{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T13:37:03Z","timestamp":1781098623151,"version":"3.54.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773106"],"award-info":[{"award-number":["61773106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61903003"],"award-info":[{"award-number":["61903003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Project of Anhui Province Key Laboratory of Special and Heavy Load Robot","award":["TZJQR001-2021"],"award-info":[{"award-number":["TZJQR001-2021"]}]},{"name":"Scientific Research Projects in Colleges and Universities of Anhui Province","award":["KJ2019A0051"],"award-info":[{"award-number":["KJ2019A0051"]}]},{"name":"Nature Science Research Project of Anhui Province","award":["2008085QE227"],"award-info":[{"award-number":["2008085QE227"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00521-022-07097-5","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T15:04:15Z","timestamp":1647443055000},"page":"12097-12118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A bagging-based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization"],"prefix":"10.1007","volume":"34","author":[{"given":"Yuanchao","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianchang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shubin","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongkuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"issue":"2","key":"7097_CR1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TETCI.2017.2669104","volume":"1","author":"R Cheng","year":"2017","unstructured":"Cheng R, Rodemann T, Fischer M, Olhofer M, Jin Y (2017) Evolutionary many-objective optimization of hybrid electric vehicle control: From general optimization to preference articulation. IEEE Trans Emerg Top Comput Intell 1(2):97\u2013111","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"6","key":"7097_CR2","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1109\/TNNLS.2017.2695223","volume":"29","author":"J Liu","year":"2018","unstructured":"Liu J, Gong M, Miao Q, Wang X, Li H (2018) Structure learning for deep neural networks based on multiobjective optimization. IEEE Trans Neural Netw Learn Syst 29(6):2450\u20132463","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7097_CR3","doi-asserted-by":"crossref","unstructured":"Chugh T, Sindhya K, Miettinen K, Yaochu Jin, Kratky T, Makkonen P (2017) Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system. In: 2017 IEEE Congress on evolutionary computation (CEC) pp 1541\u20131548","DOI":"10.1109\/CEC.2017.7969486"},{"issue":"3","key":"7097_CR4","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MCI.2009.933094","volume":"4","author":"Y Jin","year":"2009","unstructured":"Jin Y, Sendhoff B (2009) A systems approach to evolutionary multiobjective structural optimization and beyond. IEEE Comput Intell M 4(3):62\u201376","journal-title":"IEEE Comput Intell M"},{"issue":"1","key":"7097_CR5","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.swevo.2011.03.001","volume":"1","author":"A Zhou","year":"2011","unstructured":"Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1(1):32\u201349","journal-title":"Swarm Evol Comput"},{"issue":"3","key":"7097_CR6","first-page":"29","volume":"3","author":"P Zsuzsa","year":"2006","unstructured":"Zsuzsa P, Radu-Emil P, Tar JK, M\u00e1rta T (2006) Use of multi-parametric quadratic programming in fuzzy control systems. Acta Polytech Hung 3(3):29\u201343","journal-title":"Acta Polytech Hung"},{"issue":"10","key":"7097_CR7","doi-asserted-by":"crossref","first-page":"4268","DOI":"10.1109\/TCYB.2019.2900159","volume":"50","author":"H Zhang","year":"2020","unstructured":"Zhang H, Sun Y, Zhao M, Chow TWS, Wu QMJ (2020) Bridging user interest to item content for recommender systems: an optimization model. IEEE Trans Cybern 50(10):4268\u20134280","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"7097_CR8","first-page":"1","volume":"18","author":"H Zapata","year":"2020","unstructured":"Zapata H, Perozo N, Angulo W (2020) A hybrid swarm algorithm for collective construction of 3d structures. Int J Artif Intell 18(1):1\u201318","journal-title":"Int J Artif Intell"},{"key":"7097_CR9","doi-asserted-by":"crossref","unstructured":"Precup RE, David RC, Roman RC, Szedlak-Stinean AI, Petriu EM (2021) Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using