{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:11:12Z","timestamp":1763017872141,"version":"3.37.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,5,16]],"date-time":"2018-05-16T00:00:00Z","timestamp":1526428800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61573306"],"award-info":[{"award-number":["61573306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61403331"],"award-info":[{"award-number":["61403331"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"crossref","award":["F2016203427"],"award-info":[{"award-number":["F2016203427"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2019,4]]},"DOI":"10.1007\/s11063-018-9854-0","type":"journal-article","created":{"date-parts":[[2018,5,16]],"date-time":"2018-05-16T12:16:54Z","timestamp":1526473014000},"page":"737-759","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency"],"prefix":"10.1007","volume":"49","author":[{"given":"Peifeng","family":"Niu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4981-5302","authenticated-orcid":false,"given":"Jinbai","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lingfang","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Xianchen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Rongyan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Guoqiang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,5,16]]},"reference":[{"issue":"1","key":"9854_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.joei.2014.06.007","volume":"88","author":"Y Tunckaya","year":"2015","unstructured":"Tunckaya Y, Koklukaya E (2015) Comparative prediction analysis of 600\u00a0MWe coal-fired power plant production rate using statistical and neural-based models. J Energy Inst 88(1):11\u201318","journal-title":"J Energy Inst"},{"key":"9854_CR2","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neunet.2013.12.006","volume":"51","author":"G Li","year":"2014","unstructured":"Li G, Niu P, Wang H, Liu Y (2014) Least Square Fast Learning Network for modeling the combustion efficiency of a 300\u00a0WM coal-fired boiler. Neural Netw 51:57\u201366","journal-title":"Neural Netw"},{"issue":"2","key":"9854_CR3","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.joei.2014.07.003","volume":"88","author":"Y Tunckaya","year":"2015","unstructured":"Tunckaya Y, Koklukaya E (2015) Comparative analysis and prediction study for effluent gas emissions in a coal-fired thermal power plant using artificial intelligence and statistical tools. J Energy Inst 88(2):118\u2013125","journal-title":"J Energy Inst"},{"key":"9854_CR4","first-page":"1","volume":"3","author":"X Li","year":"2017","unstructured":"Li X, Niu P, Li G, Liu J (2017) An adaptive extreme learning machine for modeling NOx emission of a 300\u00a0MW circulating fluidized bed boiler. Neural Process Lett 3:1\u201320","journal-title":"Neural Process Lett"},{"key":"9854_CR5","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.measurement.2016.06.015","volume":"92","author":"B Liu","year":"2016","unstructured":"Liu B, Hu J, Yan F, Turkson RF, Lin F (2016) A novel optimal support vector machine ensemble model for NOx emissions prediction of a diesel engine. Measurement 92:183\u2013192","journal-title":"Measurement"},{"key":"9854_CR6","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.egypro.2016.10.164","volume":"100","author":"R Suntivarakorn","year":"2016","unstructured":"Suntivarakorn R, Treedet W (2016) Improvement of boiler\u2019s efficiency using heat recovery and automatic combustion control system. Energy Proc 100:193\u2013197","journal-title":"Energy Proc"},{"issue":"3","key":"9854_CR7","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s11063-016-9496-z","volume":"44","author":"P Niu","year":"2016","unstructured":"Niu P, Ma Y, Li M, Yan S, Li G (2016) A kind of parameters self-adjusting extreme learning machine. Neural Process Lett 44(3):813\u2013830","journal-title":"Neural Process Lett"},{"key":"9854_CR8","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.chemolab.2013.04.012","volume":"126","author":"G Li","year":"2013","unstructured":"Li G, Niu P, Zhang W, Liu Y (2013) Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching\u2013learning-based optimization. Chemom Intell Lab Syst 126:11\u201320","journal-title":"Chemom Intell Lab Syst"},{"issue":"7\u20138","key":"9854_CR9","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1007\/s00521-013-1398-7","volume":"24","author":"G Li","year":"2014","unstructured":"Li G, Niu P, Duan X, Zhang X (2014) Fast learning network: a novel artificial neural network with a fast learning speed. Neural Comput Appl 24(7\u20138):1683\u20131695","journal-title":"Neural Comput Appl"},{"issue":"1","key":"9854_CR10","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"key":"9854_CR11","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.knosys.2016.11.011","volume":"118","author":"P Niu","year":"2017","unstructured":"Niu P, Chen K, Ma Y et al (2017) Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm. Knowl-Based Syst 118:80\u201392","journal-title":"Knowl-Based Syst"},{"key":"9854_CR12","doi-asserted-by":"crossref","unstructured":"Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science. MHS\u201995. IEEE, pp 39\u201343","DOI":"10.1109\/MHS.1995.494215"},{"key":"9854_CR13","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1007\/978-0-387-30164-8_630","volume-title":"Encyclopedia