{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T20:00:52Z","timestamp":1784836852751,"version":"3.55.0"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10586-026-06029-5","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T11:32:00Z","timestamp":1781263920000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EDRIME: combining exponential distribution optimizer and directed crossover for feature selection"],"prefix":"10.1007","volume":"29","author":[{"given":"Yufeng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boli","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Asghar","family":"Heidari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"issue":"8","key":"6029_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3718364","volume":"57","author":"M Shahnawaz","year":"2025","unstructured":"Shahnawaz, M., Kumar, M.: A Comprehensive Survey on Big Data Analytics: Characteristics, Tools and Techniques. ACM Comput. Surveys. 57(8), 1\u201333 (2025)","journal-title":"ACM Comput. Surveys"},{"key":"6029_CR2","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: Unsupervised Discriminative Feature Selection With $$\\ell _ {2, 0}$$-Norm Constrained Sparse Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)","DOI":"10.1109\/TPAMI.2025.3580669"},{"issue":"2","key":"6029_CR3","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1007\/s11831-024-10165-9","volume":"32","author":"R Rishabh","year":"2025","unstructured":"Rishabh, R., Das, K.N.: A critical review on metaheuristic algorithms based multi-criteria decision-making approaches and applications. Arch. Comput. Methods Eng. 32(2), 963\u2013993 (2025)","journal-title":"Arch. Comput. Methods Eng."},{"key":"6029_CR4","doi-asserted-by":"publisher","first-page":"101899","DOI":"10.1016\/j.swevo.2025.101899","volume":"94","author":"X Yue","year":"2025","unstructured":"Yue, X., et al.: A high-dimensional feature selection algorithm via fast dimensionality reduction and multi-objective differential evolution. Swarm Evol. Comput. 94, 101899 (2025)","journal-title":"Swarm Evol. Comput."},{"issue":"4","key":"6029_CR5","doi-asserted-by":"publisher","first-page":"2549","DOI":"10.1007\/s11831-024-10218-z","volume":"32","author":"QS Hamad","year":"2025","unstructured":"Hamad, Q.S., et al.: A review of enhancing sine cosine algorithm: Common approaches for improved metaheuristic algorithms. Arch. Comput. Methods Eng. 32(4), 2549\u20132606 (2025)","journal-title":"Arch. Comput. Methods Eng."},{"issue":"11","key":"6029_CR6","doi-asserted-by":"publisher","first-page":"13187","DOI":"10.1007\/s10462-023-10470-y","volume":"56","author":"K Rajwar","year":"2023","unstructured":"Rajwar, K., Deep, K., Das, S.: An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif. Intell. Rev. 56(11), 13187\u201313257 (2023)","journal-title":"Artif. Intell. Rev."},{"issue":"1","key":"6029_CR7","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66\u201373 (1992)","journal-title":"Sci. Am."},{"key":"6029_CR8","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN\u201995-international conference on neural networks. IEEE. (1995)"},{"issue":"4598","key":"6029_CR9","first-page":"671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick, S., Gelatt, C.D. Jr., Vecchi, M.P.: Optimization simulated annealing Sci. 220(4598), 671\u2013680 (1983)","journal-title":"Optimization simulated annealing Sci."},{"issue":"4","key":"6029_CR10","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28\u201339 (2006)","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"3","key":"6029_CR11","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1287\/ijoc.1.3.190","volume":"1","author":"F Glover","year":"1989","unstructured":"Glover, F.: Tabu search\u2014part I. ORSA J. Comput. 1(3), 190\u2013206 (1989)","journal-title":"ORSA J. Comput."},{"key":"6029_CR12","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.