{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:43:02Z","timestamp":1783784582152,"version":"3.55.0"},"reference-count":101,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"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. &amp; Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02462-3","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T03:26:01Z","timestamp":1737689161000},"page":"3461-3499","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Advancing bankruptcy prediction: a study on an improved rime optimization algorithm and its application in feature selection"],"prefix":"10.1007","volume":"16","author":[{"given":"Yaoxian","family":"Ji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Asghar","family":"Heidari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengwen","family":"Wu","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":[[2025,1,24]]},"reference":[{"issue":"4","key":"2462_CR1","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1111\/j.1540-6261.1968.tb00843.x","volume":"23","author":"EI Altman","year":"1968","unstructured":"Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance 23(4):589\u2013609","journal-title":"J Finance"},{"issue":"4","key":"2462_CR2","doi-asserted-by":"publisher","first-page":"743","DOI":"10.24136\/oc.2019.034","volume":"10","author":"M Kovacova","year":"2019","unstructured":"Kovacova M et al (2019) Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries. Oeconomia Copernicana 10(4):743\u2013772","journal-title":"Oeconomia Copernicana"},{"key":"2462_CR3","first-page":"178","volume":"17","author":"C Clement","year":"2020","unstructured":"Clement C (2020) Machine learning in bankruptcy prediction\u2013a review. J Public Administ Finance Law 17:178\u2013196","journal-title":"J Public Administ Finance Law"},{"issue":"5","key":"2462_CR4","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.3846\/tede.2021.15106","volume":"27","author":"AD Voda","year":"2021","unstructured":"Voda AD et al (2021) Corporate bankruptcy and insolvency prediction model. Technol Econ Dev Econ 27(5):1039\u20131056","journal-title":"Technol Econ Dev Econ"},{"key":"2462_CR5","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.aej.2022.11.002","volume":"66","author":"M Shaheen","year":"2023","unstructured":"Shaheen M, Naheed N, Ahsan A (2023) Relevance-diversity algorithm for feature selection and modified Bayes for prediction. Alex Eng J 66:329\u2013342","journal-title":"Alex Eng J"},{"key":"2462_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120275","volume":"227","author":"J Radovanovic","year":"2023","unstructured":"Radovanovic J, Haas C (2023) The evaluation of bankruptcy prediction models based on socio-economic costs. Expert Syst Appl 227:120275","journal-title":"Expert Syst Appl"},{"key":"2462_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121418","volume":"237","author":"E da Silva Mattos","year":"2024","unstructured":"da Silva Mattos E, Shasha D (2024) Bankruptcy prediction with low-quality financial information. Expert Syst Appl 237:121418","journal-title":"Expert Syst Appl"},{"key":"2462_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120714","volume":"233","author":"T-K Chen","year":"2023","unstructured":"Chen T-K et al (2023) Bankruptcy prediction using machine learning models with the text-based communicative value of annual reports. Expert Syst Appl 233:120714","journal-title":"Expert Syst Appl"},{"issue":"10","key":"2462_CR9","doi-asserted-by":"publisher","first-page":"176","DOI":"10.3390\/risks11100176","volume":"11","author":"D M\u00e1t\u00e9","year":"2023","unstructured":"M\u00e1t\u00e9 D, Raza H, Ahmad I (2023) Comparative analysis of machine learning models for bankruptcy prediction in the context of pakistani companies. Risks 11(10):176","journal-title":"Risks"},{"key":"2462_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113155","volume":"146","author":"Z Chen","year":"2020","unstructured":"Chen Z, Chen W, Shi Y (2020) Ensemble learning with label proportions for bankruptcy prediction. Expert Syst Appl 146:113155","journal-title":"Expert Syst Appl"},{"key":"2462_CR11","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.econmod.2019.04.003","volume":"84","author":"M Zori\u010d\u00e1k","year":"2020","unstructured":"Zori\u010d\u00e1k M et al (2020) Bankruptcy prediction for small-and medium-sized companies using severely imbalanced datasets. Econ Model 84:165\u2013176","journal-title":"Econ Model"},{"issue":"7","key":"2462_CR12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0254030","volume":"16","author":"H