{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T21:13:17Z","timestamp":1769893997547,"version":"3.49.0"},"reference-count":110,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"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":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s12652-025-05008-9","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T11:36:21Z","timestamp":1757676981000},"page":"361-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved Zebra optimization strategies for efficient feature selection in high-dimensional spaces"],"prefix":"10.1007","volume":"17","author":[{"given":"Thompson","family":"Stephan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Punitha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinaytosh","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"issue":"1","key":"5008_CR1","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/s10462-020-09860-3","volume":"54","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset M, Ding W, El-Shahat D (2021) A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artif Intell Rev 54(1):593\u2013637","journal-title":"Artif Intell Rev"},{"issue":"1","key":"5008_CR2","doi-asserted-by":"publisher","first-page":"26047","DOI":"10.1038\/s41598-024-77488-2","volume":"14","author":"F Abdelmalek","year":"2024","unstructured":"Abdelmalek F et al. (2024) Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems. Sci Rep 14(1):26047","journal-title":"Sci Rep"},{"key":"5008_CR3","doi-asserted-by":"crossref","unstructured":"Abualigah LMQ, et al. (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Vol.816. Springer","DOI":"10.1007\/978-3-030-10674-4"},{"issue":"5","key":"5008_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-024-04991-6","volume":"28","author":"OR Adegboye","year":"2025","unstructured":"Adegboye OR et al. (2025) Enhanced global optimization using quadratically interpolated hybrid pathfinder algorithm. Clust Comput 28(5):1\u201337","journal-title":"Clust Comput"},{"issue":"7","key":"5008_CR5","doi-asserted-by":"publisher","first-page":"4157","DOI":"10.3390\/app13074157","volume":"13","author":"OR Adegboye","year":"2023","unstructured":"Adegboye OR, \u00dclker ED (2023) Gaussian mutation specular reflection learning with local escaping operator based artificial electric field algorithm and its engineering application. Appl Sci 13(7):4157","journal-title":"Appl Sci"},{"issue":"19","key":"5008_CR6","doi-asserted-by":"publisher","first-page":"3177","DOI":"10.3390\/electronics11193177","volume":"11","author":"W Albattah","year":"2022","unstructured":"Albattah W et al. (2022) Feature selection techniques for big data Analytics. Electronics 11(19):3177","journal-title":"Electronics"},{"key":"5008_CR7","unstructured":"Alelyani S, Tang J, Liu H (2013) Feature selection for clustering: a review. Data clustering: algorithms and applications, editor: Charu aggarwal and chandan reddy"},{"issue":"1\/2","key":"5008_CR8","first-page":"113","volume":"18","author":"N AlNuaimi","year":"2022","unstructured":"AlNuaimi N et al. (2022) Streaming feature selection algorithms for big data: a survey. Appl Comput Inf 18(1\/2):113\u2013135","journal-title":"Appl Comput Inf"},{"key":"5008_CR9","doi-asserted-by":"publisher","first-page":"31662","DOI":"10.1109\/ACCESS.2021.3060096","volume":"9","author":"R Al-Wajih","year":"2021","unstructured":"Al-Wajih R et al. (2021) Hybrid binary grey wolf with Harris hawks optimizer for feature selection. IEEE Access 9:31662\u201331677","journal-title":"IEEE Access"},{"key":"5008_CR10","doi-asserted-by":"crossref","unstructured":"Amin R, et al. (2024) Hybrid chaotic zebra optimization algorithm and long short-term memory for cyber threats detection. IEEE Access","DOI":"10.1109\/ACCESS.2024.3397303"},{"issue":"1","key":"5008_CR11","doi-asserted-by":"publisher","first-page":"12","DOI":"10.52866\/ijcsm.2024.05.01.023","volume":"5","author":"D Anand","year":"2024","unstructured":"Anand D, Khalaf OI, Chandra GR (2024) Enhancing the zebra optimization algorithm with chaotic sinusoidal map for versatile optimization. Iraqi J Comput Sci Math 