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459\u2013471 (2007)","journal-title":"J. Global Optim."},{"key":"6029_CR13","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46\u201361 (2014)","journal-title":"Adv. Eng. Softw."},{"key":"6029_CR14","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.: SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 96, 120\u2013133 (2016)","journal-title":"Knowl. Based Syst."},{"key":"6029_CR15","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.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51\u201367 (2016)","journal-title":"Adv. Eng. Softw."},{"key":"6029_CR16","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution\u2013a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341\u2013359 (1997)","journal-title":"J. Global Optim."},{"key":"6029_CR17","doi-asserted-by":"crossref","unstructured":"Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65\u201374. Springer (2010)","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"6029_CR18","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","volume":"111","author":"S Li","year":"2020","unstructured":"Li, S., et al.: Slime mould algorithm: A new method for stochastic optimization. Future generation Comput. Syst. 111, 300\u2013323 (2020)","journal-title":"Future generation Comput. Syst."},{"key":"6029_CR19","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari, A.A., et al.: Harris hawks optimization: Algorithm and applications. Future generation Comput. Syst. 97, 849\u2013872 (2019)","journal-title":"Future generation Comput. Syst."},{"issue":"1","key":"6029_CR20","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67\u201382 (1997)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"6029_CR21","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s12065-024-00998-5","volume":"18","author":"XW Wang","year":"2025","unstructured":"Wang, X.W.: Draco lizard optimizer: a novel metaheuristic algorithm for global optimization problems. Evol. Intel. 18(1), 10 (2025)","journal-title":"Evol. Intel."},{"issue":"11","key":"6029_CR22","doi-asserted-by":"publisher","first-page":"115275","DOI":"10.1088\/1402-4896\/ad86f7","volume":"99","author":"XW Wang","year":"2024","unstructured":"Wang, X.W.: Eurasian lynx optimizer: a novel metaheuristic optimization algorithm for global optimization and engineering applications. Phys. Scr. 99(11), 115275 (2024)","journal-title":"Phys. Scr."},{"issue":"12","key":"6029_CR23","doi-asserted-by":"publisher","first-page":"125280","DOI":"10.1088\/1402-4896\/ad91f2","volume":"99","author":"XW Wang","year":"2024","unstructured":"Wang, X.W.: Artificial meerkat algorithm: a new metaheuristic algorithm for solving optimization problems. Phys. Scr. 99(12), 125280 (2024)","journal-title":"Phys. Scr."},{"issue":"1","key":"6029_CR24","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10462-024-11008-6","volume":"58","author":"KC Ouyang","year":"2025","unstructured":"Ouyang, K.C., et al.: Escape: an optimization method based on crowd evacuation behaviors. Artif. Intell. Rev. 58(1), 19 (2025)","journal-title":"Artif. Intell. Rev."},{"issue":"5","key":"6029_CR25","first-page":"184","volume":"11","author":"B.L. Zheng","year":"2024","unstructured":"Zheng, B.L., et al.: The moss growth optimization (MGO): concepts and performance. J. Comput. Des. Eng. 11(5), 184\u2013221 (2024)","journal-title":"J. Comput. Des. Eng."},{"key":"6029_CR26","doi-asserted-by":"publisher","first-page":"102207","DOI":"10.1016\/j.swevo.2025.102207","volume":"99","author":"H Ren","year":"2025","unstructured":"Ren, H., et al.: GMO: A general multimodal optimization framework applicable to various global metaheuristic algorithms. Swarm Evol. Comput. 99, 102207 (2025)","journal-title":"Swarm Evol. Comput."