Wang","year":"2021","unstructured":"Wang H, Liu X (2021) Undersampling bankruptcy prediction: Taiwan bankruptcy data. PLoS ONE 16(7):e0254030","journal-title":"PLoS ONE"},{"issue":"2","key":"2462_CR13","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s13042-022-01653-0","volume":"14","author":"L Sun","year":"2023","unstructured":"Sun L et al (2023) TSFNFS: two-stage-fuzzy-neighborhood feature selection with binary whale optimization algorithm. Int J Mach Learn Cybern 14(2):609\u2013631","journal-title":"Int J Mach Learn Cybern"},{"key":"2462_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113429","volume":"140","author":"G Kou","year":"2021","unstructured":"Kou G et al (2021) Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decis Support Syst 140:113429","journal-title":"Decis Support Syst"},{"key":"2462_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2021.113576","volume":"147","author":"P du Jardin","year":"2021","unstructured":"du Jardin P (2021) Dynamic self-organizing feature map-based models applied to bankruptcy prediction. Decis Support Syst 147:113576","journal-title":"Decis Support Syst"},{"key":"2462_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ribaf.2022.101844","volume":"64","author":"SB Jabeur","year":"2023","unstructured":"Jabeur SB, Serret V (2023) Bankruptcy prediction using fuzzy convolutional neural networks. Res Int Bus Financ 64:101844","journal-title":"Res Int Bus Financ"},{"issue":"3","key":"2462_CR17","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/s13042-022-01658-9","volume":"14","author":"M Han","year":"2023","unstructured":"Han M et al (2023) A survey of multi-label classification based on supervised and semi-supervised learning. Int J Mach Learn Cybern 14(3):697\u2013724","journal-title":"Int J Mach Learn Cybern"},{"key":"2462_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.120658","volume":"166","author":"SB Jabeur","year":"2021","unstructured":"Jabeur SB et al (2021) CatBoost model and artificial intelligence techniques for corporate failure prediction. Technol Forecast Soc Chang 166:120658","journal-title":"Technol Forecast Soc Chang"},{"key":"2462_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115559","volume":"185","author":"Y Zelenkov","year":"2021","unstructured":"Zelenkov Y, Volodarskiy N (2021) Bankruptcy prediction on the base of the unbalanced data using multi-objective selection of classifiers. Expert Syst Appl 185:115559","journal-title":"Expert Syst Appl"},{"key":"2462_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119390","volume":"216","author":"SH Cho","year":"2023","unstructured":"Cho SH, Shin K-S (2023) Feature-weighted counterfactual-based explanation for bankruptcy prediction. Expert Syst Appl 216:119390","journal-title":"Expert Syst Appl"},{"issue":"6","key":"2462_CR21","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1016\/j.jksuci.2017.10.007","volume":"32","author":"J Uthayakumar","year":"2020","unstructured":"Uthayakumar J, Vengattaraman T, Dhavachelvan P (2020) Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: an application of bankruptcy prediction and credit risk analysis. J King Saud Univ-Comput Inf Sci 32(6):647\u2013657","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"2462_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.irfa.2022.102174","volume":"82","author":"E Fedorova","year":"2022","unstructured":"Fedorova E et al (2022) Economic policy uncertainty and bankruptcy filings. Int Rev Financ Anal 82:102174","journal-title":"Int Rev Financ Anal"},{"issue":"10","key":"2462_CR23","doi-asserted-by":"publisher","first-page":"6858","DOI":"10.1109\/TAP.2020.3001743","volume":"68","author":"L Cui","year":"2020","unstructured":"Cui L et al (2020) A modified efficient KNN method for antenna optimization and design. IEEE Trans Antennas Propag 68(10):6858\u20136866","journal-title":"IEEE Trans Antennas Propag"},{"issue":"3","key":"2462_CR24","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1007\/s10462-021-10044-w","volume":"55","author":"S Goyal","year":"2022","unstructured":"Goyal S (2022) Handling class-imbalance with KNN (neighbourhood) under-sampling for software defect prediction. Artif Intell Rev 55(3):2023\u20132064","journal-title":"Artif Intell Rev"},{"key":"2462_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108437","volume":"175","author":"J Zhao","year":"2024","unstructured":"Zhao J et al (2024) Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction. Comput Biol Med 175:108437","journal-title":"Comput Biol Med"},{"issue":"6","key":"2462_CR26","doi-asserted-by":"publisher","first-page":"5380","DOI":"10.1109\/TCYB.2020.3031610","volume":"52","author":"Z