slime mould algorithm. Int J Syst Sci 0(0):1\u201316","DOI":"10.1080\/00207721.2021.1927236"},{"key":"7097_CR10","volume-title":"Multi-objective optimization using evolutionary algorithms","author":"K Deb","year":"2001","unstructured":"Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester, U.K"},{"issue":"2","key":"7097_CR11","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182\u2013197","journal-title":"IEEE Trans Evol Comput"},{"issue":"5","key":"7097_CR12","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/TEVC.2010.2041060","volume":"14","author":"LB Said","year":"2010","unstructured":"Said LB, Bechikh S, Ghedira K (2010) The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making. IEEE Trans Evol Comput 14(5):801\u2013818","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"7097_CR13","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TEVC.2015.2420112","volume":"20","author":"Y Yuan","year":"2016","unstructured":"Yuan Y, Xu H, Wang B, Yao X (2016) A new dominance relation based evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 20(1):16\u201337","journal-title":"IEEE Trans Evol Comput"},{"key":"7097_CR14","unstructured":"Jin Y, Sendhoff B (2002) Incorporation of fuzzy preferences into evolutionary. In: Genetic and evolutionary computation conference"},{"issue":"6","key":"7097_CR15","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang Q, Li H (2007) Moea\/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712\u2013731","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"7097_CR16","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TEVC.2013.2281533","volume":"18","author":"HL Liu","year":"2014","unstructured":"Liu HL, Gu F, Zhang Q (2014) Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems. IEEE Trans Evol Comput 18(3):450\u2013455","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"7097_CR17","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577\u2013601","journal-title":"IEEE Trans Evol Comput"},{"issue":"5","key":"7097_CR18","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/TEVC.2016.2519378","volume":"20","author":"R Cheng","year":"2016","unstructured":"Cheng R, Jin Y, Olhofer M, Sendhoff B (2016) A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 20(5):773\u2013791","journal-title":"IEEE Trans Evol Comput"},{"key":"7097_CR19","doi-asserted-by":"crossref","first-page":"106104","DOI":"10.1016\/j.asoc.2020.106104","volume":"89","author":"Y Yang","year":"2020","unstructured":"Yang Y, Liu J, Tan S (2020) A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism. Appl Soft Comput 89:106104","journal-title":"Appl Soft Comput"},{"issue":"1","key":"7097_CR20","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.ins.2011.08.027","volume":"182","author":"K Li","year":"2012","unstructured":"Li K, Kwonga S, Li M, Zheng J, Shen R (2012) Achieving balance between proximity and diversity in multi-objective evolutionary algorithm. Inf Sci 182(1):220\u2013242","journal-title":"Inf Sci"},{"issue":"4","key":"7097_CR21","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TEVC.2017.2749619","volume":"22","author":"Y Tian","year":"2017","unstructured":"Tian Y, Cheng R, Zhang X, Cheng F, Jin Y (2017) An indicator based multi-objective evolutionary algorithm with reference point adaptation for better versatility. IEEE Trans Evol Comput 22(4):609\u2013622","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"7097_CR22","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1109\/TEVC.2017.2776226","volume":"22","author":"I Hisao","year":"2018","unstructured":"Hisao I, Ryo I, Yu S, Yusuke N (2018) Reference point specification in inverted generational distance for triangular linear pareto front. IEEE Trans Evol Comput 22(6):961\u2013975. https:\/\/doi.org\/10.1109\/TEVC.2017.2776226","journal-title":"IEEE Trans Evol Comput"},{"key":"7097_CR23","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.asoc.2018.02.048","volume":"67","author":"F Li","year":"2018","unstructured":"Li F, Cheng R, Liu J, Jin Y (2018) A two-stage r2 indicator based evolutionary algorithm for many-objective optimization. Appl Soft Comput 67:245\u2013260","journal-title":"Appl Soft Comput"},{"key":"7097_CR24","doi-asserted-by":"crossref","first-page":"5079","DOI":"10.1007\/s00500-019-04258-y","volume":"24","author":"Y