5(1):12","journal-title":"Iraqi J Comput Sci Math"},{"issue":"1","key":"5008_CR12","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/72.265960","volume":"5","author":"PJ Angeline","year":"1994","unstructured":"Angeline PJ, Saunders GM, Pollack JB (1994) An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans Neural Networks 5(1):54\u201365","journal-title":"IEEE Trans Neural Networks"},{"key":"5008_CR13","doi-asserted-by":"crossref","unstructured":"Balasubramaniam A, et al. (2023) Breast cancer classification using optimizer-based feature selection: a metaheuristic approach. In: 2023 International conference on next generation electronics (NEleX). IEEE. pp.1\u20136","DOI":"10.1109\/NEleX59773.2023.10421158"},{"key":"5008_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110583","volume":"146","author":"M Banaie-Dezfouli","year":"2023","unstructured":"Banaie-Dezfouli M, Nadimi-Shahraki MH, Beheshti Z (2023) BE-GWO: binary extremum-based grey wolf optimizer for discrete optimization problems. Appl Soft Comput 146:110583","journal-title":"Appl Soft Comput"},{"issue":"2","key":"5008_CR15","doi-asserted-by":"publisher","first-page":"388","DOI":"10.33484\/sinopfbd.1470329","volume":"9","author":"E Ba\u015f","year":"2024","unstructured":"Ba\u015f E, Ba\u015f \u015e (2024) An example of classification using a neural network trained by the zebra optimization algorithm. Sinop \u00dc niversitesi Fen Bilimleri Dergisi 9(2):388\u2013420","journal-title":"Sinop \u00dc niversitesi Fen Bilimleri Dergisi"},{"key":"5008_CR16","doi-asserted-by":"crossref","unstructured":"Bindu NVM, et al. (2025) IoT botnet detection from software defined network using American zebra optimization algorithm with SSRNN-ELM. In: International journal of information technology, pp.1\u20139","DOI":"10.1007\/s41870-024-02348-1"},{"issue":"1\u20132","key":"5008_CR17","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","volume":"97","author":"AL Blum","year":"1997","unstructured":"Blum AL, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97(1\u20132):245\u2013271","journal-title":"Artif Intell"},{"key":"5008_CR18","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s13748-015-0080-y","volume":"5","author":"V Bol\u00f3n-Canedo","year":"2016","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A (2016) Feature selection for high-dimensional data. Progress in artificial intelligence 5:65\u201375","journal-title":"Progress Artific Intell"},{"key":"5008_CR19","doi-asserted-by":"crossref","unstructured":"Bui NDH, Duong TL (2024) An improved zebra optimization algorithm for solving transmission expansion planning problem with penetration of renewable energy sources. Int J Intell Eng Syst 17(1)","DOI":"10.22266\/ijies2024.0229.20"},{"key":"5008_CR20","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","volume":"300","author":"J Cai","year":"2018","unstructured":"Cai J et al. (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70\u201379","journal-title":"Neurocomputing"},{"issue":"5","key":"5008_CR21","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1109\/TSMCB.2005.847740","volume":"35","author":"E Cant\u00fa-Paz","year":"2005","unstructured":"Cant\u00fa-Paz E, Kamath C (2005) An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems. IEEE Trans Syst Man Cybernet Part B (Cybernetics) 35(5):915\u2013927","journal-title":"IEEE Trans Syste Man Cybernet Part B (Cybernetics)"},{"key":"5008_CR22","doi-asserted-by":"crossref","unstructured":"Chandran V, Mohapatra P (2024) A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications. Heliyon 10(10)","DOI":"10.1016\/j.heliyon.2024.e30757"},{"issue":"1","key":"5008_CR23","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Eng 40(1):16\u201328","journal-title":"Computers & electrical engineering"},{"key":"5008_CR24","unstructured":"Das S (2001) Filters, wrappers and a boosting-based hybrid for feature selection. In: Icml. Vol.1. Citeseer. pp.74\u201381"},{"key":"5008_CR25","doi-asserted-by":"crossref","unstructured":"Dash M, Liu H (1997) Feature selection for classification, intelligent data analysis 