{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T02:18:33Z","timestamp":1769048313189,"version":"3.49.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09233-1","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T08:02:32Z","timestamp":1701676952000},"page":"3479-3498","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Three intelligent computational models to predict the high-performance concrete mixture"],"prefix":"10.1007","volume":"36","author":[{"given":"Hossein","family":"Moayedi","sequence":"first","affiliation":[]},{"given":"Loke Kok","family":"Foong","sequence":"additional","affiliation":[]},{"given":"Binh Nguyen","family":"Le","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"9233_CR1","doi-asserted-by":"publisher","unstructured":"Chen S, Zhang H, Zykova KI, Gholizadeh Touchaei H, Yuan C, Moayedi H, Le BN (2023) Computational intelligence models for predicting the frictional resistance of driven pile foundations in cold regions. Comput Concr 32(2):217\u2013232. https:\/\/doi.org\/10.12989\/cac.2023.32.2.217","DOI":"10.12989\/cac.2023.32.2.217"},{"key":"9233_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.soildyn.2023.107988","volume":"172","author":"J Zhang","year":"2023","unstructured":"Zhang J, Zhang C (2023) Using viscoelastic materials to mitigate earthquake-induced pounding between adjacent frames with unequal height considering soil-structure interactions. Soil Dyn Earthq Eng 172:107988. https:\/\/doi.org\/10.1016\/j.soildyn.2023.107988","journal-title":"Soil Dyn Earthq Eng"},{"key":"9233_CR3","volume":"62","author":"H Huang","year":"2022","unstructured":"Huang H et al (2022) Theoretical analysis on the lateral drift of precast concrete frame with replaceable artificial controllable plastic hinges. J Build Eng 62:105386","journal-title":"J Build Eng"},{"issue":"5","key":"9233_CR4","first-page":"711","volume":"28","author":"X Geng","year":"2021","unstructured":"Geng X et al (2021) Predicting the concrete compressive strength through MLP network hybridized with three evolutionary algorithms. Smart Struct Syst 28(5):711","journal-title":"Smart Struct Syst"},{"issue":"11","key":"9233_CR5","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1016\/j.eng.2020.10.016","volume":"7","author":"S Zhou","year":"2021","unstructured":"Zhou S et al (2021) Preparation and characterization of high-strength geopolymer based on BH-1 lunar soil simulant with low alkali content. Engineering 7(11):1631\u20131645","journal-title":"Engineering"},{"issue":"4","key":"9233_CR6","first-page":"295","volume":"24","author":"IS Alkroosh","year":"2019","unstructured":"Alkroosh IS, Sarker PK (2019) Prediction of the compressive strength of fly ash geopolymer concrete using gene expression programming. Comput Concr Int J 24(4):295\u2013302","journal-title":"Comput Concr Int J"},{"key":"9233_CR7","doi-asserted-by":"crossref","unstructured":"Almeida Neto MA, Fagundes RAA, Bastos-Filho CJ (2018) Optimizing support vector regression with swarm intelligence for estimating the concrete compression strength. In: International conference on machine learning and data mining in pattern recognition, Springer.","DOI":"10.1007\/978-3-319-96136-1_11"},{"issue":"2","key":"9233_CR8","doi-asserted-by":"publisher","first-page":"179","DOI":"10.12989\/scs.2023.48.2.179","volume":"48","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Gor M, Voronkova DK, Gholizadeh Touchaei H, Moayedi H, Le BN (2023) An optimized ANFIS model for predicting pile pullout resistance. Steel Compos Struct 48(2):179\u2013190. https:\/\/doi.org\/10.12989\/scs.2023.48.2.179","journal-title":"Steel Compos Struct"},{"issue":"1","key":"9233_CR9","doi-asserted-by":"publisher","first-page":"20230347","DOI":"10.1515\/rams-2023-0347","volume":"62","author":"Y Tang","year":"2023","unstructured":"Tang Y, Wang Y, Wu D, Chen M, Pang L, Sun J, Wang X (2023) Exploring temperature-resilient recycled aggregate concrete with waste rubber: An experimental and multi-objective optimization analysis. Rev Adv Mater Sci 62(1):20230347. https:\/\/doi.org\/10.1515\/rams-2023-0347","journal-title":"Rev Adv Mater Sci"},{"key":"9233_CR10","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2019.107309","volume":"152","author":"A Ashrafian","year":"2020","unstructured":"Ashrafian A et al (2020) An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement. Measurement 152:107309","journal-title":"Measurement"},{"issue":"3","key":"9233_CR11","first-page":"201","volume":"30","author":"A Taleshi","year":"2021","unstructured":"Taleshi AA, Hashemi H (2021) Estimation of seismic attenuation in the Kangan formation from walkaway VSP data. J Seismic Explor 30(3):201\u2013210","journal-title":"J Seismic Explor"},{"key":"9233_CR12","first-page":"867","volume":"44","author":"H Moayedi","year":"2022","unstructured":"Moayedi H et al (2022) Optimized ANNs for predicting compressive strength of high-performance concrete. Steel Compos Struct 44:867\u2013882","journal-title":"Steel Compos Struct"},{"issue":"15","key":"9233_CR13","doi-asserted-by":"crossref","first-page":"11807","DOI":"10.1007\/s00521-019-04663-2","volume":"32","author":"PG Asteris","year":"2020","unstructured":"Asteris PG, Mokos VG (2020) Concrete compressive strength using artificial neural networks. Neural Comput Appl 32(15):11807\u201311826","journal-title":"Neural Comput Appl"},{"issue":"sup1","key":"9233_CR14","doi-asserted-by":"crossref","first-page":"s102","DOI":"10.1080\/19648189.2016.1246693","volume":"20","author":"P Asteris","year":"2016","unstructured":"Asteris P et al (2016) Prediction of self-compacting concrete strength using artificial neural networks. Eur J Environ Civ Eng 20(sup1):s102\u2013s122","journal-title":"Eur J Environ Civ Eng"},{"issue":"6","key":"9233_CR15","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.3390\/s17061344","volume":"17","author":"PG Asteris","year":"2017","unstructured":"Asteris PG, Roussis PC, Douvika MG (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17(6):1344","journal-title":"Sensors"},{"issue":"2","key":"9233_CR16","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/app9020243","volume":"9","author":"PG Asteris","year":"2019","unstructured":"Asteris PG et al (2019) Stochastic vulnerability assessment of masonry structures: concepts, modeling and restoration aspects. Appl Sci 9(2):243","journal-title":"Appl Sci"},{"key":"9233_CR17","doi-asserted-by":"crossref","DOI":"10.1201\/9781482264685","volume-title":"Testing of concrete in structures","author":"JH Bungey","year":"2006","unstructured":"Bungey JH, Grantham MG (2006) Testing of concrete in structures. Crc Press"},{"issue":"8","key":"9233_CR18","first-page":"71","volume":"9","author":"SC Nayak","year":"2017","unstructured":"Nayak SC (2017) Development and performance evaluation of adaptive hybrid higher order neural networks for exchange rate prediction. Int J Intell Syst Appl 9(8):71","journal-title":"Int J Intell Syst Appl"},{"issue":"2\u20134","key":"9233_CR19","first-page":"229","volume":"2","author":"SC Nayak","year":"2016","unstructured":"Nayak SC, Misra BB, Behera HS (2016) Fluctuation prediction of stock market index by adaptive evolutionary higher order neural networks. Int J Swarm Intell 2(2\u20134):229\u2013253","journal-title":"Int J Swarm Intell"},{"key":"9233_CR20","doi-asserted-by":"crossref","first-page":"553","DOI":"10.4018\/978-1-5225-0788-8.ch022","volume-title":"Nature-inspired computing: concepts, methodologies, tools, and applications","author":"SC Nayak","year":"2017","unstructured":"Nayak SC, Misra BB, Behera HS (2017) Adaptive hybrid higher order neural networks for prediction of stock market behavior. Nature-inspired computing: concepts, methodologies, tools, and applications. IGI Global, pp 553\u2013570"},{"key":"9233_CR21","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/978-981-10-8201-6_24","volume-title":"Innovations in computer science and engineering","author":"AK Behera","year":"2019","unstructured":"Behera AK et al (2019) Improving software reliability prediction accuracy using CRO-based FLANN. Innovations in computer science and engineering. Springer, pp 213\u2013220"},{"issue":"2","key":"9233_CR22","first-page":"1","volume":"4","author":"Y Sharifi","year":"2020","unstructured":"Sharifi Y, Hosseinpour M (2020) A predictive model based ANN for compressive strength assessment of the mortars containing metakaolin. J Soft Comput Civ Eng 4(2):1\u201312","journal-title":"J Soft Comput Civ Eng"},{"issue":"2","key":"9233_CR23","first-page":"85","volume":"4","author":"A Priyadarshee","year":"2020","unstructured":"Priyadarshee A et