Liu","year":"2020","unstructured":"Liu Y, Liu J, Li T, Li Q (2020) An r2 indicator and weight vector-based evolutionary algorithm for multi-objective optimization. Soft Comput 24:5079\u20135100","journal-title":"Soft Comput"},{"issue":"10","key":"7097_CR25","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1080\/10426914.2016.1269923","volume":"32","author":"T Chugh","year":"2017","unstructured":"Chugh T, Chakraborti N, Sindhya K, Jin Y (2017) A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem. Mater Manuf Process 32(10):1172\u20131178","journal-title":"Mater Manuf Process"},{"issue":"3","key":"7097_CR26","first-page":"409","volume":"24","author":"C Yang","year":"2020","unstructured":"Yang C, Ding J, Jin Y, Chai T (2020) Offline data-driven multiobjective optimization: knowledge transfer between surrogates and generation of final solutions. IEEE Trans Evol Comput 24(3):409\u2013423","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"7097_CR27","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TEVC.2018.2869001","volume":"23","author":"Y Jin","year":"2019","unstructured":"Jin Y, Wang H, Chugh T, Guo D, Miettinen K (2019) Data-driven evolutionary optimization: an overview and case studies. IEEE Trans Evol Comput 23(3):442\u2013458","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"7097_CR28","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s00500-003-0328-5","volume":"9","author":"Y Jin","year":"2005","unstructured":"Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9(1):3\u201312","journal-title":"Soft Comput"},{"issue":"2","key":"7097_CR29","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.swevo.2011.05.001","volume":"1","author":"Y Jin","year":"2011","unstructured":"Jin Y (2011) Surrogate-assisted evolutionary computation:recent advances and future challenges. Swarm Evol Comput 1(2):61\u201370","journal-title":"Swarm Evol Comput"},{"key":"7097_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3102298","author":"Y Liu","year":"2021","unstructured":"Liu Y, Liu J, Jin Y (2021) Surrogate-assisted multipopulation particle swarm optimizer for high-dimensional expensive optimization. IEEE Trans Syst Man Cybern Syst. https:\/\/doi.org\/10.1109\/TSMC.2021.3102298","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"1","key":"7097_CR31","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/TEVC.2018.2802784","volume":"23","author":"L Pan","year":"2019","unstructured":"Pan L, He C, Tian Y, Wang H, Zhang X, Jin Y (2019) A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans Evol Comput 23(1):74\u201388","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"7097_CR32","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455\u2013492","journal-title":"J Glob Optim"},{"issue":"1","key":"7097_CR33","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/TEVC.2005.851274","volume":"10","author":"J Knowles","year":"2006","unstructured":"Knowles J (2006) Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans Evol Comput 10(1):50\u201366","journal-title":"IEEE Trans Evol Comput"},{"key":"7097_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87700-4_78","volume-title":"Parallel problem solving from nature\u2014PPSN X","author":"W Ponweiser","year":"2008","unstructured":"Ponweiser W, Wagner T, Biermann D, Vincze M (2008) Multiobjective optimization on a limited budget of evaluations using model-assisted S-metric selection. In: Rudolph G, Jansen T, Beume N, Lucas S, Poloni C (eds) Parallel problem solving from nature\u2014PPSN X, vol 5199. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-540-87700-4_78"},{"issue":"3","key":"7097_CR35","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/TEVC.2009.2033671","volume":"14","author":"Q Zhang","year":"2010","unstructured":"Zhang Q, Liu W, Tsang E, Virginas B (2010) Expensive multiobjective optimization by moea\/d with gaussian process model. IEEE Trans Evol Comput 14(3):456\u2013474","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"7097_CR36","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1109\/TEVC.2017.2697503","volume":"21","author":"D Zhan","year":"2017","unstructured":"Zhan D, Cheng Y, Liu J (2017) Expected improvement matrix-based infill criteria for expensive multiobjective optimization. IEEE Trans Evol Comput 