{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:12:05Z","timestamp":1782990725731,"version":"3.54.5"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021ZD0112301"],"award-info":[{"award-number":["2021ZD0112301"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61890930-5"],"award-info":[{"award-number":["61890930-5"]}],"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":["62021003"],"award-info":[{"award-number":["62021003"]}],"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":["62273013"],"award-info":[{"award-number":["62273013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s10489-024-05612-w","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T14:02:47Z","timestamp":1725026567000},"page":"11649-11671","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multifidelity surrogates-assisted multi-objective particle swarm algorithm for offline data-driven optimization"],"prefix":"10.1007","volume":"54","author":[{"given":"Yingying","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1707-6074","authenticated-orcid":false,"given":"Junfei","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"5612_CR1","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1109\/TEVC.2016.2555315","volume":"20","author":"H Wang","year":"2016","unstructured":"Wang H, Jin Y, Jansen JO (2016) Data-driven surrogate-assisted multiobjective evolutionary optimization of a trauma system. IEEE Trans Evol Comput 20:939\u2013952. https:\/\/doi.org\/10.1109\/TEVC.2016.2555315","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR2","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1109\/TEVC.2019.2925959","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 24:409\u2013423. https:\/\/doi.org\/10.1109\/TEVC.2019.2925959","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR3","doi-asserted-by":"publisher","first-page":"5949","DOI":"10.1007\/s10489-021-02709-4","volume":"52","author":"Q Gu","year":"2022","unstructured":"Gu Q, Zhang X, Chen L, Xiong N (2022) An improved bagging ensemble surrogate-assisted evolutionary algorithm for expensive many-objective optimization. Appl Intell 52:5949\u20135965. https:\/\/doi.org\/10.1007\/s10489-021-02709-4","journal-title":"Appl Intell"},{"key":"5612_CR4","doi-asserted-by":"publisher","first-page":"12448","DOI":"10.1007\/s10489-022-04080-4","volume":"53","author":"N Liu","year":"2023","unstructured":"Liu N, Pan JS, Chu SC, Lai T (2023) A surrogate-assisted bi-swarm evolutionary algorithm for expensive optimization. Appl Intell 53:12448\u201312471. https:\/\/doi.org\/10.1007\/s10489-022-04080-4","journal-title":"Appl Intell"},{"key":"5612_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3246467","author":"J Sun","year":"2023","unstructured":"Sun J, Meng X, Qiao J (2023) Data-driven optimal control for municipal solid waste incineration process. IEEE Trans Industr Inform. https:\/\/doi.org\/10.1109\/TII.2023.3246467","journal-title":"IEEE Trans Industr Inform"},{"key":"5612_CR6","doi-asserted-by":"publisher","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 Processes 32:1172\u20131178. https:\/\/doi.org\/10.1080\/10426914.2016.1269923","journal-title":"Mater Manuf Processes"},{"key":"5612_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11633-022-1317-4","volume":"19","author":"JY Li","year":"2022","unstructured":"Li JY, Zhan ZH, Zhang J (2022) Evolutionary computation for expensive optimization: A survey. Machine Intelligence Research 19:3\u201323. https:\/\/doi.org\/10.1007\/s11633-022-1317-4","journal-title":"Machine Intelligence Research"},{"key":"5612_CR8","doi-asserted-by":"publisher","first-page":"101096","DOI":"10.1016\/j.swevo.2022.101096","volume":"72","author":"F Li","year":"2022","unstructured":"Li F, Li Y, Cai X, Gao L (2022) A surrogate-assisted hybrid swarm optimization algorithm for high-dimensional computationally expensive problems. Swarm Evol Comput 72:101096. https:\/\/doi.org\/10.1016\/j.swevo.2022.101096","journal-title":"Swarm Evol Comput"},{"key":"5612_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04916-7","author":"L Lin","year":"2023","unstructured":"Lin L, Liu T, Leng J et al (2023) Classification model-based assisted preselection and environment selection approach for evolutionary expensive bilevel optimization. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-023-04916-7","journal-title":"Appl Intell"},{"key":"5612_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3170344","author":"Z Liu","year":"2022","unstructured":"Liu Z, Wang H, Jin Y (2022) Performance indicator-based adaptive model selection for offline data-driven multiobjective evolutionary optimization. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2022.3170344","journal-title":"IEEE Trans Cybern"},{"key":"5612_CR11","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1109\/TEVC.2018.2869001","volume":"23","author":"Y Jin","year":"2019","unstructured":"Jin Y, Wang H, Chugh T et al (2019) Data-driven evolutionary optimization: An overview and case studies. IEEE Trans Evol Comput 23:442\u2013458. https:\/\/doi.org\/10.1109\/TEVC.2018.2869001","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR12","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TEVC.2016.2622301","volume":"22","author":"T Chugh","year":"2018","unstructured":"Chugh T, Jin Y, Miettinen K et al (2018) A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Trans Evol Comput 22:129\u2013142. https:\/\/doi.org\/10.1109\/TEVC.2016.2622301","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR13","doi-asserted-by":"publisher","first-page":"3925","DOI":"10.1109\/TCYB.2020.3008280","volume":"51","author":"JY Li","year":"2021","unstructured":"Li JY, Zhan ZH, Wang H, Zhang J (2021) Data-driven evolutionary algorithm with perturbation-based ensemble surrogates. IEEE Trans Cybern 51:3925\u20133937. https:\/\/doi.org\/10.1109\/TCYB.2020.3008280","journal-title":"IEEE Trans Cybern"},{"key":"5612_CR14","doi-asserted-by":"publisher","first-page":"101080","DOI":"10.1016\/j.swevo.2022.101080","volume":"71","author":"H Zhen","year":"2022","unstructured":"Zhen H, Gong W, Wang L (2022) Offline data-driven evolutionary optimization based on model selection. Swarm Evol Comput 71:101080. https:\/\/doi.org\/10.1016\/j.swevo.2022.101080","journal-title":"Swarm Evol Comput"},{"key":"5612_CR15","doi-asserted-by":"publisher","first-page":"106520","DOI":"10.1016\/j.knosys.2020.106520","volume":"211","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Sun C, Zeng J et al (2021) A surrogate-ensemble assisted expensive many-objective optimization. Knowl Based Syst 211:106520. https:\/\/doi.org\/10.1016\/j.knosys.2020.106520","journal-title":"Knowl Based Syst"},{"key":"5612_CR16","doi-asserted-by":"publisher","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 23:203\u2013216. https:\/\/doi.org\/10.1109\/TEVC.2018.2834881","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR17","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TEVC.2018.2802784","volume":"23","author":"L Pan","year":"2019","unstructured":"Pan L, He C, Tian Y et al (2019) A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans Evol Comput 23:74\u201388. https:\/\/doi.org\/10.1109\/TEVC.2018.2802784","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR18","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"},{"key":"5612_CR19","doi-asserted-by":"publisher","first-page":"119075","DOI":"10.1016\/j.eswa.2022.119075","volume":"214","author":"Y Liu","year":"2023","unstructured":"Liu Y, Liu J, Tan S (2023) Decision space partition based surrogate-assisted evolutionary algorithm for expensive optimization. Expert Syst Appl 214:119075. https:\/\/doi.org\/10.1016\/j.eswa.2022.119075","journal-title":"Expert Syst Appl"},{"key":"5612_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-74640-7","volume-title":"Data-Driven Evolutionary Optimization","author":"Y Jin","year":"2021","unstructured":"Jin Y, Wang H, Sun C (2021) Data-Driven Evolutionary Optimization. Springer"},{"key":"5612_CR21","doi-asserted-by":"publisher","first-page":"100800","DOI":"10.1016\/j.swevo.2020.100800","volume":"60","author":"P Huang","year":"2021","unstructured":"Huang P, Wang H, Jin Y (2021) Offline data-driven evolutionary optimization based on tri-training. Swarm Evol Comput 60:100800. https:\/\/doi.org\/10.1016\/j.swevo.2020.100800","journal-title":"Swarm Evol Comput"},{"key":"5612_CR22","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/j.ins.2019.06.016","volume":"502","author":"AHKT HabibSinghRay","year":"2019","unstructured":"HabibSinghRay AHKT (2019) A multiple surrogate