\/\/ieeexplore.ieee.org\/document\/9916483","DOI":"10.1109\/KhPIWeek57572.2022.9916483"},{"key":"2641_CR11","doi-asserted-by":"publisher","unstructured":"Rajeena PPF, Orban R, Vadivel KS, Subramanian M, Muthusamy S, Elminaam DSA, Ali MA (2022) A novel method for the classification of butterfly species using pre-trained CNN models. Electronics 11(13): 2016. https:\/\/doi.org\/10.3390\/electronics11132016","DOI":"10.3390\/electronics11132016"},{"key":"2641_CR12","doi-asserted-by":"publisher","unstructured":"Ding H, Xu H, Wu Y, Li H, Gong M, Ma W, Lei Y (2024) Evolutionary multitasking with two-level knowledge transfer for multi-view point cloud registration. In: Proceedings of the genetic and evolutionary computation conference (pp. 304\u2013312). https:\/\/doi.org\/10.1145\/3638529.3654108","DOI":"10.1145\/3638529.3654108"},{"key":"2641_CR13","doi-asserted-by":"publisher","unstructured":"Ding H, Wu Y, Gong M, Li H, Gong P, Miao Q, Tao X (2024) Point cloud registration via sampling-based evolutionary multitasking. Swarm Evol Comput 89: 101535. https:\/\/doi.org\/10.1016\/j.swevo.2024.101535","DOI":"10.1016\/j.swevo.2024.101535"},{"key":"2641_CR14","doi-asserted-by":"publisher","unstructured":"Ding H, Jiang J, Wu Y, Li H, Gong M, Ma W, Miao Q (2024) Evolutionary multitasking with compatibility graph for point cloud registration. In: 2024 IEEE congress on evolutionary computation (CEC) (pp. 01\u201308). IEEE. https:\/\/doi.org\/10.1109\/CEC60901.2024.10612147","DOI":"10.1109\/CEC60901.2024.10612147"},{"key":"2641_CR15","doi-asserted-by":"publisher","unstructured":"Wu Y, Ding H, Xiang B, Sheng J, Ma W, Qin K, Gong M (2023) Evolutionary multitask optimization in real-world applications: a survey. J Artificial Intell Technol 3(1): 32\u201338. https:\/\/doi.org\/10.1109\/TEVC.2023.3272663","DOI":"10.1109\/TEVC.2023.3272663"},{"key":"2641_CR16","doi-asserted-by":"publisher","unstructured":"Chang YC (2009) N-dimension golden section search: Its variants and limitations. In: 2009 2nd International Conference on Biomedical Engineering and Informatics (pp. 1\u20136). IEEE.https:\/\/doi.org\/10.1109\/BMEI.2009.5304779","DOI":"10.1109\/BMEI.2009.5304779"},{"issue":"1","key":"2641_CR17","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1137\/1019005","volume":"19","author":"JE Dennis Jr","year":"1977","unstructured":"Dennis JE Jr, Mor\u00e9 JJ (1977) Quasi-Newton methods, motivation and theory. SIAM Rev 19(1):46\u201389. https:\/\/doi.org\/10.1137\/1019005","journal-title":"SIAM Rev"},{"key":"2641_CR18","unstructured":"Hager WW, Zhang H (2006) A survey of nonlinear conjugate gradient methods. Pacific journal of optimization 2(1): 35\u201358. https:\/\/people.clas.ufl.edu\/hager\/files\/cg_survey.pdf"},{"key":"2641_CR19","unstructured":"Ranganathan A (2004) The levenberg-marquardt algorithm. Tutoral on LM algorithm 11(1): 101\u2013110. https:\/\/sites.cs.ucsb.edu\/~yfwang\/courses\/cs290i_mvg\/pdf\/LMA.pdf"},{"key":"2641_CR20","doi-asserted-by":"publisher","unstructured":"Andrychowicz M, Denil M, Gomez S, Hoffman MW, Pfau D, Schaul T, De Freitas N (2016) Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems,\u00a029. https:\/\/dl.acm.org\/doi\/abs\/https:\/\/doi.org\/10.5555\/3157382.3157543","DOI":"10.5555\/3157382.3157543"},{"key":"2641_CR21","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/S1570-8659(05)80036-5","volume":"1","author":"\u00c5 Bj\u00f6rck","year":"1990","unstructured":"Bj\u00f6rck \u00c5 (1990) Least squares methods. Handb Numer Anal 1:465\u2013652. https:\/\/doi.org\/10.1016\/S1570-8659(05)80036-5","journal-title":"Handb Numer Anal"},{"issue":"3","key":"2641_CR22","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","volume":"43","author":"RV Rao","year":"2011","unstructured":"Rao RV, Savsani VJ, Vakharia DP (2011) Teaching\u2013learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303\u2013315. https:\/\/doi.org\/10.1016\/j.cad.2010.12.015","journal-title":"Comput Aided