21(6):956\u2013975","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"7097_CR37","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TEVC.2016.2622301","volume":"22","author":"T Chugh","year":"2018","unstructured":"Chugh T, Jin Y, Miettinen K, Hakanen J, Sindhya K (2018) A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Trans Evol Comput 22(1):129\u2013142","journal-title":"IEEE Trans Evol Comput"},{"key":"7097_CR38","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.ins.2020.01.048","volume":"519","author":"X Wang","year":"2020","unstructured":"Wang X, Jin Y, Schmitt S, Olhofer M (2020) An adaptive bayesian approach to surrogate-assisted evolutionary multi-objective optimization. Inf Sci 519:317\u2013331","journal-title":"Inf Sci"},{"key":"7097_CR39","doi-asserted-by":"crossref","unstructured":"Syberfeldt A, Grimm H, Ng A, John RI (2008) A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems. In: 2008 IEEE Congress on evolutionary computation (IEEE World congress on computational intelligence) pp. 3177\u20133184","DOI":"10.1109\/CEC.2008.4631228"},{"key":"7097_CR40","unstructured":"Mart\u00ednez SZ, Coello CAC (2013a) Moea\/d assisted by rbf networks for expensive multi-objective optimization problems. In: Proceedings of the 15th annual conference on genetic and evolutionary computation, New York, NY, USA, GECCO \u201913 pp. 1405-1412"},{"key":"7097_CR41","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez SZ, Coello CAC (2013b) Combining surrogate models and local search for dealing with expensive multi-objective optimization problems. In: 2013 IEEE Congress on evolutionary computation. pp. 2572\u20132579","DOI":"10.1109\/CEC.2013.6557879"},{"key":"7097_CR42","doi-asserted-by":"crossref","unstructured":"Azzouz N, Bechikh S, Ben\u00a0Said L (2014) Steady state ibea assisted by mlp neural networks for expensive multi-objective optimization problems. In: Proceedings of the 2014 Annual Conference on genetic and evolutionary computation, association for computing machinery, New York, NY, USA, GECCO \u201914 pp. 581\u2013588","DOI":"10.1145\/2576768.2598271"},{"key":"7097_CR43","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.jocs.2016.05.013","volume":"16","author":"RG Regis","year":"2016","unstructured":"Regis RG (2016) Multi-objective constrained black-box optimization using radial basis function surrogates. J Comput Sci 16:140\u2013155","journal-title":"J Comput Sci"},{"issue":"3","key":"7097_CR44","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/TCYB.2018.2794503","volume":"49","author":"D Guo","year":"2019","unstructured":"Guo D, Jin Y, Ding J, Chai T (2019) Heterogeneous ensemble-based infill criterion for evolutionary multiobjective optimization of expensive problems. IEEE Trans Cybern 49(3):1012\u20131025","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"7097_CR45","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/TEVC.2019.2899030","volume":"23","author":"A Habib","year":"2019","unstructured":"Habib A, Singh HK, Chugh T, Ray T, Miettinen K (2019) A multiple surrogate assisted decomposition-based evolutionary algorithm for expensive multi\/many-objective optimization. IEEE Trans Evol Comput 23(6):1000\u20131014","journal-title":"IEEE Trans Evol Comput"},{"key":"7097_CR46","doi-asserted-by":"crossref","first-page":"106520","DOI":"10.1016\/j.knosys.2020.106520","volume":"211","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Sun C, Zeng J, Tan Y, Zhang G (2021) A surrogate-ensemble assisted expensive many-objective optimization. Knowl Based Syst 211:106520","journal-title":"Knowl Based Syst"},{"key":"7097_CR47","doi-asserted-by":"crossref","first-page":"4387","DOI":"10.1007\/s00521-020-05258-y","volume":"33","author":"F Li","year":"2021","unstructured":"Li F, Gao L, Garg A, Shen W, Huang S (2021) A comparative study of pre-screening strategies within a surrogate-assisted multi-objective algorithm framework for computationally expensive problems. Neural Comput Appl 33:4387\u20134416","journal-title":"Neural Comput Appl"},{"key":"7097_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CEC.2012.6252865","volume-title":"2012 IEEE congress on evolutionary computation (CEC)","author":"C Seah","year":"2012","unstructured":"Seah C, Ong Y, Tsang I, Jiang S (2012) Pareto rank learning in multi-objective evolutionary algorithms. In: 2012 IEEE congress on evolutionary computation (CEC). IEEE, Brisbane, pp 