1","DOI":"10.3233\/IDA-1997-1302"},{"key":"5008_CR26","doi-asserted-by":"crossref","unstructured":"Deepanraj B, et al. (2025) Drilling characteristics optimization of polymer composite fortified with eggshells using box-behnken design and zebra optimization algorithm. In: Results in Engineering, p.104102","DOI":"10.1016\/j.rineng.2025.104102"},{"key":"5008_CR27","doi-asserted-by":"publisher","first-page":"1126450","DOI":"10.3389\/fmech.2022.1126450","volume":"8","author":"M Dehghani","year":"2023","unstructured":"Dehghani M, Trojovsk\u00e1 P (2023) Osprey optimization algorithm: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Frontiers Mech Eng 8:1126450","journal-title":"Frontiers Mech Eng"},{"issue":"6","key":"5008_CR28","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1109\/TSMCB.2012.2193613","volume":"42","author":"R Diao","year":"2012","unstructured":"Diao R, Shen Q (2012) Feature selection with harmony search. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(6):1509\u20131523","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybernetics)"},{"key":"5008_CR29","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s10462-011-9270-6","volume":"39","author":"S Ding","year":"2013","unstructured":"Ding S et al. (2013) Evolutionary artificial neural networks: a review. Artif Intell Rev 39:251\u2013260","journal-title":"Artif Intell Rev"},{"key":"5008_CR30","unstructured":"Dua D, Graff C (2019) UCI Machine Learning Repository. University of California, Irvine, school of information and computer sciences. Accessed for LSVT, DARWIN, Toxicity, Period_Changer. https:\/\/archive.ics.uci.edu\/ml\/index.php"},{"issue":"4","key":"5008_CR31","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10922-024-09860-6","volume":"32","author":"HM El-Hageen","year":"2024","unstructured":"El-Hageen HM et al. (2024) Chaotic zebra optimization algorithm for increasing the lifetime of wireless sensor network. J Netw Syst Manage 32(4):85","journal-title":"J Netw Syst Manage"},{"key":"5008_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2023.117809","volume":"299","author":"MM Elymany","year":"2024","unstructured":"Elymany MM, Enany MA, Elsonbaty NA (2024) Hybrid optimized-ANFIS based MPPT for hybrid microgrid using zebra optimization algorithm and artificial gorilla troops optimizer. Energy Convers Manage 299:117809","journal-title":"Energy Convers Manage"},{"key":"5008_CR33","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","volume":"172","author":"E Emary","year":"2016","unstructured":"Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371\u2013381","journal-title":"Neurocomputing"},{"issue":"4","key":"5008_CR34","first-page":"206","volume":"6","author":"RP Eviningsih","year":"2024","unstructured":"Eviningsih RP et al. (2024) MPPT Algorithm Based on Zebra Optimization Algorithm for Solar Panels System with Partial Shading Conditions. Indones J Electron Electromed Eng Med Inf 6(4):206\u2013218","journal-title":"Indones J Electron Electromed Eng Med Inf"},{"key":"5008_CR35","doi-asserted-by":"crossref","unstructured":"Guangyou Y, (2007) A modified particle swarm optimizer algorithm. In: 2007 8th International conference on electronic measurement and instruments. IEEE. pp.2\u2013675","DOI":"10.1109\/ICEMI.2007.4350772"},{"key":"5008_CR36","doi-asserted-by":"publisher","first-page":"18510","DOI":"10.1038\/s41598-021-97962-5","volume":"11","author":"S Gul","year":"2021","unstructured":"Gul S, Rahim F, Isin S et al. (2021) Structure-based design of antiviral agents to target SARS-CoV-2 main protease. Sci Rep 11:18510","journal-title":"Sci Rep"},{"key":"5008_CR37","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I et al. (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389\u2013422","journal-title":"Mach Learn"},{"key":"5008_CR38","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 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"issue":"5","key":"5008_CR39","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1109\/TETCI.2022.3146330","volume":"6","author":"X He","year":"2022","unstructured":"He X et al. (2022) Adaptive evolution strategies for stochastic zeroth-order optimization. IEEE transactions on emerging topics in computational intelligence 6(5):1271\u20131285","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"5008_CR40","doi-asserted-by":"crossref","unstructured":"Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press","DOI":"10.7551\/mitpress\/1090.001.0001"},{"issue":"1","key":"5008_CR41","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(1):66\u201373","journal-title":"Sci Am"},{"key":"5008_CR42","doi-asserted-by":"crossref","unstructured":"Ishibuchi H, et al. (2009) Evolutionary many-objective optimization by NSGA-II and MOEA\/D with large populations. In: 2009 IEEE international conference on systems, man and cybernetics. IEEE. pp.1758\u20131763","DOI":"10.1109\/ICSMC.2009.5346628"},{"key":"5008_CR43","doi-asserted-by":"crossref","unstructured":"Jensen R, Shen Q (2008) Computational intelligence and feature selection: rough and fuzzy approaches.","DOI":"10.1002\/9780470377888"},{"issue":"1","key":"5008_CR44","first-page":"231","volume":"193","author":"Y Jiang","year":"2007","unstructured":"Jiang Y et al. (2007) An improved particle swarm optimization algorithm. Appl Math Comput 193(1):231\u2013239","journal-title":"Appl Math Comput"},{"key":"5008_CR45","doi-asserted-by":"crossref","unstructured":"Jovi\u0107 A, Brki\u0107 K, Bogunovi\u0107 N (2015) A review of feature selection methods with applications. In: 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO). Ieee. pp. 1200\u20131205","DOI":"10.1109\/MIPRO.2015.7160458"},{"key":"5008_CR46","doi-asserted-by":"crossref","unstructured":"Kattepogu NB, Saravanan G, Rao ARK (2025) An intensified sparrow search algorithm for combined economic emission dispatch including renewables and electric vehicles. Int J Intell Eng Syst 18(1)","DOI":"10.22266\/ijies2025.0229.65"},{"key":"5008_CR47","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995-international conference on neural networks. Vol.4. IEEE. pp.1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"5008_CR48","unstructured":"Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. In: Proceedings of the tenth national conference on Artificial intelligence. pp.129\u2013134"},{"issue":"1\u20132","key":"5008_CR49","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\u20132):273\u2013324","journal-title":"Artif Intell"},{"issue":"6","key":"5008_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J et al. (2017) Feature selection: a data perspective. ACM Comput Surv (CSUR) 50(6):1\u201345","journal-title":"ACM Comput Surv (CSUR)"},{"key":"5008_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110558","volume":"145","author":"X Li","year":"2023","unstructured":"Li X et al. (2023) Multi-objective binary grey wolf optimization for feature selection based on guided mutation strategy. Appl Soft Comput 145:110558","journal-title":"Appl Soft Comput"},{"key":"5008_CR52","unstructured":"Li J, et al. (2017) scikit-feature: Feature selection repository \u2014 datasets. Project website. Accessed for PCMAC, RELATHE, BASEHOCK, Arcene, Prostate_GE, colon. https:\/\/jundongl.github.io\/scikit-feature\/datasets.html"},{"key":"5008_CR53","doi-asserted-by":"publisher","DOI":"10.1201\/9781584888796","volume-title":"Computational methods of feature selection","author":"H Liu","year":"2007","unstructured":"Liu H, Motoda H (2007) Computational methods of feature selection. CRC Press"},{"issue":"4","key":"5008_CR54","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TKDE.2005.66","volume":"17","author":"H Liu","year":"2005","unstructured":"Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491\u2013502","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5008_CR55","unstructured":"Liu H, et al. (2010) Feature selection: An ever evolving frontier in data mining. In: Feature selection in data mining. PMLR. pp.4\u201313"},{"key":"5008_CR56","doi-asserted-by":"crossref","unstructured":"Liu X, et al. (2025)Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization. Briefings in bioinformatics 26(1):bbae715","DOI":"10.1093\/bib\/bbae715"},{"key":"5008_CR57","unstructured":"Liu C, Wang Y-X (2023) Global optimization with parametric