{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T11:02:14Z","timestamp":1773658934663,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"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":["Evol. Intel."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s12065-025-01126-7","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T11:58:25Z","timestamp":1767873505000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A weighted voting-based ensemble classifier based on manta ray foraging optimizer for cyberattack detection in IoT environments: a comparative study"],"prefix":"10.1007","volume":"19","author":[{"given":"Alaa","family":"Hassan","sequence":"first","affiliation":[]},{"given":"Reda","family":"Mohamed","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Abdel-Basset","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Abouhawwash","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"1126_CR1","doi-asserted-by":"crossref","first-page":"103251","DOI":"10.1016\/j.cose.2023.103251","volume":"129","author":"S Aktar","year":"2023","unstructured":"Aktar S, Yasin Nur A (2023) Towards DDoS attack detection using deep learning approach. Computers Secur 129:103251","journal-title":"Computers Secur"},{"key":"1126_CR2","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.future.2017.08.043","volume":"82","author":"AA Diro","year":"2018","unstructured":"Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for internet of things. Future Generation Comput Syst 82:761\u2013768","journal-title":"Future Generation Comput Syst"},{"issue":"1","key":"1126_CR3","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s44196-025-00767-x","volume":"18","author":"R Manivannan","year":"2025","unstructured":"Manivannan R, Senthilkumar S (2025) Intrusion detection system for network security using novel adaptive recurrent neural network-Based Fox optimizer concept. Int J Comput Intell Syst 18(1):37","journal-title":"Int J Comput Intell Syst"},{"key":"1126_CR4","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.aej.2025.02.070","volume":"120","author":"R Aboalela","year":"2025","unstructured":"Aboalela R et al (2025) Harnessing feature pruning with optimal deep learning-based distributed denial of service cyberattack detection on IoT environment. Alexandria Eng J 120:584\u2013597","journal-title":"Alexandria Eng J"},{"key":"1126_CR5","doi-asserted-by":"crossref","unstructured":"Al Mazroa A et al (2025) Boosting cyberattack detection using binary metaheuristics with deep learning on Cyber-Physical system environment. IEEE Access","DOI":"10.1109\/ACCESS.2025.3526258"},{"issue":"1","key":"1126_CR6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s40537-023-00870-w","volume":"11","author":"MH Behiry","year":"2024","unstructured":"Behiry MH, Aly M (2024) Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods. J Big Data 11(1):16","journal-title":"J Big Data"},{"issue":"4","key":"1126_CR7","doi-asserted-by":"crossref","first-page":"e4899","DOI":"10.1002\/ett.4899","volume":"35","author":"T Vaiyapuri","year":"2024","unstructured":"Vaiyapuri T et al (2024) Automated cyberattack detection using optimal ensemble deep learning model. Trans Emerg Telecommunications Technol 35(4):e4899","journal-title":"Trans Emerg Telecommunications Technol"},{"key":"1126_CR8","doi-asserted-by":"crossref","unstructured":"Duraibi S, Alashjaee AM (2024) Enhancing cyberattack detection using dimensionality reduction with hybrid deep learning on internet of things environment. IEEE Access","DOI":"10.1109\/ACCESS.2024.3411612"},{"key":"1126_CR9","doi-asserted-by":"crossref","first-page":"100381","DOI":"10.1016\/j.egyai.2024.100381","volume":"17","author":"YM Khaw","year":"2024","unstructured":"Khaw YM et al (2024) Evasive attacks against autoencoder-based cyberattack detection systems in power systems. Energy AI 17:100381","journal-title":"Energy AI"},{"key":"1126_CR10","first-page":"200462","volume":"25","author":"LKG Danquah","year":"2025","unstructured":"Danquah LKG et al (2025) Computationally efficient deep federated learning with optimized feature selection for IoT botnet attack detection. Intell Syst Appl 25:200462","journal-title":"Intell Syst Appl"},{"key":"1126_CR11","first-page":"111434","volume":"155","author":"YK Saheed","year":"2024","unstructured":"Saheed YK, Abdulganiyu OH, Ait T, Tchakoucht (2024) Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities. Appl Soft Comput 