Bian","year":"2020","unstructured":"Bian Z et al (2020) Fuzzy KNN method with adaptive nearest neighbors. IEEE Trans Cybern 52(6):5380\u20135393","journal-title":"IEEE Trans Cybern"},{"key":"2462_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2023.01.011","volume":"461","author":"S An","year":"2023","unstructured":"An S et al (2023) Robust fuzzy rough approximations with kNN granules for semi-supervised feature selection. Fuzzy Sets Syst 461:108476","journal-title":"Fuzzy Sets Syst"},{"key":"2462_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100663","volume":"54","author":"BH Nguyen","year":"2020","unstructured":"Nguyen BH, Xue B, Zhang M (2020) A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol Comput 54:100663","journal-title":"Swarm Evol Comput"},{"key":"2462_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104210","volume":"100","author":"M Rostami","year":"2021","unstructured":"Rostami M et al (2021) Review of swarm intelligence-based feature selection methods. Eng Appl Artif Intell 100:104210","journal-title":"Eng Appl Artif Intell"},{"key":"2462_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123667","volume":"249","author":"MC Barbieri","year":"2024","unstructured":"Barbieri MC, Grisci BI, Dorn M (2024) Analysis and comparison of feature selection methods towards performance and stability. Expert Syst Appl 249:123667","journal-title":"Expert Syst Appl"},{"key":"2462_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122504","volume":"240","author":"T Ouaderhman","year":"2024","unstructured":"Ouaderhman T, Chamlal H, Janane FZ (2024) A new filter-based gene selection approach in the DNA microarray domain. Expert Syst Appl 240:122504","journal-title":"Expert Syst Appl"},{"key":"2462_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108432","volume":"176","author":"MA Islam","year":"2024","unstructured":"Islam MA et al (2024) Precision healthcare: a deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction. Comput Biol Med 176:108432","journal-title":"Comput Biol Med"},{"key":"2462_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.111018","volume":"150","author":"J Liu","year":"2024","unstructured":"Liu J et al (2024) A feature selection method based on multiple feature subsets extraction and result fusion for improving classification performance. Appl Soft Comput 150:111018","journal-title":"Appl Soft Comput"},{"key":"2462_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110154","volume":"148","author":"J Ma","year":"2024","unstructured":"Ma J, Xu F, Rong X (2024) Discriminative multi-label feature selection with adaptive graph diffusion. Pattern Recogn 148:110154","journal-title":"Pattern Recogn"},{"key":"2462_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111191","volume":"284","author":"Z Beheshti","year":"2024","unstructured":"Beheshti Z (2024) A fuzzy transfer function based on the behavior of meta-heuristic algorithm and its application for high-dimensional feature selection problems. Knowl-Based Syst 284:111191","journal-title":"Knowl-Based Syst"},{"key":"2462_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100559","volume":"49","author":"M Nssibi","year":"2023","unstructured":"Nssibi M, Manita G, Korbaa O (2023) Advances in nature-inspired metaheuristic optimization for feature selection problem: a comprehensive survey. Comput Sci Rev 49:100559","journal-title":"Comput Sci Rev"},{"key":"2462_CR37","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/MWC.010.2300321","volume":"31","author":"H Zhou","year":"2024","unstructured":"Zhou H et al (2024) Heuristic algorithms for RIS-assisted wireless networks: exploring heuristic-aided machine learning. IEEE Wireless Commun 31:106\u2013111","journal-title":"IEEE Wireless Commun"},{"issue":"5","key":"2462_CR38","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.26599\/TST.2023.9010140","volume":"29","author":"L Gui","year":"2024","unstructured":"Gui L et al (2024) Domain knowledge used in meta-heuristic algorithms for the job-shop scheduling problem: review and analysis. Tsinghua Sci Technol 29(5):1368\u20131389","journal-title":"Tsinghua Sci Technol"},{"key":"2462_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101462","volume":"84","author":"J Zhang","year":"2024","unstructured":"Zhang J et al (2024) A survey of meta-heuristic algorithms in optimization of space scale expansion. Swarm Evol Comput 84:101462","journal-title":"Swarm Evol Comput"},{"issue":"4","key":"2462_CR40","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1109\/4235.797971","volume":"3","author":"GR