Des"},{"key":"2641_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/8548639","volume":"2021","author":"H Bayzidi","year":"2021","unstructured":"Bayzidi H, Talatahari S, Saraee M, Lamarche CP (2021) Social network search for solving engineering optimization problems. Comput Intell Neurosci 2021:1\u201332. https:\/\/doi.org\/10.1155\/2021\/8548639","journal-title":"Comput Intell Neurosci"},{"key":"2641_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113246","volume":"148","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Jin Z (2020) Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Syst Appl 148:113246. https:\/\/doi.org\/10.1016\/j.eswa.2020.113246","journal-title":"Expert Syst Appl"},{"key":"2641_CR25","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1007\/s42235-021-0050-y","volume":"18","author":"J Tu","year":"2021","unstructured":"Tu J, Chen H, Wang M, Gandomi AH (2021) The colony predation algorithm. J Bionic Eng 18:674\u2013710. https:\/\/doi.org\/10.1007\/s42235-021-0050-y","journal-title":"J Bionic Eng"},{"key":"2641_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115079","volume":"181","author":"I Ahmadianfar","year":"2021","unstructured":"Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H (2021) RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079. https:\/\/doi.org\/10.1016\/j.eswa.2021.115079","journal-title":"Expert Syst Appl"},{"key":"2641_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116516","volume":"195","author":"I Ahmadianfar","year":"2022","unstructured":"Ahmadianfar I, Heidari AA, Noshadian S, Chen H, Gandomi AH (2022) INFO: an efficient optimization algorithm based on weighted mean of vectors. Expert Syst Appl 195:116516. https:\/\/doi.org\/10.1016\/j.eswa.2022.116516","journal-title":"Expert Syst Appl"},{"key":"2641_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110454","volume":"268","author":"M Abdel-Basset","year":"2023","unstructured":"Abdel-Basset M, Mohamed R, Azeem SAA, Jameel M, Abouhawwash M (2023) Kepler optimization algorithm: a new metaheuristic algorithm inspired by Kepler\u2019s laws of planetary motion. Knowl-Based Syst 268:110454. https:\/\/doi.org\/10.1016\/j.knosys.2023.110454","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"2641_CR29","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/s10586-024-04753-4","volume":"28","author":"OR Adegboye","year":"2025","unstructured":"Adegboye OR, Feda AK (2025) Improved exponential distribution optimizer: enhancing global numerical optimization problem solving and optimizing machine learning paramseters. Clust Comput 28(2):128. https:\/\/doi.org\/10.1007\/s10586-024-04753-4","journal-title":"Clust Comput"},{"key":"2641_CR30","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s12293-016-0197-y","volume":"9","author":"J Liu","year":"2017","unstructured":"Liu J, Dong X, Xue J, Wang Z, Liu Z (2017) Initial states iterative learning for three-dimensional ballistic endpoint control. Memetic Computing 9:31\u201341. https:\/\/doi.org\/10.1007\/s12293-016-0197-y","journal-title":"Memetic Computing"},{"key":"2641_CR31","doi-asserted-by":"publisher","unstructured":"Van Laarhoven PJ, Aarts EH, van Laarhoven PJ, Aarts EH (1987) Simulated annealing (pp. 7\u201315). Springer Netherlands. https:\/\/doi.org\/10.1007\/978-94-015-7744-1_2","DOI":"10.1007\/978-94-015-7744-1_2"},{"issue":"13","key":"2641_CR32","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232\u20132248. https:\/\/doi.org\/10.1016\/j.ins.2009.03.004","journal-title":"Inf Sci"},{"key":"2641_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120069","volume":"225","author":"L Deng","year":"2023","unstructured":"Deng L, Liu S (2023) Snow ablation optimizer: a novel metaheuristic technique for numerical optimization and engineering design. Expert Syst Appl 225:120069. https:\/\/doi.org\/10.1016\/j.eswa.2023.120069","journal-title":"Expert Syst Appl"},{"key":"2641_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cor.2014.10.008","volume":"55","author":"YJ Zheng","year":"2015","unstructured":"Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1\u201311. https:\/\/doi.org\/10.1016\/j.cor.2014.10.008","journal-title":"Comput Oper Res"},{"key":"2641_CR35","doi-asserted-by":"publisher","unstructured":"Abdel-Basset M, Mohamed R, Sallam KM, Chakrabortty RK (2022) Light spectrum optimizer: a novel physics-inspired metaheuristic optimization algorithm. Mathematics 10(19): 3466.https:\/\/doi.org\/10.3390\/math10193466","DOI":"10.3390\/math10193466"},{"key":"2641_CR36","doi-asserted-by":"publisher","unstructured":"Azizi M, Aickelin U, Khorshidi AH, Baghalzadeh Shishehgarkhaneh M (2023) Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization. Sci Rep 13(1): 226. https:\/\/doi.org\/10.1038\/s41598-022-27344-y","DOI":"10.1038\/s41598-022-27344-y"},{"key":"2641_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3427812","author":"OR Adegboye","year":"2024","unstructured":"Adegboye OR, Feda AK, Ojekemi OS, Agyekum EB, Elattar EE, Kamel S (2024) Refinement of dynamic hunting leadership algorithm for enhanced numerical optimization. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3427812","journal-title":"IEEE Access"},{"key":"2641_CR38","doi-asserted-by":"publisher","unstructured":"Adegboye OR, \u00dclker ED, Feda AK, Agyekum EB, Mbasso WF, Kamel S (2024) Enhanced multi-layer perceptron for CO2 emission prediction with worst moth disrupted moth fly optimization (WMFO). Heliyon, 10(11). https:\/\/doi.org\/10.1016\/j.heliyon.2024.e31850","DOI":"10.1016\/j.heliyon.2024.e31850"},{"issue":"1","key":"2641_CR39","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1038\/s41598-023-50910-x","volume":"14","author":"OR Adegboye","year":"2024","unstructured":"Adegboye OR, Feda AK, Ojekemi OR, Agyekum EB, Khan B, Kamel S (2024) DGS-SCSO: enhancing sand cat swarm optimization with dynamic pinhole imaging and golden sine algorithm for improved numerical optimization performance. Sci Rep 14(1):1491. https:\/\/doi.org\/10.1038\/s41598-023-50910-x","journal-title":"Sci Rep"},{"key":"2641_CR40","doi-asserted-by":"publisher","unstructured":"Abdel-Basset M, Mohamed R, Jameel M, Abouhawwash M (2023) Spider wasp optimizer: A novel meta-heuristic optimization algorithm. Artificial Intell Rev 1\u201364. https:\/\/doi.org\/10.1007\/s10462-023-10446-y","DOI":"10.1007\/s10462-023-10446-y"},{"key":"2641_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115665","volume":"185","author":"H Jia","year":"2021","unstructured":"Jia H, Peng X, Lang C (2021) Remora optimization algorithm. Expert Syst Appl 185:115665. https:\/\/doi.org\/10.1016\/j.eswa.2021.115665","journal-title":"Expert Syst Appl"},{"key":"2641_CR42","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.engappai.2019.01.001","volume":"80","author":"S Shadravan","year":"2019","unstructured":"Shadravan S, Naji HR, Bardsiri VK (2019) The Sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20\u201334. https:\/\/doi.org\/10.1016\/j.engappai.2019.01.001","journal-title":"Eng Appl Artif Intell"},{"issue":"4","key":"2641_CR43","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1007\/s00366-022-01604-x","volume":"39","author":"A Seyyedabbasi","year":"2023","unstructured":"Seyyedabbasi A, Kiani F (2023) Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Eng Comput 39(4):2627\u20132651. https:\/\/doi.org\/10.1007\/s00366-022-01604-x","journal-title":"Eng Comput"},{"issue":"24","key":"2641_CR44","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5949","volume":"32","author":"H Ding","year":"2020","unstructured":"Ding H, Wu Z, Zhao L (2020) Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight. Concurrency Comput 32(24):e5949. https:\/\/doi.org\/10.1002\/cpe.5949","journal-title":"Concurrency Comput"},{"key":"2641_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103479","volume":"90","author":"M Pant","year":"2020","unstructured":"Pant M, Zaheer H, Garcia-Hernandez L, Abraham A (2020) Differential evolution: a review of more than two decades of research. Eng Appl Artif Intell 