155:111434","journal-title":"Appl SoftComput"},{"key":"1126_CR12","doi-asserted-by":"crossref","first-page":"81118","DOI":"10.1109\/ACCESS.2025.3566980","volume":"13","author":"YK Saheed","year":"2025","unstructured":"Saheed YK, Chukwuere JE (2025) CPS-IIoT-P2Attention: explainable Privacy-Preserving with scaled Dot-Product attention in Cyber-Physical System-Industrial IoT network. IEEE Access 13:81118\u201381142","journal-title":"IEEE Access"},{"key":"1126_CR13","first-page":"115939","volume-title":"CPS-IoT-PPDNN: A new explainable privacy preserving DNN for resilient anomaly detection in Cyber-Physical Systems-enabled IoT networks","author":"YK Saheed","year":"2025","unstructured":"Saheed YK, Misra S (2025) CPS-IoT-PPDNN: A new explainable privacy preserving DNN for resilient anomaly detection in Cyber-Physical Systems-enabled IoT networks, vol 191. Solitons & Fractals, Chaos, p 115939"},{"key":"1126_CR14","doi-asserted-by":"crossref","unstructured":"Mohandas R et al (2024) Enhanced cyberattack detection in wireless sensor networks using gradient boosting decision tree. In: 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC)","DOI":"10.1109\/ICMNWC63764.2024.10872135"},{"key":"1126_CR15","doi-asserted-by":"crossref","unstructured":"Abdullahi M et al (2024) Comparison and investigation of AI-based approaches for cyberattack detection in cyber-physical systems. IEEE Access 12:31988\u201332004","DOI":"10.1109\/ACCESS.2024.3370436"},{"issue":"1","key":"1126_CR16","doi-asserted-by":"crossref","first-page":"e497","DOI":"10.1002\/spy2.497","volume":"8","author":"R Ji","year":"2025","unstructured":"Ji R et al (2025) Cascading bagging and boosting ensemble methods for intrusion detection in Cyber-Physical systems. Secur Priv 8(1):e497","journal-title":"Secur Priv"},{"key":"1126_CR17","doi-asserted-by":"crossref","unstructured":"Ji R, Kumar N, Padha D (2024) CNN-GWO-voting & hybrid: ensemble learning inspired intrusion detection approaches for cyber-physical systems. Proceedings of the Indian National Science Academy","DOI":"10.1007\/s43538-024-00372-0"},{"issue":"4","key":"1126_CR18","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.1007\/s10115-024-02322-0","volume":"67","author":"M Maazalahi","year":"2025","unstructured":"Maazalahi M, Hosseini S (2025) Machine learning and metaheuristic optimization algorithms for feature selection and botnet attack detection. Knowl Inf Syst 67(4):3549\u20133597","journal-title":"Knowl Inf Syst"},{"issue":"1","key":"1126_CR19","doi-asserted-by":"crossref","first-page":"22887","DOI":"10.1038\/s41598-025-05545-5","volume":"15","author":"M Karthikeyan","year":"2025","unstructured":"Karthikeyan M et al (2025) Integration of metaheuristic based feature selection with ensemble representation learning models for privacy aware cyberattack detection in IoT environments. Sci Rep 15(1):22887","journal-title":"Sci Rep"},{"issue":"1","key":"1126_CR20","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1038\/s41598-024-79230-4","volume":"15","author":"EI Elsedimy","year":"2025","unstructured":"Elsedimy EI, AboHashish SMM (2025) An intelligent hybrid approach combining fuzzy C-means and the sperm Whale algorithm for cyber attack detection in IoT networks. Sci Rep 15(1):1005","journal-title":"Sci Rep"},{"issue":"30","key":"1126_CR21","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.17485\/IJST\/v17i30.1794","volume":"17","author":"R Ji","year":"2024","unstructured":"Ji R, Kumar N, Padha D (2024) Hybrid enhanced intrusion detection frameworks for cyber-physical systems via optimal features selection. Indian J Sci Technol 17(30):3069\u20133079","journal-title":"Indian J Sci Technol"},{"issue":"9","key":"1126_CR22","doi-asserted-by":"crossref","first-page":"e5029","DOI":"10.1002\/ett.5029","volume":"35","author":"R Ji","year":"2024","unstructured":"Ji R et al (2024) Review of intrusion detection system in cyber-physical system based networks: Characteristics, industrial protocols, attacks, data sets and challenges. Trans Emerg Telecommunications Technol 35(9):e5029","journal-title":"Trans Emerg Telecommunications Technol"},{"issue":"3","key":"1126_CR23","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1007\/s10207-023-00803-x","volume":"23","author":"YK Saheed","year":"2024","unstructured":"Saheed YK, Misra S (2024) A voting Gray Wolf optimizer-based ensemble learning models for intrusion detection in the internet of things. Int J Inf Secur 