function approximation. International conference on machine learning. PMLR. pp.22113\u201322136"},{"issue":"3","key":"5008_CR58","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1109\/TPWRD.2007.899259","volume":"22","author":"MM Mansour","year":"2007","unstructured":"Mansour MM, Mekhamer SF, El-Kharbawe N (2007) A modified particle swarm optimizer for the coordination of directional overcurrent relays. IEEE Trans Power Delivery 22(3):1400\u20131410","journal-title":"IEEE Trans Power Delivery"},{"issue":"6","key":"5008_CR59","first-page":"1","volume":"4","author":"A Memari","year":"2017","unstructured":"Memari A, Ahmad R et al. (2017) Metaheuristic algorithms: guidelines for implementation. J Soft Comput Decis Supp Syst 4(6):1\u20136","journal-title":"J Soft Comput Decis Supp Syst"},{"key":"5008_CR60","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"},{"key":"5008_CR61","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer Adv Eng Softw 69:46\u201361","journal-title":"Grey wolf optimizer Adv Eng Softw"},{"issue":"1","key":"5008_CR62","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1007\/s10489-022-03533-0","volume":"53","author":"H Mohammed","year":"2023","unstructured":"Mohammed H, Rashid T (2023) FOX: a FOX-inspired optimization algorithm. Appl Intell 53(1):1030\u20131050","journal-title":"Appl Intell"},{"issue":"1","key":"5008_CR63","doi-asserted-by":"publisher","first-page":"5211","DOI":"10.1038\/s41598-023-31876-2","volume":"13","author":"S Mohapatra","year":"2023","unstructured":"Mohapatra S, Mohapatra P (2023) American zebra optimization algorithm for global optimization problems. Sci Rep 13(1):5211","journal-title":"Sci Rep"},{"key":"5008_CR64","doi-asserted-by":"crossref","unstructured":"Molina LC, Belanche L, Nebot \u00c0 (2002)\u201cFeature selection algorithms: asurvey and experimental evaluation\u201d. In: 2002 IEEE International conference on data mining, 2002. Proceedings. IEEE. , pp.306\u2013313","DOI":"10.1109\/ICDM.2002.1183917"},{"key":"5008_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113917","volume":"166","author":"MH Nadimi-Shahraki","year":"2021","unstructured":"Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917","journal-title":"Expert Syst Appl"},{"key":"5008_CR66","unstructured":"Nardone D (2019) Biological datasets for machine learning. Zenodo. Accessed for GLI_85, ALLAML, CLL_SUB_111, GLIOMA, LEUKEMIA, LUNG_DISCRETE, GCM, Carcinom. https:\/\/zenodo.org\/records\/3556992"},{"key":"5008_CR67","doi-asserted-by":"crossref","unstructured":"\u00d6zbay FA (2025) An Enhanced zebra optimization algorithm with multiple strategies for global optimization and feature selection problems: a hepatocellular carcinoma case study. In: IEEE Access","DOI":"10.1109\/ACCESS.2025.3541975"},{"key":"5008_CR68","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.swevo.2017.08.002","volume":"38","author":"LK Panwar","year":"2018","unstructured":"Panwar LK et al. (2018) Binary grey wolf optimizer for large scale unit commitment problem. Swarm Evol Comput 38:251\u2013266","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"5008_CR69","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1021\/acscombsci.6b00136","volume":"19","author":"TK Patra","year":"2017","unstructured":"Patra TK et al. (2017) Neural-network-biased genetic algorithms for materials design: evolutionary algorithms that learn. ACS Comb Sci 19(2):96\u2013107","journal-title":"ACS Comb Sci"},{"key":"5008_CR70","doi-asserted-by":"crossref","unstructured":"Pedrycz W, Sillitti A, Succi G (2016) Computational intelligence: an introduction. In: Computational Intelligence and quantitative software engineering. Springer, pp.13\u201331","DOI":"10.1007\/978-3-319-25964-2_2"},{"issue":"8","key":"5008_CR71","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(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5008_CR72","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","volume":"1","author":"R Poli","year":"2007","unstructured":"Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1:33\u201357","journal-title":"Swarm