Harik","year":"1999","unstructured":"Harik GR, Lobo FG, Goldberg DE (1999) The compact genetic algorithm. IEEE Trans Evol Comput 3(4):287\u2013297","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"2462_CR41","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TEVC.2010.2059031","volume":"15","author":"S Das","year":"2010","unstructured":"Das S, Suganthan PN (2010) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4\u201331","journal-title":"IEEE Trans Evol Comput"},{"key":"2462_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121254","volume":"236","author":"F Li","year":"2024","unstructured":"Li F et al (2024) A fast density peak clustering based particle swarm optimizer for dynamic optimization. Expert Syst Appl 236:121254","journal-title":"Expert Syst Appl"},{"key":"2462_CR43","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA et al (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849\u2013872","journal-title":"Futur Gener Comput Syst"},{"issue":"6","key":"2462_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-024-10716-3","volume":"57","author":"S Fu","year":"2024","unstructured":"Fu S et al (2024) Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D\/3D UAV path planning and engineering design problems. Artif Intell Rev 57(6):1\u201389","journal-title":"Artif Intell Rev"},{"key":"2462_CR45","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.engappai.2019.08.025","volume":"86","author":"SHS Moosavi","year":"2019","unstructured":"Moosavi SHS, Bardsiri VK (2019) Poor and rich optimization algorithm: a new human-based and multi populations algorithm. Eng Appl Artif Intell 86:165\u2013181","journal-title":"Eng Appl Artif Intell"},{"key":"2462_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122638","volume":"241","author":"J Lian","year":"2024","unstructured":"Lian J, Hui G (2024) Human evolutionary optimization algorithm. Expert Syst Appl 241:122638","journal-title":"Expert Syst Appl"},{"key":"2462_CR47","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.swevo.2018.02.018","volume":"41","author":"E Rashedi","year":"2018","unstructured":"Rashedi E, Rashedi E, Nezamabadi-Pour H (2018) A comprehensive survey on gravitational search algorithm. Swarm Evol Comput 41:141\u2013158","journal-title":"Swarm Evol Comput"},{"key":"2462_CR48","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.knosys.2018.08.030","volume":"163","author":"W Zhao","year":"2019","unstructured":"Zhao W, Wang L, Zhang Z (2019) Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl-Based Syst 163:283\u2013304","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"2462_CR49","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"2462_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106520","volume":"153","author":"C Zhong","year":"2023","unstructured":"Zhong C et al (2023) A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection. Comput Biol Med 153:106520","journal-title":"Comput Biol Med"},{"key":"2462_CR51","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.aej.2022.12.045","volume":"68","author":"A Chhabra","year":"2023","unstructured":"Chhabra A, Hussien AG, Hashim FA (2023) Improved bald eagle search algorithm for global optimization and feature selection. Alex Eng J 68:141\u2013180","journal-title":"Alex Eng J"},{"key":"2462_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123362","volume":"248","author":"BD Kwakye","year":"2024","unstructured":"Kwakye BD et al (2024) Particle guided metaheuristic algorithm for global optimization and feature selection problems. Expert Syst Appl 248:123362","journal-title":"Expert Syst Appl"},{"key":"2462_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107298","volume":"105","author":"K Panwar","year":"2021","unstructured":"Panwar K, Deep K (2021) Discrete grey wolf optimizer for symmetric travelling salesman problem. Appl Soft Comput 105:107298","journal-title":"Appl Soft Comput"},{"issue":"3","key":"2462_CR54","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.artmed.2013.11.002","volume":"60","author":"AO de Carvalho Filho","year":"2014","unstructured":"de Carvalho Filho AO et al (2014) Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artif Intell Med 60(3):165\u2013177","journal-title":"Artif Intell Med"},{"key":"2462_CR55","doi-asserted-by":"crossref","unstructured":"Wang L, Shen J, Yong J (2012) A survey on bio-inspired algorithms for web service composition. In: Proceedings of the 2012 IEEE 16th international conference on computer supported cooperative work in design (CSCWD). IEEE","DOI":"10.1109\/CSCWD.2012.6221875"},{"issue":"7","key":"2462_CR56","doi-asserted-by":"publisher","first-page":"5605","DOI":"10.1007\/s11831-022-09778-9","volume":"29","author":"S Sharma","year":"2022","unstructured":"Sharma S, Kumar V (2022) A comprehensive review on multi-objective optimization techniques: past, present and future. Arch Comput Methods Eng 29(7):5605\u20135633","journal-title":"Arch Comput Methods Eng"},{"issue":"5","key":"2462_CR57","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s11831-018-9289-9","volume":"26","author":"KG Dhal","year":"2019","unstructured":"Dhal KG et al (2019) A survey on nature-inspired optimization algorithms and their application in image enhancement domain. Arch Comput Methods Eng 26(5):1607\u20131638","journal-title":"Arch Comput Methods Eng"},{"key":"2462_CR58","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 (2023) RIME: A physics-based optimization. Neurocomputing 532:183\u2013214","journal-title":"Neurocomputing"},{"key":"2462_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108219","volume":"174","author":"L Guo","year":"2024","unstructured":"Guo L et al (2024) An improved RIME optimization algorithm for lung cancer image segmentation. Comput Biol Med 174:108219","journal-title":"Comput Biol Med"},{"key":"2462_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.139957","volume":"434","author":"B Yang","year":"2024","unstructured":"Yang B et al (2024) Mismatch losses mitigation of PV-TEG hybrid system via improved RIME algorithm: design and hardware validation. J Clean Prod 434:139957","journal-title":"J Clean Prod"},{"key":"2462_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.130726","volume":"294","author":"W Liu","year":"2024","unstructured":"Liu W et al (2024) A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM. Energy 294:130726","journal-title":"Energy"},{"key":"2462_CR62","unstructured":"Wu G, Mallipeddi R, Suganthan PN (2017) Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Repor"},{"key":"2462_CR63","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s00500-008-0323-y","volume":"13","author":"J Alcal\u00e1-Fdez","year":"2009","unstructured":"Alcal\u00e1-Fdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13:307\u2013318","journal-title":"Soft Comput"},{"issue":"10","key":"2462_CR64","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 (2010) 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","journal-title":"Inf Sci"},{"key":"2462_CR65","doi-asserted-by":"publisher","DOI":"10.52465\/joiser.v1i2.180","author":"WF Abror","year":"2023","unstructured":"Abror WF, Alamsyah A, Aziz MAA (2023) Bankruptcy prediction using genetic algorithm-support vector machine (GA-SVM) feature selection and stacking. J Inf Syst Explor Res. https:\/\/doi.org\/10.52465\/joiser.v1i2.180","journal-title":"J Inf Syst Explor Res"},{"key":"2462_CR66","doi-asserted-by":"publisher","first-page":"24497","DOI":"10.1109\/ACCESS.2023.3253512","volume":"11","author":"GS Rani","year":"2023","unstructured":"Rani GS, Jayan S, Alatas B (2023) Analysis of chaotic maps for global optimization and a hybrid chaotic pattern search algorithm for optimizing the reliability of a bank. IEEE Access 11:24497\u201324510","journal-title":"IEEE Access"},{"issue":"5","key":"2462_CR67","doi-asserted-by":"publisher","first-page":"3761","DOI":"10.1007\/s00366-020-01234-1","volume":"38","author":"C Yu","year":"2022","unstructured":"Yu C et al (2022) SGOA: annealing-behaved grasshopper optimizer for global tasks. Eng Comput 38(5):3761\u20133788","journal-title":"Eng Comput"},{"issue":"1","key":"2462_CR68","first-page":"1","volume":"23","author":"Z Annuur Zakiah","year":"2024","unstructured":"Annuur Zakiah Z et al (2024) An enhanced ant colony optimisation algorithm with the Hellinger distance for Shariah-compliant securities companies bankruptcy prediction. J Inf Commun Technol 23(1):1\u201324","journal-title":"J Inf Commun Technol"},{"issue":"6","key":"2462_CR69","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1016\/j.jksuci.2017.10.007","volume":"32","author":"J Uthayakumar","year":"2020","unstructured":"Uthayakumar J, Vengattaraman T, Dhavachelvan P (2020) Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: an application of bankruptcy prediction and credit risk analysis. J King Saud Univ Comput Inf Sci 32(6):647\u2013657","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"2462_CR70","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.engappai.2017.05.003","volume":"63","author":"M Wang","year":"2017","unstructured":"Wang M et al (2017) Grey wolf optimization evolving kernel extreme learning machine: application