23(3):1557\u20131581","journal-title":"Int J Inf Secur"},{"issue":"5","key":"1126_CR24","doi-asserted-by":"crossref","first-page":"e2247","DOI":"10.1002\/nem.2247","volume":"34","author":"A Alqahtani","year":"2024","unstructured":"Alqahtani A, Khan SB (2024) An optimal hybrid cascade regional convolutional network for cyberattack detection. Int J Network Manage 34(5):e2247","journal-title":"Int J Network Manage"},{"key":"1126_CR25","doi-asserted-by":"crossref","first-page":"109848","DOI":"10.1016\/j.ijepes.2024.109848","volume":"157","author":"J Wang","year":"2024","unstructured":"Wang J et al (2024) Cyberattack detection for electricity theft in smart grids via stacking ensemble GRU optimization algorithm using federated learning framework. Int J Electr Power Energy Syst 157:109848","journal-title":"Int J Electr Power Energy Syst"},{"key":"1126_CR26","doi-asserted-by":"crossref","first-page":"104964","DOI":"10.1016\/j.jpdc.2024.104964","volume":"193","author":"B Guembe","year":"2024","unstructured":"Guembe B, Misra S, Azeta A (2024) Federated bayesian optimization XGBoost model for cyberattack detection in internet of medical things. J Parallel Distrib Comput 193:104964","journal-title":"J Parallel Distrib Comput"},{"issue":"1","key":"1126_CR27","doi-asserted-by":"crossref","first-page":"29285","DOI":"10.1038\/s41598-024-79632-4","volume":"14","author":"FK Karim","year":"2024","unstructured":"Karim FK et al (2024) Modeling of bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment. Sci Rep 14(1):29285","journal-title":"Sci Rep"},{"key":"1126_CR28","first-page":"316","volume":"5","author":"WF Urmi","year":"2024","unstructured":"Urmi WF et al (2024) A stacked ensemble approach to detect cyber attacks based on feature selection techniques. Int J Cogn Comput Eng 5:316\u2013331","journal-title":"Int J Cogn Comput Eng"},{"issue":"2","key":"1126_CR29","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1007\/s10586-022-03686-0","volume":"26","author":"OA Alzubi","year":"2023","unstructured":"Alzubi OA, Qiqieh I, Alzubi JA (2023) Fusion of deep learning based cyberattack detection and classification model for intelligent systems. Cluster Comput 26(2):1363\u20131374","journal-title":"Cluster Comput"},{"key":"1126_CR30","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.ins.2023.03.052","volume":"634","author":"W Ding","year":"2023","unstructured":"Ding W, Abdel-Basset M,Mohamed R (2023) DeepAK-IoT: an effective deep learning model for cyberattack detection in IoT networks. Inf Sci 634:157\u2013171","journal-title":"Inf Sci"},{"key":"1126_CR31","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1016\/j.procs.2023.01.153","volume":"218","author":"R Golchha","year":"2023","unstructured":"Golchha R, Joshi A, Gupta GP (2023) Voting-based ensemble learning approach for cyber attacks detection in industrial internet of things. Procedia Comput Sci 218:1752\u20131759","journal-title":"Procedia Comput Sci"},{"key":"1126_CR32","doi-asserted-by":"crossref","unstructured":"Maaz M et al (2024) Empowering IoT resilience: hybrid deep learning techniques for enhanced security. IEEE Access 12:180597\u2013180618","DOI":"10.1109\/ACCESS.2024.3482005"},{"key":"1126_CR33","doi-asserted-by":"crossref","first-page":"100936","DOI":"10.1016\/j.iot.2023.100936","volume":"24","author":"SA Bakhsh","year":"2023","unstructured":"Bakhsh SA et al (2023) Enhancing IoT network security through deep learning-powered intrusion detection system. Internet Things 24:100936","journal-title":"Internet Things"},{"issue":"4","key":"1126_CR34","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.3390\/app13042479","volume":"13","author":"JB Awotunde","year":"2023","unstructured":"Awotunde JB et al (2023) An ensemble tree-based model for intrusion detection in industrial internet of things networks. Appl Sci 13(4):2479","journal-title":"Appl Sci"},{"key":"1126_CR35","doi-asserted-by":"crossref","first-page":"103300","DOI":"10.1016\/j.engappai.2019.103300","volume":"87","author":"W Zhao","year":"2020","unstructured":"Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300","journal-title":"Eng Appl Artif Intell"},{"key":"1126_CR36","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1016\/j.jclepro.2018.08.207","volume":"203","author":"MW Ahmad","year":"2018","unstructured":"Ahmad MW, Reynolds J, Rezgui Y (2018) Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees. J Clean Prod 203:810\u2013821","journal-title":"J