},{"key":"6029_CR27","doi-asserted-by":"publisher","first-page":"127281","DOI":"10.1016\/j.eswa.2025.127281","volume":"279","author":"HA Nabi","year":"2025","unstructured":"Nabi, H.A., Faraj, K.H.A.: Enhanced classification of web services using hybrid meta-heuristic algorithms and deep learning. Expert Syst. Appl. 279, 127281 (2025)","journal-title":"Expert Syst. Appl."},{"issue":"12","key":"6029_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-025-11377-6","volume":"58","author":"MS Shaikh","year":"2025","unstructured":"Shaikh, M.S., et al.: Applications, classifications, and challenges: A comprehensive evaluation of recently developed metaheuristics for search and analysis. Artif. Intell. Rev. 58(12), 1\u2013110 (2025)","journal-title":"Artif. Intell. Rev."},{"key":"6029_CR29","doi-asserted-by":"crossref","unstructured":"Palakonda, V., et al.: Differential evolution with stochastic selection for uncertain environments: A smart grid application. In: 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE (2018)","DOI":"10.1109\/CEC.2018.8477809"},{"issue":"2","key":"6029_CR30","first-page":"154","volume":"3","author":"S Gao","year":"2025","unstructured":"Gao, S., et al.: Cooperative target allocation for heterogeneous agent models using a matrix-encoding genetic algorithm. J. Inform. Intell. 3(2), 154\u2013172 (2025)","journal-title":"J. Inform. Intell."},{"issue":"9","key":"6029_CR31","doi-asserted-by":"publisher","first-page":"12239","DOI":"10.1007\/s10586-024-04587-0","volume":"27","author":"R Zhong","year":"2024","unstructured":"Zhong, R., Yu, J.: DEA 2 H 2: Differential evolution architecture based adaptive hyper-heuristic algorithm for continuous optimization. Cluster Comput. 27(9), 12239\u201312266 (2024)","journal-title":"Cluster Comput."},{"key":"6029_CR32","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., et al.: RIME: A physics-based optimization. Neurocomputing. 532, 183\u2013214 (2023)","journal-title":"NEUROCOMPUTING"},{"key":"6029_CR33","doi-asserted-by":"crossref","unstructured":"Li, Y.P., et al.: CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images. Comput. Biol. Med. 169 (2024)","DOI":"10.1016\/j.compbiomed.2023.107838"},{"key":"6029_CR34","doi-asserted-by":"crossref","unstructured":"Zhu, W., et al.: An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines. Sensors 23(21) (2023)","DOI":"10.3390\/s23218787"},{"key":"6029_CR35","doi-asserted-by":"publisher","first-page":"102648","DOI":"10.1016\/j.displa.2024.102648","volume":"82","author":"J Xing","year":"2024","unstructured":"Xing, J., et al.: WHRIME: A weight-based recursive hierarchical RIME optimizer for breast cancer histopathology image segmentation. Displays. 82, 102648 (2024)","journal-title":"Displays"},{"key":"6029_CR36","doi-asserted-by":"publisher","first-page":"107408","DOI":"10.1016\/j.compbiomed.2023.107408","volume":"165","author":"X Yu","year":"2023","unstructured":"Yu, X., et al.: Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension. Comput. Biol. Med. 165, 107408 (2023)","journal-title":"Comput. Biol. Med."},{"key":"6029_CR37","doi-asserted-by":"publisher","first-page":"107551","DOI":"10.1016\/j.compbiomed.2023.107551","volume":"166","author":"W Zhu","year":"2023","unstructured":"Zhu, W., et al.: IDRM: Brain tumor image segmentation with boosted RIME optimization. Comput. Biol. Med. 166, 107551 (2023)","journal-title":"Comput. Biol. Med."},{"key":"6029_CR38","doi-asserted-by":"publisher","first-page":"139957","DOI":"10.1016\/j.jclepro.2023.139957","volume":"434","author":"B Yang","year":"2024","unstructured":"Yang, B., et al.: Mismatch losses mitigation of PV-TEG hybrid system via improved RIME algorithm: Design and hardware validation. J. Clean. Prod. 434, 139957 (2024)","journal-title":"J. Clean. Prod."},{"issue":"10","key":"6029_CR39","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda, S., et al.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180(10), 2044\u20132064 (2010)","journal-title":"Inf. Sci."