1\u20138. https:\/\/doi.org\/10.1109\/CEC.2012.6252865"},{"key":"7097_CR49","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1109\/CEC.2014.6900641","volume-title":"2014 IEEE Congress on evolutionary computation (CEC)","author":"S Bandaru","year":"2014","unstructured":"Bandaru S, Ng A, Deb K (2014) On the performance of classification algorithms for learning pareto-dominance relations. In: 2014 IEEE Congress on evolutionary computation (CEC). IEEE, Beijing, pp 1139\u20131146. https:\/\/doi.org\/10.1109\/CEC.2014.6900641"},{"key":"7097_CR50","doi-asserted-by":"publisher","first-page":"2883","DOI":"10.1109\/CEC.2015.7257247","volume-title":"2015 IEEE Congress on evolutionary computation (CEC)","author":"J Zhang","year":"2015","unstructured":"Zhang J, Zhou A, Zhang G (2015) A classification and pareto domination based multiobjective evolutionary algorithm. In: 2015 IEEE Congress on evolutionary computation (CEC). IEEE, Sendai, pp 2883\u20132890. https:\/\/doi.org\/10.1109\/CEC.2015.7257247"},{"key":"7097_CR51","doi-asserted-by":"crossref","unstructured":"He C, Cheng R, Jin Y, Yao X (2019) Surrogate-assisted expensive many-objective optimization by model fusion. In: 2019 IEEE Congress on evolutionary computation (CEC) pp. 1672\u20131679","DOI":"10.1109\/CEC.2019.8790155"},{"issue":"5","key":"7097_CR52","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TEVC.2002.800884","volume":"6","author":"Y Jin","year":"2002","unstructured":"Jin Y, Olhofer M, Sendhoff B (2002) A framework for evolutionary optimization with approximate fitness functions. IEEE Trans Evol Comput 6(5):481\u2013494","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"7097_CR53","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"key":"7097_CR54","first-page":"106","volume":"91","author":"C Fan","year":"2020","unstructured":"Fan C, Hou B, Zheng J, Xiao L, Yi L (2020) A surrogate-assisted particle swarm optimization using ensemble learning for expensive problems with small sample datasets. Appl Soft Comput 91:106242","journal-title":"Appl Soft Comput"},{"issue":"2","key":"7097_CR55","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/TEVC.2018.2834881","volume":"23","author":"H Wang","year":"2019","unstructured":"Wang H, Jin Y, Sun C, Doherty J (2019) Offline data-driven evolutionary optimization using selective surrogate ensembles. IEEE Trans Evol Comput 23(2):203\u2013216","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"7097_CR56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00158-001-0160-4","volume":"23","author":"R Jin","year":"2001","unstructured":"Jin R, Chen W, Simpson WT (2001) Comparative studies of metamodeling techniques under multiple modeling criteria. Struct Multidiscip Optim 23(1):1\u201313","journal-title":"Struct Multidiscip Optim"},{"issue":"5","key":"7097_CR57","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1109\/TEVC.2020.2979740","volume":"24","author":"JY Li","year":"2020","unstructured":"Li JY, Zhan ZH, Wang C, Jin H, Zhang J (2020) Boosting data-driven evolutionary algorithm with localized data generation. IEEE Trans Evol Comput 24(5):923\u2013937","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"7097_CR58","first-page":"239","volume":"21","author":"MD McKay","year":"1979","unstructured":"McKay MD, Beckman RJ, Conover WJ (1979) Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239\u2013245","journal-title":"Technometrics"},{"key":"7097_CR59","doi-asserted-by":"crossref","DOI":"10.1201\/b12207","volume-title":"Ensemble methods: Foundations and algorithms","author":"ZH Zhou","year":"2012","unstructured":"Zhou ZH (2012) Ensemble methods: Foundations and algorithms. CRC Press, Boca Raton"},{"issue":"3","key":"7097_CR60","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1137\/S1052623496307510","volume":"8","author":"I Das","year":"1996","unstructured":"Das I, Dennis JE (1996) Normal-boundary intersection: a new method for generating the pareto surface in nonlinear multicriteria optimization problems. Siam J Optimiz 8(3):631\u2013657","journal-title":"Siam J Optimiz"},{"key":"7097_CR61","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3067785","author":"J Liu","year":"2021","unstructured":"Liu J, Liu Y, Jin Y, Li F (2021) A decision variable assortment-based evolutionary algorithm for dominance robust multiobjective optimization. IEEE Trans Syst