of machine learning","author":"J Kennedy","year":"2011","unstructured":"Kennedy J (2011) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp 760\u2013766"},{"issue":"4","key":"9854_CR14","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2013a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341\u2013359","journal-title":"J Global Optim"},{"issue":"7","key":"9854_CR15","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1109\/TCYB.2013.2279211","volume":"44","author":"WJ Yu","year":"2014","unstructured":"Yu WJ, Shen M, Chen WN et al (2014) Differential evolution with two-level parameter adaptation. IEEE Trans Cybern 44(7):1080\u20131099","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"9854_CR16","doi-asserted-by":"publisher","first-page":"2745","DOI":"10.1109\/TAP.2013.2238654","volume":"61","author":"Z Bayraktar","year":"2013","unstructured":"Bayraktar Z, Komurcu M, Bossard JA, Werner DH (2013) The wind driven optimization technique and its application in electromagnetics. IEEE Trans Antennas Propag 61(5):2745\u20132757","journal-title":"IEEE Trans Antennas Propag"},{"key":"9854_CR17","doi-asserted-by":"crossref","unstructured":"Yang XS (2012) Flower pollination algorithm for global optimization. In: UCNC. pp 240\u2013249","DOI":"10.1007\/978-3-642-32894-7_27"},{"issue":"9","key":"9854_CR18","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1080\/0305215X.2013.832237","volume":"46","author":"XS Yang","year":"2014","unstructured":"Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222\u20131237","journal-title":"Eng Optim"},{"issue":"04","key":"9854_CR19","doi-asserted-by":"publisher","first-page":"1659010","DOI":"10.1142\/S0218001416590102","volume":"30","author":"Y Zhou","year":"2016","unstructured":"Zhou Y, Wang R (2016) An improved flower pollination algorithm for optimal unmanned undersea vehicle path planning problem. Int J Pattern Recognit Artif Intell 30(04):1659010","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"9854_CR20","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.patrec.2016.03.014","volume":"77","author":"SAF Sayed","year":"2016","unstructured":"Sayed SAF, Nabil E, Badr A (2016) A binary clonal flower pollination algorithm for feature selection. Pattern Recognit Lett 77:21\u201327","journal-title":"Pattern Recognit Lett"},{"issue":"1","key":"9854_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ipl.2015.08.007","volume":"116","author":"R Wang","year":"2016","unstructured":"Wang R, Zhou Y, Qiao S, Huang K (2016) Flower pollination algorithm with bee pollinator for cluster analysis. Inf Proc Lett 116(1):1\u201314","journal-title":"Inf Proc Lett"},{"key":"9854_CR22","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.enconman.2017.04.042","volume":"144","author":"S Xu","year":"2017","unstructured":"Xu S, Wang Y (2017) Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Convers Manag 144:53\u201368","journal-title":"Energy Convers Manag"},{"key":"9854_CR23","doi-asserted-by":"crossref","unstructured":"Ludwig SA (2012) Clonal selection based genetic algorithm for workflow service selection. In: IEEE congress on evolutionary computation (CEC) 2012. IEEE, pp 1\u20137","DOI":"10.1109\/CEC.2012.6256465"},{"key":"9854_CR24","doi-asserted-by":"crossref","unstructured":"Sarangi SK, Panda R, Priyadarshini S, Sarangi A (2016) A new modified firefly algorithm for function optimization. In: International conference on electrical, electronics, and optimization techniques (ICEEOT). IEEE, pp 2944\u20132949","DOI":"10.1109\/ICEEOT.2016.7755239"},{"issue":"2","key":"9854_CR25","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.dss.2008.01.002","volume":"45","author":"ML Wong","year":"2008","unstructured":"Wong ML, Guo YY (2008) Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm. Decis Support Syst 45(2):368\u2013383","journal-title":"Decis Support Syst"},{"key":"9854_CR26","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.asoc.2015.10.070","volume":"48","author":"WF Gao","year":"2016","unstructured":"Gao WF, Huang LL, Wang J, Liu SY, Qin CD (2016) Enhanced artificial bee colony algorithm through differential evolution. Appl Soft Comput 48:137\u2013150","journal-title":"Appl Soft Comput"},{"issue":"19","key":"9854_CR27","doi-asserted-by":"publisher","first-page":"8036","DOI":"10.1016\/j.ijleo.2016.06.002","volume":"127","author":"HT Liang","year":"2016","unstructured":"Liang HT, Kang FH (2016) Adaptive mutation particle swarm algorithm with dynamic nonlinear changed inertia weight. Optik-Int J Light Electron Opt 127(19):8036\u20138042","journal-title":"Optik-Int J Light Electron Opt"},{"key":"9854_CR28","doi-asserted-by":"crossref","unstructured":"Aslani H, Yaghoobi M, Akbarzadeh-T MR (2015) Chaotic inertia weight in black hole algorithm for function optimization. In: International congress on technology, communication and knowledge (ICTCK) 2015. IEEE, pp 123\u2013129","DOI":"10.1109\/ICTCK.2015.7582657"},{"issue":"7","key":"9854_CR29","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1049\/iet-gtd.2014.0965","volume":"9","author":"NC Yang","year":"2015","unstructured":"Yang NC, Le MD (2015) Multi-objective bat algorithm with time-varying inertia weights for optimal design of passive power filters set. IET Gener Transm Distrib 9(7):644\u2013654","journal-title":"IET Gener Transm