},{"issue":"1","key":"6029_CR40","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"J Derrac","year":"2011","unstructured":"Derrac, J., et al.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3\u201318 (2011)","journal-title":"Swarm Evol. Comput."},{"issue":"1","key":"6029_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/0952813X.2023.2183267","volume":"37","author":"Z Sadeghian","year":"2025","unstructured":"Sadeghian, Z., et al.: A review of feature selection methods based on meta-heuristic algorithms. J. Exp. Theor. Artif. Intell. 37(1), 1\u201351 (2025)","journal-title":"J. Exp. Theor. Artif. Intell."},{"issue":"1","key":"6029_CR42","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1186\/s40537-025-01205-7","volume":"12","author":"MM Emam","year":"2025","unstructured":"Emam, M.M., et al.: Multi strategy Horned Lizard Optimization Algorithm for complex optimization and advanced feature selection problems. J. Big Data. 12(1), 148 (2025)","journal-title":"J. Big Data"},{"issue":"5","key":"6029_CR43","doi-asserted-by":"publisher","first-page":"btaf266","DOI":"10.1093\/bioinformatics\/btaf266","volume":"41","author":"Y Dai","year":"2025","unstructured":"Dai, Y., et al.: High-dimensional biomarker identification for interpretable disease prediction via machine learning models. Bioinformatics. 41(5), btaf266 (2025)","journal-title":"Bioinformatics"},{"issue":"Mar","key":"6029_CR44","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157\u20131182 (2003)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"6029_CR45","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1007\/s11042-023-15675-5","volume":"83","author":"H Ming","year":"2024","unstructured":"Ming, H., Heyong, W.: Filter feature selection methods for text classification: a review. Multimedia Tools Appl. 83(1), 2053\u20132091 (2024)","journal-title":"Multimedia Tools Appl."},{"issue":"2","key":"6029_CR46","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s41060-024-00509-w","volume":"20","author":"R Iranzad","year":"2025","unstructured":"Iranzad, R., Liu, X.: A review of random forest-based feature selection methods for data science education and applications. Int. J. Data Sci. Analytics. 20(2), 197\u2013211 (2025)","journal-title":"Int. J. Data Sci. Analytics"},{"issue":"1","key":"6029_CR47","doi-asserted-by":"publisher","first-page":"22887","DOI":"10.1038\/s41598-025-05545-5","volume":"15","author":"M Karthikeyan","year":"2025","unstructured":"Karthikeyan, M., et al.: Integration of metaheuristic based feature selection with ensemble representation learning models for privacy aware cyberattack detection in IoT environments. Sci. Rep. 15(1), 22887 (2025)","journal-title":"Sci. Rep."},{"key":"6029_CR48","doi-asserted-by":"publisher","first-page":"110171","DOI":"10.1016\/j.compbiomed.2025.110171","volume":"191","author":"M Sowmiya","year":"2025","unstructured":"Sowmiya, M., Rekha, B.B., Malar, E.: Optimized heart disease prediction model using a meta-heuristic feature selection with improved binary salp swarm algorithm and stacking classifier. Comput. Biol. Med. 191, 110171 (2025)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"6029_CR49","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1186\/s40537-025-01132-7","volume":"12","author":"P Sarker","year":"2025","unstructured":"Sarker, P., et al.: Breast cancer prediction with feature-selected XGB classifier, optimized by metaheuristic algorithms. J. Big Data. 12(1), 78 (2025)","journal-title":"J. Big Data"},{"issue":"1","key":"6029_CR50","doi-asserted-by":"publisher","first-page":"4803","DOI":"10.1038\/s41598-025-86362-8","volume":"15","author":"A Roy","year":"2025","unstructured":"Roy, A., et al.: Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images: A. Roy. Sci. Rep. 15(1), 4803 (2025)","journal-title":"Sci. Rep."