Clean Prod"},{"key":"1126_CR37","doi-asserted-by":"crossref","unstructured":"Jafari S, Byun Y-C (2024) Efficient state of charge Estimation in electric vehicles batteries based on the extra tree regressor: a data-driven approach. Heliyon 10(4):e25949","DOI":"10.1016\/j.heliyon.2024.e25949"},{"key":"1126_CR38","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1023\/A:1022648800760","volume":"5","author":"RE Schapire","year":"1990","unstructured":"Schapire RE (1990) The strength of weak learnability. Mach Learn 5:197\u2013227","journal-title":"Mach Learn"},{"key":"1126_CR39","doi-asserted-by":"crossref","unstructured":"Schapire RE (2013) Explaining adaboost. Empirical inference: festschrift in honor of Vladimir N. Vapnik. Springer, pp 37\u201352","DOI":"10.1007\/978-3-642-41136-6_5"},{"key":"1126_CR40","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","volume":"7","author":"A Natekin","year":"2013","unstructured":"Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobotics 7:21","journal-title":"Front Neurorobotics"},{"issue":"1","key":"1126_CR41","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock JT, Khoshgoftaar TM (2020) CatBoost for big data: an interdisciplinary review. J Big Data 7(1):94","journal-title":"J Big Data"},{"key":"1126_CR42","doi-asserted-by":"crossref","first-page":"105626","DOI":"10.1016\/j.engappai.2022.105626","volume":"118","author":"A Dezhkam","year":"2023","unstructured":"Dezhkam A, Manzuri MT (2023) Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert\u2013Huang\u200b transform. Eng Appl Artif Intell 118:105626","journal-title":"Eng Appl Artif Intell"},{"key":"1126_CR43","doi-asserted-by":"crossref","first-page":"104542","DOI":"10.1016\/j.cose.2025.104542","volume":"157","author":"S Wali","year":"2025","unstructured":"Wali S, Farrukh YA, Khan I (2025) Explainable AI and random forest based reliable intrusion detection system. Computers Secur 157:104542","journal-title":"Computers Secur"},{"key":"1126_CR44","doi-asserted-by":"crossref","unstructured":"Bisong E (2019) Introduction to Scikit-learn. Building machine learning and deep learning models on Google cloud platform: a comprehensive guide for beginners. Springer, pp 215\u2013229","DOI":"10.1007\/978-1-4842-4470-8_18"},{"issue":"4","key":"1126_CR45","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s10586-024-04898-2","volume":"28","author":"A Babaei Goushlavandani","year":"2025","unstructured":"Babaei Goushlavandani A, Bayat P, Ekbatanifard G (2025) Detecting attacks on the internet of things network in the computing fog layer with an embedded learning approach based on clustering and blockchain. Cluster Comput 28(4):226","journal-title":"Cluster Comput"},{"issue":"5","key":"1126_CR46","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.3390\/s25051382","volume":"25","author":"B Li","year":"2025","unstructured":"Li B, Li J, Jia M (2025) ADFCNN-BiLSTM: A deep neural network based on attention and deformable Convolution for network intrusion detection. Sensors 25(5):1382","journal-title":"Sensors"},{"issue":"4","key":"1126_CR47","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s10586-024-04904-7","volume":"28","author":"K Kharoubi","year":"2025","unstructured":"Kharoubi K et al (2025) Network intrusion detection system using convolutional neural networks: NIDS-DL-CNN for IoT security. Cluster Comput 28(4):219","journal-title":"Cluster Comput"},{"key":"1126_CR48","unstructured":"https:\/\/research.unsw.edu.au\/projects\/unsw-nb15-dataset"},{"key":"1126_CR49","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1016\/j.procs.2020.03.367","volume":"167","author":"S Choudhary","year":"2020","unstructured":"Choudhary S, Kesswani N (2020) Analysis of KDD-Cup\u201999, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT. Procedia Comput Sci 167:1561\u20131573","journal-title":"Procedia Comput Sci"},{"issue":"1","key":"1126_CR50","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1186\/s40537-020-00379-6","volume":"7","author":"SM Kasongo","year":"2020","unstructured":"Kasongo SM, Sun Y (2020) Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset. J Big Data 7(1):105","journal-title":"J Big Data"},{"key":"1126_CR51","first-page":"q70p","volume":"10","author":"H Kang","year":"2019","unstructured":"Kang H et al (2019) IoT network intrusion dataset. IEEE Dataport 