to bankruptcy prediction. Eng Appl Artif Intell 63:54\u201368","journal-title":"Eng Appl Artif Intell"},{"issue":"42","key":"2462_CR71","first-page":"225","volume":"11","author":"E Alizadeh","year":"2022","unstructured":"Alizadeh E, Vakilifard H, Hamidian M (2022) Predicting corporate financial indicators using the conditional average estimator and genetic metaheuristic algorithms. J Invest Knowl 11(42):225\u2013245","journal-title":"J Invest Knowl"},{"key":"2462_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123839","volume":"249","author":"Y Zelenkov","year":"2024","unstructured":"Zelenkov Y (2024) Firm failure prediction using genetic programming generated features. Expert Syst Appl 249:123839","journal-title":"Expert Syst Appl"},{"issue":"6","key":"2462_CR73","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1016\/j.physa.2011.12.004","volume":"391","author":"A Lipowski","year":"2012","unstructured":"Lipowski A, Lipowska D (2012) Roulette-wheel selection via stochastic acceptance. Physica A 391(6):2193\u20132196","journal-title":"Physica A"},{"issue":"15","key":"2462_CR74","first-page":"8121","volume":"219","author":"P Civicioglu","year":"2013","unstructured":"Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121\u20138144","journal-title":"Appl Math Comput"},{"issue":"11","key":"2462_CR75","doi-asserted-by":"publisher","first-page":"3944","DOI":"10.1109\/TFUZZ.2023.3272316","volume":"31","author":"B Sang","year":"2023","unstructured":"Sang B et al (2023) Active antinoise fuzzy dominance rough feature selection using adaptive k-nearest neighbors. IEEE Trans Fuzzy Syst 31(11):3944\u20133958","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"2462_CR76","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.eswa.2019.06.044","volume":"137","author":"Y Zhang","year":"2019","unstructured":"Zhang Y et al (2019) Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Syst Appl 137:46\u201358","journal-title":"Expert Syst Appl"},{"key":"2462_CR77","doi-asserted-by":"crossref","unstructured":"Mohamed AW, et al. (2017) LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE.","DOI":"10.1109\/CEC.2017.7969307"},{"key":"2462_CR78","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.chemolab.2015.08.020","volume":"149","author":"F Marini","year":"2015","unstructured":"Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemomet Intell Lab Syst 149:153\u2013165","journal-title":"Chemomet Intell Lab Syst"},{"key":"2462_CR79","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","volume":"89","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228\u2013249","journal-title":"Knowl-Based Syst"},{"issue":"13","key":"2462_CR80","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","journal-title":"Inf Sci"},{"key":"2462_CR81","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1201\/9780429422614-13","volume-title":"Swarm intelligence algorithms","author":"X-S Yang","year":"2020","unstructured":"Yang X-S, Slowik A (2020) Firefly algorithm. Swarm intelligence algorithms. CRC Press, pp 163\u2013174"},{"key":"2462_CR82","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s00521-015-1870-7","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495\u2013513","journal-title":"Neural Comput Appl"},{"key":"2462_CR83","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"key":"2462_CR84","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili S et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163\u2013191","journal-title":"Adv Eng Softw"},{"key":"2462_CR85","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":"Adv Eng Softw"},{"key":"2462_CR86","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.asoc.2018.02.042","volume":"67","author":"X Xia","year":"2018","unstructured":"Xia X, Gui L, Zhan Z-H (2018) A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting. Appl Soft Comput 67:126\u2013140","journal-title":"Appl Soft Comput"},{"issue":"2","key":"2462_CR87","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 (2012) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241\u2013258","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"2462_CR88","first-page":"210","volume":"16","author":"X Li","year":"2011","unstructured":"Li X, Yao X (2011) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evol Comput 16(2):210\u2013224","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"2462_CR89","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/106365603321828970","volume":"11","author":"N Hansen","year":"2003","unstructured":"Hansen N, M\u00fcller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1\u201318","journal-title":"Evol