Man Cybern Syst. https:\/\/doi.org\/10.1109\/TSMC.2021.3067785","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"7097_CR62","doi-asserted-by":"crossref","first-page":"105711","DOI":"10.1016\/j.knosys.2020.105711","volume":"195","author":"Y Liu","year":"2020","unstructured":"Liu Y, Liu J, Jin Y, Li F, Zheng T (2020) An affinity propagation clustering based particle swarm optimizer for dynamic optimization. Knowl Based Syst 195:105711","journal-title":"Knowl Based Syst"},{"key":"7097_CR63","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06355-2","author":"T Zheng","year":"2021","unstructured":"Zheng T, Liu J, Liu Y, Tan S (2021) Hybridizing multi-objective, clustering and particle swarm optimization for multimodal optimization. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06355-2","journal-title":"Neural Comput Appl"},{"key":"7097_CR64","volume-title":"Advancedinformation and knowledge processing","author":"K Deb","year":"2005","unstructured":"Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization. In: Advancedinformation and knowledge processing. Springer, London"},{"issue":"5","key":"7097_CR65","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1109\/TEVC.2005.861417","volume":"10","author":"S Huband","year":"2006","unstructured":"Huband S, Hingston P, Barone L, While RL (2006) A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans Evol Comput 10(5):477\u2013506","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"7097_CR66","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1162\/evco.1999.7.3.205","volume":"7","author":"K Deb","year":"1999","unstructured":"Deb K (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol Comput 7(3):205\u2013230","journal-title":"Evol Comput"},{"issue":"6","key":"7097_CR67","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s00158-007-0163-x","volume":"35","author":"X Liao","year":"2008","unstructured":"Liao X, Li Q, Yang X, Zhang W, Li W (2008) Multiobjective optimization for crash safety design of vehicles using stepwise regression model. Struct Multidiplinary Optim 35(6):561\u2013569","journal-title":"Struct Multidiplinary Optim"},{"issue":"4","key":"7097_CR68","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/MCI.2017.2742868","volume":"12","author":"Y Tian","year":"2017","unstructured":"Tian Y, Cheng R, Zhang X, Jin Y (2017) Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]. IEEE Comput Intell M 12(4):73\u201387","journal-title":"IEEE Comput Intell M"},{"issue":"2","key":"7097_CR69","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TEVC.2003.810761","volume":"7","author":"PAN Bosman","year":"2003","unstructured":"Bosman PAN, Thierens D (2003) The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Trans Evol Comput 7(2):174\u2013188","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"7097_CR70","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/TEVC.2005.851275","volume":"10","author":"L While","year":"2006","unstructured":"While L, Hingston P, Barone L, Huband S (2006) A faster algorithm for calculating hypervolume. IEEE Trans Evol Comput 10(1):29\u201338","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"7097_CR71","doi-asserted-by":"crossref","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, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3\u201318","journal-title":"Swarm Evol Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07097-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07097-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07097-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T09:36:37Z","timestamp":1657877797000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07097-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,16]]},"references-count":71,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["7097"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07097-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,16]]},"assertion":[{"value":"1 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2022","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 declared that there are no conflicts of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. The work is original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. The manuscript is approved by all authors for publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}