assisted multi\/many-objective multi-fidelity evolutionary algorithm. Inf Sci (N Y) 502:537\u2013557. https:\/\/doi.org\/10.1016\/j.ins.2019.06.016","journal-title":"Inf Sci (N Y)"},{"key":"5612_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2019.01.004","volume":"170","author":"J Yi","year":"2019","unstructured":"Yi J, Gao L, Li X et al (2019) An on-line variable-fidelity surrogate-assisted harmony search algorithm with multi-level screening strategy for expensive engineering design optimization. Knowl Based Syst 170:1\u201319. https:\/\/doi.org\/10.1016\/j.knosys.2019.01.004","journal-title":"Knowl Based Syst"},{"key":"5612_CR24","doi-asserted-by":"publisher","first-page":"106276","DOI":"10.1016\/j.asoc.2020.106276","volume":"92","author":"H Wang","year":"2020","unstructured":"Wang H, Jin Y, Yang C, Jiao L (2020) Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm. Applied Soft Computing Journal 92:106276. https:\/\/doi.org\/10.1016\/j.asoc.2020.106276","journal-title":"Applied Soft Computing Journal"},{"key":"5612_CR25","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1109\/TEVC.2021.3064835","volume":"25","author":"X Ji","year":"2021","unstructured":"Ji X, Zhang Y, Gong D, Sun X (2021) Dual-surrogate-assisted cooperative particle swarm optimization for expensive multimodal problems. IEEE Trans Evol Comput 25:794\u2013808. https:\/\/doi.org\/10.1109\/TEVC.2021.3064835","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR26","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1109\/TEVC.2022.3182810","volume":"27","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Ji XF, Gao XZ et al (2023) Objective-constraint mutual-guided surrogate-based particle swarm optimization for expensive constrained multimodal problems. IEEE Trans Evol Comput 27:908\u2013922. https:\/\/doi.org\/10.1109\/TEVC.2022.3182810","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR27","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TCYB.2017.2710978","volume":"47","author":"H Wang","year":"2017","unstructured":"Wang H, Jin Y, Doherty J (2017) Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems. IEEE Trans Cybern 47:2664\u20132677. https:\/\/doi.org\/10.1109\/TCYB.2017.2710978","journal-title":"IEEE Trans Cybern"},{"key":"5612_CR28","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1109\/TEVC.2021.3073648","volume":"25","author":"Z Song","year":"2021","unstructured":"Song Z, Wang H, He C, Jin Y (2021) A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization. IEEE Trans Evol Comput 25:1013\u20131027. https:\/\/doi.org\/10.1109\/TEVC.2021.3073648","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR29","doi-asserted-by":"publisher","first-page":"110630","DOI":"10.1016\/j.knosys.2023.110630","volume":"274","author":"H Bian","year":"2023","unstructured":"Bian H, Tian J, Yu J, Yu H (2023) Bayesian co-evolutionary optimization based entropy search for high-dimensional many-objective optimization. Knowl Based Syst 274:110630. https:\/\/doi.org\/10.1016\/j.knosys.2023.110630","journal-title":"Knowl Based Syst"},{"key":"5612_CR30","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1109\/TEVC.2020.3017865","volume":"25","author":"FF Wei","year":"2021","unstructured":"Wei FF, Chen WN, Yang Q et al (2021) A classifier-assisted level-based learning swarm optimizer for expensive optimization. IEEE Trans Evol Comput 25:219\u2013233. https:\/\/doi.org\/10.1109\/TEVC.2020.3017865","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/SSCI.2016.7850211","volume":"2016","author":"D Guo","year":"2017","unstructured":"Guo D, Chai T, Ding J, Jin Y (2017) Small data driven evolutionary multi-objective optimization of fused magnesium furnaces. 2016 IEEE Symposium Series on Computational Intelligence. SSCI 2016:1\u20138. https:\/\/doi.org\/10.1109\/SSCI.2016.7850211","journal-title":"SSCI"},{"key":"5612_CR32","doi-asserted-by":"publisher","unstructured":"Huang P, Wang\u00a0 H, Ma W (2019) Stochastic ranking for offline data-driven evolutionary optimization using radial basis function networks with multiple kernel. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp 2050\u20132057. https:\/\/doi.org\/10.1109\/SSCI44817.2019.9002961","DOI":"10.1109\/SSCI44817.2019.9002961"},{"key":"5612_CR33","doi-asserted-by":"publisher","first-page":"107212","DOI":"10.1016\/j.knosys.2021.107212","volume":"227","author":"J Liu","year":"2021","unstructured":"Liu J, Dong H, Wang P (2021) Multi-fidelity global optimization using a data-mining strategy for computationally intensive black-box problems. Knowl Based Syst 227:107212. https:\/\/doi.org\/10.1016\/j.knosys.2021.107212","journal-title":"Knowl Based Syst"},{"key":"5612_CR34","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TEVC.2021.3120111","volume":"26","author":"MM Mamun","year":"2022","unstructured":"Mamun MM, Singh HK, Ray T (2022) A multifidelity approach for bilevel optimization with limited computing budget. IEEE Trans Evol Comput 26:392\u2013399. https:\/\/doi.org\/10.1109\/TEVC.2021.3120111","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR35","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1109\/TEVC.2009.2027359","volume":"14","author":"D Lim","year":"2010","unstructured":"Lim D, Jin Y, Ong YS, Sendhoff B (2010) Generalizing surrogate-assisted evolutionary computation. IEEE Trans Evol Comput 14:329\u2013355. https:\/\/doi.org\/10.1109\/TEVC.2009.2027359","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR36","doi-asserted-by":"publisher","first-page":"5317","DOI":"10.1109\/TMTT.2023.3276212","volume":"71","author":"Y Li","year":"2023","unstructured":"Li Y, Luo X (2023) Adaptive synthesis using hybrid genetic algorithm and particle swarm optimization for reflectionless filter with lumped elements. IEEE Trans Microw Theory Tech 71:5317\u20135334. https:\/\/doi.org\/10.1109\/TMTT.2023.3276212","journal-title":"IEEE Trans Microw Theory Tech"},{"key":"5612_CR37","doi-asserted-by":"publisher","first-page":"1941","DOI":"10.1109\/TETC.2021.3137980","volume":"10","author":"J Bi","year":"2022","unstructured":"Bi J, Yuan H, Zhang K, Zhou MC (2022) Energy-minimized partial computation offloading for delay-sensitive applications in heterogeneous edge networks. IEEE Trans Emerg Top Comput 10:1941\u20131954. https:\/\/doi.org\/10.1109\/TETC.2021.3137980","journal-title":"IEEE Trans Emerg Top Comput"},{"key":"5612_CR38","doi-asserted-by":"publisher","first-page":"9290","DOI":"10.1109\/TCYB.2020.3029748","volume":"52","author":"N Zeng","year":"2022","unstructured":"Zeng N, Wang Z, Liu W et al (2022) A dynamic neighborhood-based switching particle swarm optimization algorithm. IEEE Trans Cybern 52:9290\u20139301. https:\/\/doi.org\/10.1109\/TCYB.2020.3029748","journal-title":"IEEE Trans Cybern"},{"key":"5612_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108606","volume":"119","author":"Y Hu","year":"2022","unstructured":"Hu Y, Wang J, Liang J et al (2022) A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems. Appl Soft Comput 119:108606. https:\/\/doi.org\/10.1016\/j.asoc.2022.108606","journal-title":"Appl Soft Comput"},{"key":"5612_CR40","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s10462-022-10164-x","volume":"56","author":"M Braik","year":"2023","unstructured":"Braik M, Al-Zoubi H, Ryalat M et al (2023) Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems. Artif Intell Rev 56:27\u201399. https:\/\/doi.org\/10.1007\/s10462-022-10164-x","journal-title":"Artif Intell Rev"},{"key":"5612_CR41","doi-asserted-by":"publisher","first-page":"20535","DOI":"10.1007\/s10489-023-04519-2","volume":"53","author":"J Osei-Kwakye","year":"2023","unstructured":"Osei-Kwakye J, Han F, Amponsah AA et al (2023) A diversity enhanced hybrid particle swarm optimization and crow search algorithm for feature selection. Appl Intell 53:20535\u201320560. https:\/\/doi.org\/10.1007\/s10489-023-04519-2","journal-title":"Appl Intell"},{"key":"5612_CR42","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1109\/TCYB.2020.2967553","volume":"51","author":"F Li","year":"2021","unstructured":"Li F, Cai X, Gao L, Shen W (2021) A surrogate-assisted multiswarm optimization algorithm for high-dimensional computationally expensive problems. IEEE Trans Cybern 51:1390\u20131402. https:\/\/doi.org\/10.1109\/TCYB.2020.2967553","journal-title":"IEEE Trans Cybern"},{"key":"5612_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/J.ASOC.2022.108532","volume":"119","author":"Y Cui","year":"2022","unstructured":"Cui Y, Meng X, Qiao J (2022) A multi-objective particle swarm optimization algorithm based on two-archive