Intell"},{"issue":"12","key":"5008_CR73","doi-asserted-by":"publisher","first-page":"5016","DOI":"10.3390\/su16125016","volume":"16","author":"Z Qi","year":"2024","unstructured":"Qi Z et al. (2024) Renewable energy distributed energy system optimal configuration and performance analysis: improved zebra optimization algorithm. Sustainability 16(12):5016","journal-title":"Sustainability"},{"key":"5008_CR74","doi-asserted-by":"crossref","unstructured":"Rana A, (2022) A ZEBRA optimization algorithm search for improving localization in wireless sensor network. In: 2022 2nd International conference on technological advancements in computational sciences (ICTACS). IEEE. pp.817\u2013824","DOI":"10.1109\/ICTACS56270.2022.9988278"},{"key":"5008_CR75","doi-asserted-by":"crossref","unstructured":"Rani TJ, Kavitha D (2024) Classification of Epileptic Seizures Using LSTM Based Zebra Optimization Algorithm with Hyperparameter Tuning. Int J Intell Eng Syst 17(3)","DOI":"10.22266\/ijies2024.0630.07"},{"key":"5008_CR76","doi-asserted-by":"crossref","unstructured":"Rehman AU, et al. (2016) A new image encryption scheme based on dynamic s-boxes and chaotic maps. 3D Research 7:1\u20138","DOI":"10.1007\/s13319-016-0084-9"},{"key":"5008_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103375","volume":"112","author":"B Remeseiro","year":"2019","unstructured":"Remeseiro B, Bolon-Canedo V (2019) A review of feature selection methods in medical applications. Comput Biol Med 112:103375","journal-title":"Comput Biol Med"},{"issue":"1","key":"5008_CR78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-024-06548-1","volume":"81","author":"Q Ren","year":"2025","unstructured":"Ren Q, Feng F (2025) PID parameter tuning optimization based on multi-strategy fusion improved zebra optimization algorithm. J Supercomput 81(1):1\u201339","journal-title":"J Supercomput"},{"key":"5008_CR79","unstructured":"Reunanen J (2003) Overfitting in making comparisons between variable selection methods. J Mach Learn Res 3, pp.1371\u20131382"},{"key":"5008_CR80","volume":"41","author":"MK Roberts","year":"2024","unstructured":"Roberts MK, Ramasamy P, Dahan F (2024) An innovative approach for cluster head selection and energy optimization in wireless sensor networks using zebra fish and sea horse optimization techniques. J Ind Inf Integr 41:100642","journal-title":"J Ind Inf Integr"},{"key":"5008_CR81","doi-asserted-by":"crossref","unstructured":"Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507\u20132517","DOI":"10.1093\/bioinformatics\/btm344"},{"issue":"5","key":"5008_CR82","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 Recogn Lett 10(5):335\u2013347","journal-title":"Pattern Recogn Lett"},{"key":"5008_CR83","doi-asserted-by":"crossref","unstructured":"Song H, Zhang M, Shi Q, (2024) Capacity optimization of hybrid energy storage system based on improved zebra optimization algorithm. In: 2024 IEEE 7th information technology, networking, electronic and automation control conference (ITNEC). Vol.7. IEEE. pp.1390\u20131394","DOI":"10.1109\/ITNEC60942.2024.10733249"},{"issue":"5\u20136","key":"5008_CR84","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/j.parco.2003.12.015","volume":"30","author":"T Sousa","year":"2004","unstructured":"Sousa T, Silva A, Neves A (2004) Particle swarm based data mining algorithms for classification tasks. Parallel Comput 30(5\u20136):767\u2013783","journal-title":"Parallel Comput"},{"key":"5008_CR85","doi-asserted-by":"crossref","unstructured":"Sun Y, et al. (2024) Research and application of improved Zebra optimization algorithm. In: 2024 5th International conference on computer engineering and application (ICCEA). IEEE. pp.203\u201327","DOI":"10.1109\/ICCEA62105.2024.10603809"},{"issue":"1","key":"5008_CR86","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0241-0","volume":"6","author":"K Tadist","year":"2019","unstructured":"Tadist K et al. (2019) Feature selection methods and genomic big data: a systematic review. J Big Data 6(1):1\u201324","journal-title":"J Big Data"},{"key":"5008_CR87","first-page":"268","volume":"2","author":"EG Talbi","year":"2009","unstructured":"Talbi EG (2009) Metaheuristics: from design to implementation. John Wiley & Sons google schola 2:268\u2013308","journal-title":"John Wiley & Sons google schola"},{"key":"5008_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116550","volume":"195","author":"T Thaher","year":"2022","unstructured":"Thaher T et al. (2022) Boolean particle swarm optimization with various evolutionary population dynamics approaches for feature selection problems. Expert Syst Appl 195:116550","journal-title":"Expert Syst Appl"},{"key":"5008_CR89","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.swevo.2018.01.011","volume":"41","author":"D Tian","year":"2018","unstructured":"Tian D, Shi Z (2018) MPSO: Modified particle swarm optimization and its applications. Swarm Evol Comput 41:49\u201368","journal-title":"Swarm Evol Comput"},{"key":"5008_CR90","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s12293-015-0173-y","volume":"8","author":"B Tran","year":"2016","unstructured":"Tran B, Xue B, Zhang M (2016) Genetic programming for feature construction and selection in classification on high-dimensional data. Memetic Comput 8:3\u201315","journal-title":"Memetic Comput"},{"key":"5008_CR91","doi-asserted-by":"publisher","first-page":"49445","DOI":"10.1109\/ACCESS.2022.3172789","volume":"10","author":"E Trojovsk\u00e1","year":"2022","unstructured":"Trojovsk\u00e1 E, Dehghani M, Trojovsk\u00e1 P (2022) Zebra optimization algorithm: a new bio-inspired optimization algorithm for solving optimization algorithm. IEEE Access 10:49445\u201349473","journal-title":"Ieee Access"},{"key":"5008_CR92","unstructured":"Tsanas A, et al. (2013) Objective Assessment of motor speech disorders in parkinson\u2019s disease using acoustic features and machine learning. In: IEEE Trans Neural Syst Rehab Eng"},{"key":"5008_CR93","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111737","volume":"295","author":"X Wang","year":"2024","unstructured":"Wang X et al. (2024) Artificial Protozoa Optimizer (APO): a novel bio-inspired metaheuristic algorithm for engineering optimization. Knowl-Based Syst 295:111737","journal-title":"Knowl-Based Syst"},{"issue":"9","key":"5008_CR94","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/s10462-025-11269-9","volume":"58","author":"L Wang","year":"2025","unstructured":"Wang L et al. (2025) Tianji\u2019s horse racing optimization (THRO): a new metaheuristic inspired by ancient wisdom and its engineering optimization applications. Artif Intell Rev 58(9):282","journal-title":"Artif Intell Rev"},{"issue":"17","key":"5008_CR95","doi-asserted-by":"publisher","first-page":"7803","DOI":"10.3390\/app14177803","volume":"14","author":"H Wang","year":"2024","unstructured":"Wang H, Mansor NNB, Mokhlis HB (2024) Novel hybrid optimization technique for solar photovoltaic output prediction using improved hippopotamus algorithm. Appl Sci 14(17):7803","journal-title":"Appl Sci"},{"issue":"4","key":"5008_CR96","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2015","unstructured":"Xue B et al. (2015) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606\u2013626","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"5008_CR97","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","volume":"43","author":"B Xue","year":"2012","unstructured":"Xue B, Zhang M, Browne WN (2012) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybernet 43(6):1656\u20131671","journal-title":"IEEE Trans Cybernet"},{"issue":"2","key":"5008_CR98","first-page":"1205","volume":"189","author":"X Yang","year":"2007","unstructured":"Yang X et al. (2007) A modified particle swarm optimizer with dynamic adaptation. Appl Math Comput 189(2):1205\u20131213","journal-title":"Appl Math Comput"},{"key":"5008_CR99","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2020.101104","volume":"46","author":"X-S Yang","year":"2020","unstructured":"Yang X-S (2020) Nature-inspired optimization algorithms: challenges and open problems. J Comput Sci 46:101104","journal-title":"J Comput Sci"},{"issue":"16","key":"5008_CR100","doi-asserted-by":"publisher","first-page":"7074","DOI":"10.3390\/app14167074","volume":"14","author":"B