90:103479. https:\/\/doi.org\/10.1016\/j.engappai.2020.103479","journal-title":"Eng Appl Artif Intell"},{"key":"2641_CR46","doi-asserted-by":"publisher","unstructured":"Mirjalili S, Mirjalili S (2019) Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, 43\u201355. https:\/\/doi.org\/10.1007\/978-3-319-93025-1","DOI":"10.1007\/978-3-319-93025-1"},{"key":"2641_CR47","doi-asserted-by":"publisher","unstructured":"Wu Y, Ding H, Xiang B, Sheng J, Ma W, Qin K, Gong M (2023) Evolutionary multitask optimization in real-world applications: a survey. J Artificial Intell Technol 3(1): 32\u201338. https:\/\/doi.org\/10.37965\/jait.2023.0149","DOI":"10.37965\/jait.2023.0149"},{"issue":"2","key":"2641_CR48","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/4235.771163","volume":"3","author":"X Yao","year":"1999","unstructured":"Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82\u2013102. https:\/\/doi.org\/10.1109\/4235.771163","journal-title":"IEEE Trans Evol Comput"},{"key":"2641_CR49","doi-asserted-by":"publisher","unstructured":"Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs\/0102027. https:\/\/doi.org\/10.48550\/arXiv.cs\/0102027","DOI":"10.48550\/arXiv.cs\/0102027"},{"key":"2641_CR50","doi-asserted-by":"publisher","unstructured":"Adam SP, Alexandropoulos SAN, Pardalos PM, Vrahatis MN (2019) No free lunch theorem: a review. Approximation and optimization: Algorithms, complexity and applications, 57\u201382. https:\/\/doi.org\/10.1007\/978-3-030-12767-1_5","DOI":"10.1007\/978-3-030-12767-1_5"},{"issue":"6","key":"2641_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2017) Feature selection: a data perspective. ACM Comput Surveys (CSUR) 50(6):1\u201345. https:\/\/doi.org\/10.1145\/3136625","journal-title":"ACM Comput Surveys (CSUR)"},{"issue":"2","key":"2641_CR52","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1109\/TASSP.1986.1164814","volume":"34","author":"R Harris","year":"1986","unstructured":"Harris R, Chabries D, Bishop F (1986) A variable step (VS) adaptive filter algorithm. IEEE Trans Acoust Speech Signal Process 34(2):309\u2013316. https:\/\/doi.org\/10.1109\/TASSP.1986.1164814","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"2641_CR53","unstructured":"Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th international conference on machine learning (ICML-03) (pp. 856\u2013863). https:\/\/cdn.aaai.org\/ICML\/2003\/ICML03-111.pdf"},{"key":"2641_CR54","doi-asserted-by":"publisher","unstructured":"Polikar R (2012) Ensemble learning. Ensemble machine learning: Methods and applications, 1\u201334. https:\/\/doi.org\/10.1007\/978-1-4419-9326-7_1","DOI":"10.1007\/978-1-4419-9326-7_1"},{"key":"2641_CR55","doi-asserted-by":"publisher","unstructured":"Kohavi R, John GH (1998) The wrapper approach. In\u00a0Feature Extraction, Construction and Selection: a data mining perspective (pp. 33\u201350). Boston, MA: Springer US. https:\/\/doi.org\/10.1007\/978-1-4615-5725-8_3","DOI":"10.1007\/978-1-4615-5725-8_3"},{"issue":"Suppl 2","key":"2641_CR56","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.1007\/s10462-023-10567-4","volume":"56","author":"H Jia","year":"2023","unstructured":"Jia H, Rao H, Wen C, Mirjalili S (2023) Crayfish optimization algorithm. Artif Intell Rev 56(Suppl 2):1919\u20131979. https:\/\/doi.org\/10.1007\/s10462-023-10567-4","journal-title":"Artif Intell Rev"},{"key":"2641_CR57","doi-asserted-by":"publisher","unstructured":"Chaib L, Tadj M, Choucha A, Khemili FZ, EL-Fergany A (2024) Improved crayfish optimization algorithm for parameters estimation of photovoltaic models. Energy Conversion and Management 313: 118627. https:\/\/doi.org\/10.1016\/j.enconman.2024.118627","DOI":"10.1016\/j.enconman.2024.118627"},{"issue":"6","key":"2641_CR58","doi-asserted-by":"publisher","first-page":"341","DOI":"10.3390\/biomimetics9060341","volume":"9","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Liu P, Li Y (2024) Implementation of an enhanced crayfish optimization algorithm. Biomimetics 