mechanism. Appl Soft Comput 119:108532. https:\/\/doi.org\/10.1016\/J.ASOC.2022.108532","journal-title":"Appl Soft Comput"},{"key":"5612_CR44","doi-asserted-by":"publisher","unstructured":"Basseur M, Burke EK (2007) Indicator-based multi-objective local search. In: 2007 IEEE Congress on Evolutionary Computation, pp 3100\u20133107. https:\/\/doi.org\/10.1109\/CEC.2007.4424867","DOI":"10.1109\/CEC.2007.4424867"},{"key":"5612_CR45","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1109\/TEVC.2013.2262178","volume":"18","author":"MQ Li","year":"2014","unstructured":"Li MQ, Yang SX, Liu XH (2014) Shift-based density estimation for pareto-based algorithms in many-objective optimization. IEEE Trans Evol Comput 18:348\u2013365","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR46","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/TEVC.2016.2600642","volume":"22","author":"X Zhang","year":"2018","unstructured":"Zhang X, Tian Y, Cheng R, Jin Y (2018) A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans Evol Comput 22:97\u2013112. https:\/\/doi.org\/10.1109\/TEVC.2016.2600642","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR47","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TETCI.2017.2769104","volume":"2","author":"A Gupta","year":"2018","unstructured":"Gupta A, Ong YS, Feng L (2018) Insights on transfer optimization: Because experience is the best teacher. IEEE Trans Emerg Top Comput Intell 2:51\u201364. https:\/\/doi.org\/10.1109\/TETCI.2017.2769104","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"5612_CR48","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1109\/TEVC.2021.3133874","volume":"26","author":"X Xue","year":"2022","unstructured":"Xue X, Yang C, Hu Y et al (2022) Evolutionary sequential transfer optimization for objective-heterogeneous problems. IEEE Trans Evol Comput 26:1424\u20131438. https:\/\/doi.org\/10.1109\/TEVC.2021.3133874","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR49","doi-asserted-by":"publisher","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 20:773\u2013791. https:\/\/doi.org\/10.1109\/TEVC.2016.2519378","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR50","doi-asserted-by":"publisher","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 (N Y) 519:317\u2013331. https:\/\/doi.org\/10.1016\/j.ins.2020.01.048","journal-title":"Inf Sci (N Y)"},{"key":"5612_CR51","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1109\/TEVC.2014.2350987","volume":"19","author":"HD Wang","year":"2015","unstructured":"Wang HD, Jiao LC, Yao X (2015) Two_Arch2: An improved two-archive algorithm for many-objective optimization. IEEE Trans Evol Comput 19:524\u2013541","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR52","doi-asserted-by":"publisher","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 6:182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"key":"5612_CR53","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1007\/978-3-540-30217-9_84\/COVER","volume":"3242","author":"E Zitzler","year":"2004","unstructured":"Zitzler E, K\u00fcnzli S (2004) Indicator-based selection in multiobjective search. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3242:832\u2013842. https:\/\/doi.org\/10.1007\/978-3-540-30217-9_84\/COVER","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"5612_CR54","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/MCI.2017.2742868","volume":"12","author":"Y Tian","year":"2017","unstructured":"Tian Y, Cheng R, Zhang XY, Jin YC (2017) PlatEMO: A matlab platform for evolutionary multi-objective optimization. IEEE Comput Intell Mag 12:73\u201387","journal-title":"IEEE Comput Intell Mag"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05612-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05612-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05612-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T15:28:33Z","timestamp":1726673313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05612-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":54,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["5612"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05612-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]},"assertion":[{"value":"11 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}