},{"issue":"1","key":"6029_CR51","doi-asserted-by":"publisher","first-page":"2601378","DOI":"10.1080\/08839514.2025.2601378","volume":"40","author":"T Stephan","year":"2026","unstructured":"Stephan, T., et al.: A Comprehensive Study of Grey Wolf Optimizer Variants for Optimizing Feature Selection in High-Dimensional Data. Appl. Artif. Intell. 40(1), 2601378 (2026)","journal-title":"Appl. Artif. Intell."},{"key":"6029_CR52","doi-asserted-by":"crossref","unstructured":"Song, X., et al.: A Surrogate-Assisted Multi-Phase Ensemble Feature Selection Algorithm With Particle Swarm Optimization in Imbalanced Data. IEEE Transactions on Emerging Topics in Computational Intelligence (2025)","DOI":"10.1109\/TETCI.2025.3548786"},{"issue":"7","key":"6029_CR53","doi-asserted-by":"publisher","first-page":"6101","DOI":"10.1007\/s10462-022-10328-9","volume":"56","author":"J-S Pan","year":"2023","unstructured":"Pan, J.-S., et al.: A survey on binary metaheuristic algorithms and their engineering applications. Artif. Intell. Rev. 56(7), 6101\u20136167 (2023)","journal-title":"Artif. Intell. Rev."},{"issue":"1\u20133","key":"6029_CR54","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing. 70(1\u20133), 489\u2013501 (2006)","journal-title":"Neurocomputing"},{"key":"6029_CR55","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.ast.2017.10.024","volume":"71","author":"F Lu","year":"2017","unstructured":"Lu, F., et al.: Dual reduced kernel extreme learning machine for aero-engine fault diagnosis. Aerosp. Sci. Technol. 71, 742\u2013750 (2017)","journal-title":"Aerosp. Sci. Technol."},{"issue":"1","key":"6029_CR56","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3390\/math12010103","volume":"12","author":"W Chai","year":"2023","unstructured":"Chai, W., et al.: GSA-KELM-KF: A Hybrid Model for Short-Term Traffic Flow Forecasting. Mathematics. 12(1), 103 (2023)","journal-title":"Mathematics"},{"key":"6029_CR57","doi-asserted-by":"publisher","first-page":"106674","DOI":"10.1016\/j.compbiomed.2023.106674","volume":"156","author":"H Yang","year":"2023","unstructured":"Yang, H., Liu, H., Li, G.: A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases. Comput. Biol. Med. 156, 106674 (2023)","journal-title":"Comput. Biol. Med."},{"key":"6029_CR58","doi-asserted-by":"publisher","first-page":"2781","DOI":"10.1109\/JSTARS.2021.3059451","volume":"14","author":"H Chen","year":"2021","unstructured":"Chen, H., et al.: A hyperspectral image classification method using multifeature vectors and optimized KELM. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 14, 2781\u20132795 (2021)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"6029_CR59","doi-asserted-by":"publisher","first-page":"105539","DOI":"10.1016\/j.ast.2019.105539","volume":"96","author":"J Lu","year":"2020","unstructured":"Lu, J., Huang, J., Lu, F.: Kernel extreme learning machine with iterative picking scheme for failure diagnosis of a turbofan engine. Aerosp. Sci. Technol. 96, 105539 (2020)","journal-title":"Aerosp. Sci. Technol."},{"key":"6029_CR60","doi-asserted-by":"publisher","first-page":"108134","DOI":"10.1016\/j.compbiomed.2024.108134","volume":"172","author":"M Zhang","year":"2024","unstructured":"Zhang, M., et al.: Anticipating interpersonal sensitivity: a predictive model for early intervention in psychological disorders in college students. Comput. Biol. Med. 172, 108134 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"16","key":"6029_CR61","doi-asserted-by":"publisher","first-page":"3574","DOI":"10.3390\/math11163574","volume":"11","author":"W Chai","year":"2023","unstructured":"Chai, W., et al.: GA-KELM: Genetic-algorithm-improved kernel extreme learning machine for traffic flow forecasting. Mathematics. 11(16), 3574 (2023)","journal-title":"Mathematics"},{"key":"6029_CR62","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s12065-019-00295-6","volume":"14","author":"N Parida","year":"2021","unstructured":"Parida, N., et al.: Development and performance evaluation of hybrid KELM models for forecasting of agro-commodity price. Evol. Intel. 