9(6):341. https:\/\/doi.org\/10.3390\/biomimetics9060341","journal-title":"Biomimetics"},{"key":"2641_CR59","doi-asserted-by":"publisher","unstructured":"Zhang M, Wen G, Yang J (2021) Duck swarm algorithm: a novel swarm intelligence algorithm. arXiv preprint arXiv:2112.13508. https:\/\/doi.org\/10.48550\/arXiv.2112.13508","DOI":"10.48550\/arXiv.2112.13508"},{"key":"2641_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113609","volume":"376","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https:\/\/doi.org\/10.1016\/j.cma.2020.113609","journal-title":"Comput Methods Appl Mech Eng"},{"key":"2641_CR61","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. https:\/\/doi.org\/10.1016\/j.knosys.2015.12.022","journal-title":"Knowl-Based Syst"},{"issue":"22","key":"2641_CR62","doi-asserted-by":"publisher","first-page":"4350","DOI":"10.3390\/math10224350","volume":"10","author":"D Wu","year":"2022","unstructured":"Wu D, Rao H, Wen C, Jia H, Liu Q, Abualigah L (2022) Modified sand cat swarm optimization algorithm for solving constrained engineering optimization problems. Mathematics 10(22):4350. https:\/\/doi.org\/10.3390\/math10224350","journal-title":"Mathematics"},{"key":"2641_CR63","doi-asserted-by":"publisher","unstructured":"Mafarja MM, Eleyan D, Jaber I, Hammouri A, Mirjalili S (2017) Binary dragonfly algorithm for feature selection. In: 2017 International conference on new trends in computing sciences (ICTCS) (pp 12\u201317). IEEE. https:\/\/doi.org\/10.1109\/ICTCS.2017.43","DOI":"10.1109\/ICTCS.2017.43"},{"key":"2641_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116158","volume":"191","author":"L Abualigah","year":"2022","unstructured":"Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158. https:\/\/doi.org\/10.1016\/j.eswa.2021.116158","journal-title":"Expert Syst Appl"},{"issue":"23","key":"2641_CR65","doi-asserted-by":"publisher","first-page":"16229","DOI":"10.1007\/s00521-021-06224-y","volume":"33","author":"J Too","year":"2021","unstructured":"Too J, Mafarja M, Mirjalili S (2021) Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach. Neural Comput Appl 33(23):16229\u201316250. https:\/\/doi.org\/10.1007\/s00521-021-06224-y","journal-title":"Neural Comput Appl"},{"issue":"3","key":"2641_CR66","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45\u201350. https:\/\/doi.org\/10.1109\/51.932724","journal-title":"IEEE Eng Med Biol Mag"},{"issue":"2","key":"2641_CR67","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1029\/2000RS002432","volume":"36","author":"D Bilitza","year":"2001","unstructured":"Bilitza D (2001) International reference ionosphere 2000. Radio Sci 36(2):261\u2013275. https:\/\/doi.org\/10.1029\/2000RS002432","journal-title":"Radio Sci"},{"issue":"6","key":"2641_CR68","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1159\/000068629","volume":"25","author":"M Uppenkamp","year":"2002","unstructured":"Uppenkamp M, Feller AC (2002) Classification of malignant lymphoma. Oncol Res Treat 25(6):563\u2013570. https:\/\/doi.org\/10.1159\/000068629","journal-title":"Oncol Res Treat"},{"issue":"3","key":"2641_CR69","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1177\/0278364914555720","volume":"34","author":"IM Bullock","year":"2015","unstructured":"Bullock IM, Feix T, Dollar AM (2015) The Yale human grasping dataset: Grasp, object, and task data in household and machine shop environments. Int J Robot Res 34(3):251\u2013255. https:\/\/doi.org\/10.1177\/0278364914555720","journal-title":"Int J Robot Res"},{"key":"2641_CR70","doi-asserted-by":"crossref","unstructured":"Rahman MA, Muniyandi RC (2018) Feature selection from colon cancer dataset for cancer classification using artificial neural network. Int. J. Adv. Sci. Eng. Inf. Technol 8(4\u20132): 1387\u20131393. https:\/\/core.ac.uk\/download\/pdf\/325990403.pdf","DOI":"10.18517\/ijaseit.8.4-2.6790"},{"key":"2641_CR71","doi-asserted-by":"publisher","unstructured":"Sim T, Baker S, Bsat M (2002) The CMU pose, illumination, and expression (PIE) database. In: Proceedings of fifth IEEE