Comput"},{"issue":"3","key":"2462_CR90","first-page":"243","volume":"6","author":"S Mostafa Bozorgi","year":"2019","unstructured":"Mostafa Bozorgi S, Yazdani S (2019) IWOA: an improved whale optimization algorithm for optimization problems. J Comput Des Eng 6(3):243\u2013259","journal-title":"J Comput Des Eng"},{"issue":"8","key":"2462_CR91","doi-asserted-by":"publisher","first-page":"4864","DOI":"10.1002\/int.22744","volume":"37","author":"J Hu","year":"2022","unstructured":"Hu J et al (2022) Chaotic diffusion-limited aggregation enhanced grey wolf optimizer: insights, analysis, binarization, and feature selection. Int J Intell Syst 37(8):4864\u20134927","journal-title":"Int J Intell Syst"},{"issue":"10","key":"2462_CR92","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2023.107736","volume":"26","author":"X Li","year":"2023","unstructured":"Li X et al (2023) Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design. Iscience 26(10):107736","journal-title":"Iscience"},{"key":"2462_CR93","doi-asserted-by":"publisher","first-page":"159218","DOI":"10.1109\/ACCESS.2021.3129255","volume":"9","author":"B Ma","year":"2021","unstructured":"Ma B et al (2021) Enhanced sparrow search algorithm with mutation strategy for global optimization. IEEE Access 9:159218\u2013159261","journal-title":"IEEE Access"},{"key":"2462_CR94","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.energy.2016.01.052","volume":"99","author":"X Chen","year":"2016","unstructured":"Chen X et al (2016) Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy 99:170\u2013180","journal-title":"Energy"},{"issue":"4","key":"2462_CR95","doi-asserted-by":"publisher","first-page":"3813","DOI":"10.3934\/mbe.2021192","volume":"18","author":"Y Jiang","year":"2021","unstructured":"Jiang Y et al (2021) An efficient binary Gradient-based optimizer for feature selection. Math Biosci Eng 18(4):3813\u20133854","journal-title":"Math Biosci Eng"},{"key":"2462_CR96","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.102130","volume":"58","author":"L Li","year":"2023","unstructured":"Li L et al (2023) A LightGBM-based strategy to predict tunnel rockmass class from TBM construction data for building control. Adv Eng Inform 58:102130","journal-title":"Adv Eng Inform"},{"key":"2462_CR97","doi-asserted-by":"publisher","first-page":"23366","DOI":"10.1109\/ACCESS.2023.3253885","volume":"11","author":"H Yang","year":"2023","unstructured":"Yang H et al (2023) Predicting coronary heart disease using an improved LightGBM model: performance analysis and comparison. IEEE Access 11:23366\u201323380","journal-title":"IEEE Access"},{"key":"2462_CR98","doi-asserted-by":"publisher","first-page":"105971","DOI":"10.1016\/j.envsoft.2024.105971","volume":"174","author":"M Niazkar","year":"2024","unstructured":"Niazkar M et al (2024) Applications of XGBoost in water resources engineering: a systematic literature review (Dec 2018\u2013May 2023). Environ Model Software 174:105971","journal-title":"Environ Model Software"},{"issue":"1","key":"2462_CR99","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s12040-023-02210-1","volume":"133","author":"A Joshi","year":"2023","unstructured":"Joshi A et al (2023) Application of XGBoost model for early prediction of earthquake magnitude from waveform data. J Earth Syst Sci 133(1):5","journal-title":"J Earth Syst Sci"},{"key":"2462_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121549","volume":"237","author":"Z Sun","year":"2024","unstructured":"Sun Z et al (2024) An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Syst Appl 237:121549","journal-title":"Expert Syst Appl"},{"key":"2462_CR101","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120138","volume":"225","author":"NEI Karabadji","year":"2023","unstructured":"Karabadji NEI et al (2023) Accuracy and diversity-aware multi-objective approach for random forest construction. Expert Syst Appl 225:120138","journal-title":"Expert Syst Appl"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02462-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02462-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02462-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T09:02:27Z","timestamp":1749286947000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02462-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":101,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2462"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02462-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]},"assertion":[{"value":"31 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}