Yang","year":"2024","unstructured":"Yang B et al. (2024) Classification of rock mass quality in underground rock engineering with incomplete data using xgboost model and zebra optimization algorithm. Appl Sci 14(16):7074","journal-title":"Appl Sci"},{"key":"5008_CR101","doi-asserted-by":"crossref","unstructured":"Yang J, Honavar V (1998) Feature subset selection using a genetic algorithm. IEEE Intell Syst Appl 13(2):44\u201349","DOI":"10.1109\/5254.671091"},{"issue":"2","key":"5008_CR102","first-page":"162","volume":"27","author":"X Yao","year":"2012","unstructured":"Yao X et al. (2012) A Review on feature selection methods. Control Decis 27(2):162\u2013166","journal-title":"Control Decis"},{"key":"5008_CR103","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113020","volume":"310","author":"X Yuefeng","year":"2025","unstructured":"Yuefeng X et al. (2025) Crested ibis algorithm and its application in human-powered aircraft design. Knowl-Based Syst 310:113020","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"5008_CR104","first-page":"301","volume":"3","author":"G Zames","year":"1981","unstructured":"Zames G (1981) Genetic algorithms in search, optimization and machine learning. Inf Tech J 3(1):301","journal-title":"Inf Tech J"},{"key":"5008_CR105","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.swevo.2018.02.021","volume":"42","author":"HM Zawbaa","year":"2018","unstructured":"Zawbaa HM et al. (2018) Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach. Swarm Evol Comput 42:29\u201342","journal-title":"Swarm Evol Comput"},{"key":"5008_CR106","doi-asserted-by":"crossref","unstructured":"Zebari RR, et al. (2021) A review on automation artificial neural networks based on evolutionary algorithms. In: 2021 14th international conference on developments in esystems engineering (DeSE). IEEE. pp.235\u2013240","DOI":"10.1109\/DeSE54285.2021.9719492"},{"issue":"1","key":"5008_CR107","doi-asserted-by":"publisher","first-page":"15779","DOI":"10.1038\/s41598-025-00076-5","volume":"15","author":"Y Zhang","year":"2025","unstructured":"Zhang Y et al. (2025) Dynamic gold rush optimizer: fusing worker adaptation and salp navigation mechanism for enhanced search. Sci Rep 15(1):15779","journal-title":"Sci Rep"},{"issue":"11","key":"5008_CR108","doi-asserted-by":"publisher","first-page":"6933","DOI":"10.1007\/s10115-024-02179-3","volume":"66","author":"R Zhong","year":"2024","unstructured":"Zhong R, Zhang C, Jun Yu (2024) Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems. Knowl Inf Syst 66(11):6933\u20136974","journal-title":"Knowl Inf Syst"},{"key":"5008_CR109","doi-asserted-by":"crossref","unstructured":"Zhong R, et al. (2025) Enhanced Crested ibis algorithm: performance validation in benchmark functions, engineering problem, and application in brain tumor detection. Expert Syst Appl 128231","DOI":"10.1016\/j.eswa.2025.128231"},{"issue":"18","key":"5008_CR110","doi-asserted-by":"publisher","first-page":"5975","DOI":"10.3390\/s24185975","volume":"24","author":"S Zhou","year":"2024","unstructured":"Zhou S et al. (2024) Novel multi-classification dynamic detection model for android malware based on improved zebra optimization algorithm and lightGBM. Sensors (Basel Switzerland) 24(18):5975","journal-title":"Sensors (Basel Switzerland)"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-025-05008-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-025-05008-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-025-05008-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:03:28Z","timestamp":1769850208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-025-05008-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"references-count":110,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["5008"],"URL":"https:\/\/doi.org\/10.1007\/s12652-025-05008-9","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,12]]},"assertion":[{"value":"20 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 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 declare that there is no conflict of interest regarding the publication of this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}