al (2020) Neural Models for Unconfined Compressive Strength of Kaolin clay mixed with pond ash, rice husk ash and cement. J Soft Comput Civ Eng 4(2):85\u2013102","journal-title":"J Soft Comput Civ Eng"},{"issue":"6","key":"9233_CR24","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1016\/j.asoc.2012.01.008","volume":"12","author":"F Fern\u00e1ndez-Navarro","year":"2012","unstructured":"Fern\u00e1ndez-Navarro F et al (2012) Evolutionary generalized radial basis function neural networks for improving prediction accuracy in gene classification using feature selection. Appl Soft Comput 12(6):1787\u20131800","journal-title":"Appl Soft Comput"},{"issue":"1\u20133","key":"9233_CR25","doi-asserted-by":"crossref","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 (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"2","key":"9233_CR26","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2011","unstructured":"Huang GB et al (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513\u2013529","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"},{"key":"9233_CR27","volume":"148","author":"J Li","year":"2022","unstructured":"Li J, Chen M, Li Z (2022) Improved soil\u2013structure interaction model considering time-lag effect. Comput Geotech 148:104835","journal-title":"Comput Geotech"},{"issue":"2","key":"9233_CR28","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1108\/EC-10-2021-0583","volume":"40","author":"M Shi","year":"2023","unstructured":"Shi M, Lv L, Xu L (2023) A multi-fidelity surrogate model based on extreme support vector regression: fusing different fidelity data for engineering design. Eng Comput 40(2):473\u2013493. https:\/\/doi.org\/10.1108\/EC-10-2021-0583","journal-title":"Eng Comput"},{"issue":"1","key":"9233_CR29","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.dss.2008.07.009","volume":"46","author":"Z-L Sun","year":"2008","unstructured":"Sun Z-L et al (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411\u2013419","journal-title":"Decis Support Syst"},{"issue":"1","key":"9233_CR30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40854-020-00177-2","volume":"6","author":"SC Nayak","year":"2020","unstructured":"Nayak SC, Misra BB (2020) Extreme learning with chemical reaction optimization for stock volatility prediction. Financ Innov 6(1):1\u201323","journal-title":"Financ Innov"},{"key":"9233_CR31","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neunet.2013.12.002","volume":"51","author":"A Grigorievskiy","year":"2014","unstructured":"Grigorievskiy A et al (2014) Long-term time series prediction using OP-ELM. Neural Netw 51:50\u201356","journal-title":"Neural Netw"},{"issue":"5","key":"9233_CR32","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s13201-023-01913-6","volume":"13","author":"RM Adnan","year":"2023","unstructured":"Adnan RM et al (2023) Application of novel binary optimized machine learning models for monthly streamflow prediction. Appl Water Sci 13(5):110","journal-title":"Appl Water Sci"},{"key":"9233_CR33","doi-asserted-by":"publisher","first-page":"103607","DOI":"10.1016\/j.addma.2023.103607","volume":"71","author":"C Xiao","year":"2023","unstructured":"Xiao C, Zheng K, Chen S, Li N, Shang X, Wang F, Chen Z (2023) Additive manufacturing of high solid content lunar regolith simulant paste based on vat photopolymerization and the effect of water addition on paste retention properties. Addit Manuf 71:103607. https:\/\/doi.org\/10.1016\/j.addma.2023.103607","journal-title":"Addit Manuf"},{"key":"9233_CR34","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.conbuildmat.2018.06.081","volume":"183","author":"Y Moodi","year":"2018","unstructured":"Moodi Y et al (2018) Using response surface methodology and providing a modified model using whale algorithm for estimating the compressive strength of columns confined with FRP sheets. Constr Build Mater 183:163\u2013170","journal-title":"Constr Build Mater"},{"issue":"1","key":"9233_CR35","doi-asserted-by":"crossref","first-page":"04013019","DOI":"10.1061\/(ASCE)CC.1943-5614.0000407","volume":"18","author":"TM Pham","year":"2014","unstructured":"Pham TM, Hadi MN (2014) Stress prediction model for FRP confined rectangular concrete columns with rounded corners. J Compos Constr 18(1):04013019","journal-title":"J Compos