Distrib"},{"key":"9854_CR30","first-page":"1","volume":"6","author":"G Li","year":"2017","unstructured":"Li G, Qi X, Chen B et al (2017) Fast learning network with parallel layer perceptrons. Neural Process Lett 6:1\u201316","journal-title":"Neural Process Lett"},{"key":"9854_CR31","first-page":"484","volume":"8","author":"J Wang","year":"2011","unstructured":"Wang J, Wu W, Li Z, Li L (2011) Convergence of gradient method for double parallel feedforward neural network. Int J Numer Anal Model 8:484\u2013495","journal-title":"Int J Numer Anal Model"},{"key":"9854_CR32","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199661596.001.0001","volume-title":"Understanding flowers and flowering","author":"B Glover","year":"2014","unstructured":"Glover B (2014) Understanding flowers and flowering. Oxford University Press, Oxford"},{"key":"9854_CR33","first-page":"451","volume-title":"Intelligent Systems\u20192014","author":"S \u0141ukasik","year":"2015","unstructured":"\u0141ukasik S, Kowalski PA (2015) Study of flower pollination algorithm for continuous optimization. In: Angelov P, Atanassov KT, Doukovska L, Hadjiski M, Jotsov V, Kacprzyk J, Kasabov N, Sotirov S, Szmidt E, Zadro\u017cny S (eds) Intelligent Systems\u20192014. Springer, Cham, pp 451\u2013459"},{"key":"9854_CR34","first-page":"1","volume":"2","author":"S Pant","year":"2017","unstructured":"Pant S, Kumar A, Ram M (2017) Flower pollination algorithm development: a state of art review. Int J Syst Assur Eng Manag 2:1\u20139","journal-title":"Int J Syst Assur Eng Manag"},{"issue":"2","key":"9854_CR35","doi-asserted-by":"publisher","first-page":"1830","DOI":"10.1016\/j.jcp.2007.06.008","volume":"226","author":"I Pavlyukevich","year":"2007","unstructured":"Pavlyukevich I (2007) L\u00e9vy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830\u20131844","journal-title":"J Comput Phys"},{"key":"9854_CR36","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.eswa.2016.03.047","volume":"57","author":"E Nabil","year":"2016","unstructured":"Nabil E (2016) A modified flower pollination algorithm for global optimization. Expert Syst Appl 57:192\u2013203","journal-title":"Expert Syst Appl"},{"key":"9854_CR37","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.neucom.2015.01.110","volume":"188","author":"Y Zhou","year":"2016","unstructured":"Zhou Y, Wang R, Luo Q (2016) Elite opposition-based flower pollination algorithm. Neurocomputing 188:294\u2013310","journal-title":"Neurocomputing"},{"key":"9854_CR38","doi-asserted-by":"crossref","unstructured":"Ramadas M, Kumar S (2016) An efficient hybrid approach using differential evolution and flower pollination algorithm. In: 6th international conference on cloud system and big data engineering (Confluence). IEEE, pp 59\u201364","DOI":"10.1109\/CONFLUENCE.2016.7508048"},{"key":"9854_CR39","doi-asserted-by":"crossref","unstructured":"Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on IEEE world congress on computational intelligence evolutionary computation proceedings. IEEE, pp 69\u201373","DOI":"10.1109\/ICEC.1998.699146"},{"key":"9854_CR40","first-page":"253","volume":"8","author":"E Ozcan","year":"1998","unstructured":"Ozcan E, Mohan CK (1998) Analysis of a simple particle swarm optimization system. Intell Eng Syst Through Artif Neural Netw 8:253\u2013258","journal-title":"Intell Eng Syst Through Artif Neural Netw"},{"issue":"1","key":"9854_CR41","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TEVC.2010.2059031","volume":"15","author":"S Das","year":"2011","unstructured":"Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4\u201331","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"9854_CR42","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, Garc\u00eda S, Molina D et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3\u201318","journal-title":"Swarm Evolut Comput"},{"issue":"1","key":"9854_CR43","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/TII.2006.890530","volume":"3","author":"Z Song","year":"2007","unstructured":"Song Z, Kusiak A (2007) Constraint-based control of boiler efficiency: a data-mining approach. IEEE Trans Industr Inf 3(1):73\u201383","journal-title":"IEEE Trans Industr Inf"},{"issue":"10","key":"9854_CR44","doi-asserted-by":"publisher","first-page":"3132","DOI":"10.1016\/j.asoc.2012.06.016","volume":"12","author":"G Li","year":"2012","unstructured":"Li G, Niu P, Liu C et al (2012) Enhanced combination modeling method for combustion efficiency in coal-fired boilers. Appl Soft Comput J 12(10):3132\u20133140","journal-title":"Appl Soft Comput J"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-018-9854-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-018-9854-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-018-9854-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T20:24:53Z","timestamp":1720297493000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-018-9854-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,16]]},"references-count":44,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,4]]}},"alternative-id":["9854"],"URL":"https:\/\/doi.org\/10.1007\/s11063-018-9854-0","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2018,5,16]]},"assertion":[{"value":"16 May 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"All authors declares that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}