14, 529\u2013544 (2021)","journal-title":"Evol. Intel."},{"key":"6029_CR63","doi-asserted-by":"publisher","first-page":"106930","DOI":"10.1016\/j.soildyn.2021.106930","volume":"150","author":"Z Zhao","year":"2021","unstructured":"Zhao, Z., Duan, W., Cai, G.: A novel PSO-KELM based soil liquefaction potential evaluation system using CPT and Vs measurements. Soil Dyn. Earthq. Eng. 150, 106930 (2021)","journal-title":"Soil Dyn. Earthq. Eng."},{"issue":"9","key":"6029_CR64","doi-asserted-by":"publisher","first-page":"9329","DOI":"10.1007\/s10462-023-10403-9","volume":"56","author":"M Abdel-Basset","year":"2023","unstructured":"Abdel-Basset, M., et al.: Exponential distribution optimizer (EDO): A novel math-inspired algorithm for global optimization and engineering problems. Artif. Intell. Rev. 56(9), 9329\u20139400 (2023)","journal-title":"Artif. Intell. Rev."},{"issue":"9","key":"6029_CR65","doi-asserted-by":"publisher","first-page":"9051","DOI":"10.1007\/s10462-022-10370-7","volume":"56","author":"S Wu","year":"2023","unstructured":"Wu, S., et al.: Gaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures. Artif. Intell. Rev. 56(9), 9051\u20139087 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"6029_CR66","doi-asserted-by":"publisher","first-page":"112999","DOI":"10.1016\/j.eswa.2019.112999","volume":"142","author":"H Chen","year":"2020","unstructured":"Chen, H., et al.: Efficient multi-population outpost fruit fly-driven optimizers: Framework and advances in support vector machines. Expert Syst. Appl. 142, 112999 (2020)","journal-title":"Expert Syst. Appl."},{"key":"6029_CR67","doi-asserted-by":"crossref","unstructured":"Kumar, A., Misra, R.K., Singh, D.: Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE (2017)","DOI":"10.1109\/CEC.2017.7969524"},{"key":"6029_CR68","doi-asserted-by":"crossref","unstructured":"Awad, N.H., Ali, M.Z., Suganthan, P.N.: Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE (2017)","DOI":"10.1109\/CEC.2017.7969336"},{"issue":"2","key":"6029_CR69","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1109\/TEVC.2011.2173577","volume":"17","author":"W-N Chen","year":"2012","unstructured":"Chen, W.-N., et al.: Particle swarm optimization with an aging leader and challengers. IEEE Trans. Evol. Comput. 17(2), 241\u2013258 (2012)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"6029_CR70","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.eswa.2017.07.043","volume":"90","author":"M Abd Elaziz","year":"2017","unstructured":"Abd Elaziz, M., Oliva, D., Xiong, S.: An improved opposition-based sine cosine algorithm for global optimization. Expert Syst. Appl. 90, 484\u2013500 (2017)","journal-title":"Expert Syst. Appl."},{"key":"6029_CR71","doi-asserted-by":"crossref","unstructured":"Tanabe, R., Fukunaga, A.S.: Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE (2014)","DOI":"10.1109\/CEC.2014.6900380"},{"issue":"2","key":"6029_CR72","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","volume":"2","author":"X-S Yang","year":"2010","unstructured":"Yang, X.-S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. bio-inspired Comput. 2(2), 78\u201384 (2010)","journal-title":"Int. J. bio-inspired Comput."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-06029-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-026-06029-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-06029-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T19:02:12Z","timestamp":1784833332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-026-06029-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":72,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["6029"],"URL":"https:\/\/doi.org\/10.1007\/s10586-026-06029-5","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"11 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"326"}}