Constr"},{"issue":"21\u201322","key":"9233_CR36","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1177\/0731684419858708","volume":"38","author":"Y Moodi","year":"2019","unstructured":"Moodi Y, Mousavi SR, Sohrabi MR (2019) New models for estimating compressive strength of concrete confined with FRP sheets in circular sections. J Reinf Plast Compos 38(21\u201322):1014\u20131028","journal-title":"J Reinf Plast Compos"},{"issue":"1","key":"9233_CR37","first-page":"19","volume":"5","author":"S Pandey","year":"2021","unstructured":"Pandey S, Kumar V, Kumar P (2021) Application and analysis of machine learning algorithms for design of concrete mix with plasticizer and without plasticizer. J Soft Comput Civ Eng 5(1):19\u201337","journal-title":"J Soft Comput Civ Eng"},{"key":"9233_CR38","doi-asserted-by":"crossref","first-page":"100875","DOI":"10.1016\/j.trgeo.2022.100875","volume":"37","author":"H Moayedi","year":"2022","unstructured":"Moayedi H et al (2022) Applicability and comparison of four nature-inspired hybrid techniques in predicting driven piles\u2019 friction capacity. Transp Geotech 37:100875","journal-title":"Transp Geotech"},{"key":"9233_CR39","volume":"612","author":"L Xu","year":"2022","unstructured":"Xu L et al (2022) An upscaling approach to predict mine water inflow from roof sandstone aquifers. J Hydrol 612:128314","journal-title":"J Hydrol"},{"issue":"3","key":"9233_CR40","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s11204-017-9449-1","volume":"54","author":"H Moayedi","year":"2017","unstructured":"Moayedi H, Mosallanezhad M, Nazir R (2017) Evaluation of maintained load test (MLT) and pile driving analyzer (PDA) in measuring bearing capacity of driven reinforced concrete piles. Soil Mech Found Eng 54(3):150\u2013154","journal-title":"Soil Mech Found Eng"},{"issue":"1","key":"9233_CR41","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1007\/s00366-019-00850-w","volume":"37","author":"D Tien Bui","year":"2021","unstructured":"Tien Bui D et al (2021) Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete. Eng Comput 37(1):701\u2013712","journal-title":"Eng Comput"},{"key":"9233_CR42","first-page":"281","volume":"45","author":"D Wu","year":"2022","unstructured":"Wu D et al (2022) ANN-Incorporated satin bowerbird optimizer for predicting uniaxial compressive strength of concrete. Steel Compos Struct 45:281\u2013291","journal-title":"Steel Compos Struct"},{"issue":"6","key":"9233_CR43","first-page":"753","volume":"26","author":"Y Zhao","year":"2020","unstructured":"Zhao Y et al (2020) Employing TLBO and SCE for optimal prediction of the compressive strength of concrete. Smart Struct Syst 26(6):753\u2013763","journal-title":"Smart Struct Syst"},{"key":"9233_CR44","unstructured":"Dua D, Graff C (2017) UCI machine learning repository"},{"key":"9233_CR45","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-023-30762-8","volume-title":"A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran","author":"Y Shen","year":"2023","unstructured":"Shen Y, Ahmadi Dehrashid A, Bahar RA, Moayedi H, Nasrollahizadeh B (2023) A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran. Environ Sci Pollut Res 1\u201329. https:\/\/doi.org\/10.1007\/s11356-023-30762-8"},{"key":"9233_CR46","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1007\/s00366-019-00798-x","volume":"36","author":"C Yuan","year":"2020","unstructured":"Yuan C, Moayedi H (2020) Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence. Eng Comput 36:1801\u20131811. https:\/\/doi.org\/10.1007\/s00366-019-00798-x","journal-title":"Eng Comput"},{"issue":"12","key":"9233_CR47","first-page":"9053","volume":"35","author":"RMA Ikram","year":"2023","unstructured":"Ikram RMA et al (2023) Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study. Neural Comput Appl 35(12):9053\u20139070","journal-title":"Neural Comput Appl"},{"key":"9233_CR48","volume":"245","author":"H Huang","year":"2021","unstructured":"Huang H et al (2021) Seismic behavior of a replaceable artificial controllable plastic hinge for precast concrete beam-column joint. Eng Struct 245:112848","journal-title":"Eng Struct"},{"key":"9233_CR49","volume":"315","author":"M Wang","year":"2022","unstructured":"Wang M, Yang X, Wang W (2022) Establishing a 3D aggregates database from X-ray CT scans of bulk concrete. Constr