10:q70p\u2013q449","journal-title":"IEEEDataport"},{"issue":"1","key":"1126_CR52","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/s42400-023-00178-5","volume":"6","author":"BS Sharmila","year":"2023","unstructured":"Sharmila BS, Nagapadma R (2023) Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset. Cybersecurity 6(1):41","journal-title":"Cybersecurity"},{"key":"1126_CR53","doi-asserted-by":"crossref","first-page":"4471","DOI":"10.1109\/ACCESS.2024.3349950","volume":"12","author":"S Saha","year":"2024","unstructured":"Saha S et al (2024) Churnnet: deep learning enhanced customer churn prediction in telecommunication industry. IEEE Access 12:4471\u20134484","journal-title":"IEEE Access"},{"key":"1126_CR54","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.neucom.2014.05.096","volume":"172","author":"L Bao","year":"2016","unstructured":"Bao L et al (2016) Boosted near-miss under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets. Neurocomputing 172:198\u2013206","journal-title":"Neurocomputing"},{"key":"1126_CR55","doi-asserted-by":"crossref","first-page":"117130","DOI":"10.1016\/j.eswa.2022.117130","volume":"201","author":"A Tanimoto","year":"2022","unstructured":"Tanimoto A et al (2022) Improving imbalanced classification using near-miss instances. Expert Syst Appl 201:117130","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1126_CR56","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1080\/1540496X.2020.1825935","volume":"58","author":"L Yu","year":"2022","unstructured":"Yu L et al (2022) Missing data preprocessing in credit classification: One-hot encoding or imputation? Emerg Markets Finance Trade 58(2):472\u2013482","journal-title":"Emerg Markets Finance Trade"},{"issue":"7","key":"1126_CR57","doi-asserted-by":"crossref","first-page":"6429","DOI":"10.1109\/JIOT.2020.2985082","volume":"7","author":"X Zhou","year":"2020","unstructured":"Zhou X et al (2020) Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet Things J 7(7):6429\u20136438","journal-title":"IEEE Internet Things J"},{"key":"1126_CR58","doi-asserted-by":"crossref","first-page":"100342","DOI":"10.1016\/j.rico.2023.100342","volume":"14","author":"SK Wagh","year":"2024","unstructured":"Wagh SK et al (2024) Customer churn prediction in Telecom sector using machine learning techniques. Results Control Optim 14:100342","journal-title":"Results Control Optim"},{"key":"1126_CR59","doi-asserted-by":"crossref","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":"1126_CR60","doi-asserted-by":"crossref","first-page":"105082","DOI":"10.1016\/j.engappai.2022.105082","volume":"114","author":"L Wang","year":"2022","unstructured":"Wang L et al (2022) Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 114:105082","journal-title":"Eng Appl Artif Intell"},{"issue":"5","key":"1126_CR61","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1109\/TEVC.2009.2014613","volume":"13","author":"J Zhang","year":"2009","unstructured":"Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945\u2013958","journal-title":"IEEE Trans Evol Comput"},{"key":"1126_CR62","doi-asserted-by":"crossref","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. Future Generation Comput Syst 97:849\u2013872","journal-title":"Future Generation Comput Syst"},{"issue":"4","key":"1126_CR63","doi-asserted-by":"crossref","first-page":"246","DOI":"10.3390\/drones9040246","volume":"9","author":"GM Nayeem","year":"2025","unstructured":"Nayeem GM, Fan M, Daiyan GM (2025) Adaptive Q-Learning grey Wolf optimizer for UAV path planning. Drones 9(4):246","journal-title":"Drones"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-025-01126-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-025-01126-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-025-01126-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:08:12Z","timestamp":1773655692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-025-01126-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,8]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1126"],"URL":"https:\/\/doi.org\/10.1007\/s12065-025-01126-7","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,8]]},"assertion":[{"value":"19 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The submitted work is original, and the manuscript has not been submitted to another journal for simultaneous consideration.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"The authors declare that they consent to publish the article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"18"}}