international conference on automatic face gesture recognition (pp. 53\u201358). IEEE. https:\/\/doi.org\/10.1109\/AFGR.2002.1004130","DOI":"10.1109\/AFGR.2002.1004130"},{"key":"2641_CR72","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1007\/s00521-020-05136-7","volume":"33","author":"S Liu","year":"2021","unstructured":"Liu S, Mocanu DC, Matavalam ARR, Pei Y, Pechenizkiy M (2021) Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. Neural Comput Appl 33:2589\u20132604. https:\/\/doi.org\/10.1007\/s00521-020-05136-7","journal-title":"Neural Comput Appl"},{"key":"2641_CR73","doi-asserted-by":"crossref","unstructured":"Castillo-Garc\u0131a G, Mor\u00e1n-Fern\u00e1ndez L, Bol\u00f3n-Canedo V (2022) Feature selection for transfer learning using particle swarm optimization and complexity measures. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 7\u201312). https:\/\/www.esann.org\/sites\/default\/files\/proceedings\/2022\/ES2022-57.pdf","DOI":"10.14428\/esann\/2022.ES2022-57"},{"key":"2641_CR74","doi-asserted-by":"publisher","unstructured":"Nie F, Huang H, Cai X, Ding C (2010) Efficient and robust feature selection via joint \u21132, 1-norms minimization. Advances in neural information processing systems 23. https:\/\/dl.acm.org\/doi\/abs\/https:\/\/doi.org\/10.5555\/2997046.2997098","DOI":"10.5555\/2997046.2997098"},{"key":"2641_CR75","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.neucom.2023.02.010","volume":"532","author":"H Su","year":"2023","unstructured":"Su H, Zhao D, Heidari AA, Liu L, Zhang X, Mafarja M, Chen H (2023) RIME: a physics-based optimization. Neurocomputing 532:183\u2013214. https:\/\/doi.org\/10.1016\/j.neucom.2023.02.010","journal-title":"Neurocomputing"},{"key":"2641_CR76","doi-asserted-by":"publisher","first-page":"173548","DOI":"10.1109\/ACCESS.2020.3024108","volume":"8","author":"AG Hussien","year":"2020","unstructured":"Hussien AG, Amin M, Wang M, Liang G, Alsanad A, Gumaei A, Chen H (2020) Crow search algorithm: theory, recent advances, and applications. IEEE Access 8:173548\u2013173565. https:\/\/doi.org\/10.1109\/ACCESS.2020.3024108","journal-title":"IEEE Access"},{"key":"2641_CR77","doi-asserted-by":"publisher","unstructured":"Zhong C, Li G, Meng Z, Li H, Yildiz AR, Mirjalili S (2024) Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers. Neural Comput Appl 1\u201343. https:\/\/doi.org\/10.1007\/s00521-024-10694-1","DOI":"10.1007\/s00521-024-10694-1"},{"key":"2641_CR78","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1007\/s00500-018-3102-4","volume":"23","author":"S Arora","year":"2019","unstructured":"Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715\u2013734. https:\/\/doi.org\/10.1007\/s00500-018-3102-4","journal-title":"Soft Comput"},{"key":"2641_CR79","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.asoc.2015.10.036","volume":"56","author":"A Baykaso\u011flu","year":"2017","unstructured":"Baykaso\u011flu A, Akpinar \u015e (2017) Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems\u2013Part 1: Unconstrained optimization. Appl Soft Comput 56:520\u2013540. https:\/\/doi.org\/10.1016\/j.asoc.2015.10.036","journal-title":"Appl Soft Comput"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02641-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02641-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02641-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:00:27Z","timestamp":1757156427000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02641-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,28]]},"references-count":79,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2641"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02641-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,4,28]]},"assertion":[{"value":"23 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors confirm that they have no conflicts of interest or personal relationships that could have influenced the findings presented in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}