Build Mater 315:125740","journal-title":"Constr Build Mater"},{"key":"9233_CR50","volume":"279","author":"SY Zhai","year":"2023","unstructured":"Zhai SY et al (2023) Seismic behavior of an innovative bolted connection with dual-slot hole for modular steel buildings. Eng Struct 279:115619","journal-title":"Eng Struct"},{"issue":"4","key":"9233_CR51","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s43452-022-00526-1","volume":"22","author":"H Huang","year":"2022","unstructured":"Huang H et al (2022) Seismic behavior of a friction-type artificial plastic hinge for the precast beam\u2013column connection. Arch Civ Mech Eng 22(4):201","journal-title":"Arch Civ Mech Eng"},{"key":"9233_CR52","volume":"43","author":"H Huang","year":"2021","unstructured":"Huang H et al (2021) Progressive collapse resistance of multistory RC frame strengthened with HPFL-BSP. J Build Eng 43:103123","journal-title":"J Build Eng"},{"issue":"10","key":"9233_CR53","doi-asserted-by":"crossref","DOI":"10.1016\/j.asej.2023.102168","volume":"14","author":"RM Adnan","year":"2023","unstructured":"Adnan RM et al (2023) Improving drought modeling based on new heuristic machine learning methods. Ain Shams Eng J 14(10):102168","journal-title":"Ain Shams Eng J"},{"issue":"3","key":"9233_CR54","first-page":"389","volume":"45","author":"M Abedini","year":"2022","unstructured":"Abedini M, Zhang C (2022) Residual capacity assessment of post-damaged RC columns exposed to high strain rate loading. Steel Compos Struct 45(3):389\u2013408","journal-title":"Steel Compos Struct"},{"key":"9233_CR55","doi-asserted-by":"publisher","unstructured":"Sun Y, Dai HL, Moayedi H, Le BN, Adnan RM (2024) Predicting steady-state biogas production from waste using advanced machine learning-metaheuristic approaches. Fuel 355:129493. https:\/\/doi.org\/10.1016\/j.fuel.2023.129493","DOI":"10.1016\/j.fuel.2023.129493"},{"key":"9233_CR56","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.asoc.2015.01.050","volume":"30","author":"A Sadollah","year":"2015","unstructured":"Sadollah A et al (2015) Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput 30:58\u201371","journal-title":"Appl Soft Comput"},{"key":"9233_CR57","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.compstruc.2012.07.010","volume":"110","author":"H Eskandar","year":"2012","unstructured":"Eskandar H et al (2012) Water cycle algorithm\u2013a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151\u2013166","journal-title":"Comput Struct"},{"issue":"1","key":"9233_CR58","first-page":"2192258","volume":"17","author":"RM Adnan","year":"2023","unstructured":"Adnan RM et al (2023) Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data. Eng Appl Comput Fluid Mech 17(1):2192258","journal-title":"Eng Appl Comput Fluid Mech"},{"key":"9233_CR59","doi-asserted-by":"publisher","unstructured":"Sun Y, Dai HL, Xu L, Asaditaleshi A, Ahmadi Dehrashid A, Adnan Ikram RM, Moayedi H, Ahmadi Dehrashid H, Thi QT (2023) Development of the artificial neural network\u2019s swarm-based approaches predicting East Azerbaijan landslide susceptibility mapping. Environ Dev Sustain 1\u201338. https:\/\/doi.org\/10.1007\/s10668-023-04117-9","DOI":"10.1007\/s10668-023-04117-9"},{"key":"9233_CR60","doi-asserted-by":"crossref","unstructured":"Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Preprint arXiv:1005.2908","DOI":"10.1504\/IJMMNO.2010.035430"},{"issue":"8","key":"9233_CR61","doi-asserted-by":"crossref","first-page":"5508","DOI":"10.1016\/j.asoc.2011.05.008","volume":"11","author":"R Rajabioun","year":"2011","unstructured":"Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508\u20135518","journal-title":"Appl Soft Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09233-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09233-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09233-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T12:08:39Z","timestamp":1707480519000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09233-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":61,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9233"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09233-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,4]]},"assertion":[{"value":"15 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}