{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:23:29Z","timestamp":1777652609413,"version":"3.51.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"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":["Soft Comput"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00500-023-09464-3","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T10:02:48Z","timestamp":1703152968000},"page":"5881-5897","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A low-sample-count, high-precision Pareto front adaptive sampling algorithm based on multi-criteria and Voronoi"],"prefix":"10.1007","volume":"28","author":[{"given":"Changkun","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailang","family":"Sang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzhang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"9464_CR1","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1145\/116873.116880","volume":"23","author":"F Aurenhammer","year":"1991","unstructured":"Aurenhammer F (1991) Voronoi diagrams\u2014a survey of a fundamental geometric data structure. ACM Comput Surv 23:345\u2013405","journal-title":"ACM Comput Surv"},{"key":"9464_CR2","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1007\/s00158-019-02262-2","volume":"60","author":"Q Chen","year":"2019","unstructured":"Chen Q, Ni J, Wang Q, Shi X (2019) Match-based pseudo-MAP full-operation-range optimization method for a turbocharger compressor. Struct Multidiscip Optim 60:1139\u20131153","journal-title":"Struct Multidiscip Optim"},{"key":"9464_CR3","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.ast.2019.02.039","volume":"87","author":"S Cheng","year":"2019","unstructured":"Cheng S, Zhan H, Shu Z, Fan H, Wang B (2019) Effective optimization on Bump inlet using meta-model multi-objective particle swarm assisted by expected hyper-volume improvement. Aerosp Sci Technol 87:431\u2013447","journal-title":"Aerosp Sci Technol"},{"key":"9464_CR4","doi-asserted-by":"publisher","first-page":"1948","DOI":"10.1137\/090761811","volume":"33","author":"K Crombecq","year":"2011","unstructured":"Crombecq K, Gorissen D, Deschrijver D, Dhaene T (2011) A novel hybrid sequential design strategy for global surrogate modeling of computer experiments. Siam J Sci Comput 33:1948\u20131974","journal-title":"Siam J Sci Comput"},{"key":"9464_CR5","doi-asserted-by":"crossref","unstructured":"Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International conference on parallel problem solving from nature, pp 849\u2013858.","DOI":"10.1007\/3-540-45356-3_83"},{"key":"9464_CR6","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.compchemeng.2014.05.021","volume":"68","author":"J Eason","year":"2014","unstructured":"Eason J, Cremaschi S (2014) Adaptive sequential sampling for surrogate model generation with artificial neural networks. Comput Chem Eng 68:220\u2013232","journal-title":"Comput Chem Eng"},{"key":"9464_CR7","doi-asserted-by":"publisher","first-page":"117582","DOI":"10.1016\/j.energy.2020.117582","volume":"201","author":"K Ekradi","year":"2020","unstructured":"Ekradi K, Madadi A (2020) Performance improvement of a transonic centrifugal compressor impeller with splitter blade by three-dimensional optimization. Energy 201:117582","journal-title":"Energy"},{"key":"9464_CR8","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1109\/TEVC.2005.859463","volume":"10","author":"MTM Emmerich","year":"2006","unstructured":"Emmerich MTM, Giannakoglou KC, Naujoks B (2006) Single-objective and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Trans Evolut Comput 10:421\u2013439","journal-title":"IEEE Trans Evolut Comput"},{"key":"9464_CR9","first-page":"509","volume":"2","author":"L Gu","year":"2001","unstructured":"Gu L (2001) A comparison of polynomial based regression models in vehicle safety analysis. Proc ASME Des Eng Tech Conf 2:509\u2013514","journal-title":"Proc ASME Des Eng Tech Conf"},{"key":"9464_CR10","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.ast.2014.04.014","volume":"39","author":"S Guo","year":"2014","unstructured":"Guo S, Duan F, Tang H, Lim SC, Yip MS (2014) Multi-objective optimization for centrifugal compressor of mini turbojet engine. Aerosp Sci Technol 39:414\u2013425","journal-title":"Aerosp Sci Technol"},{"key":"9464_CR11","doi-asserted-by":"crossref","unstructured":"Guo Z, Song L, Li J, Li G, Feng Z (2015) Research on meta-model based global design optimization and data mining methods","DOI":"10.1115\/GT2015-42554"},{"key":"9464_CR12","doi-asserted-by":"crossref","unstructured":"Ibaraki S, Van den Braembussche R, Verstraete T, Alsalihi Z, Sugimoto K, Tomita I (2014a) Aerodynamic design optimization of a centrifugal compressor impeller based on an artificial neural network and genetic algorithm. In: Institute Of Mechanical Engineers, 'editors'. 11th International Conference on Turbochargers and Turbocharging. Oxford: Woodhead Publishing;. p. 65\u201377.","DOI":"10.1533\/978081000342.65"},{"key":"9464_CR13","doi-asserted-by":"crossref","unstructured":"Ibaraki S, Van den Braembussche R, Verstraete T, Alsalihi Z, Sugimoto K, Tomita I (2014b) Aerodynamic design optimization of a centrifugal compressor impeller based on an artificial neural network and genetic algorithm. p 65\u201377","DOI":"10.1533\/978081000342.65"},{"key":"9464_CR14","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 (2021a) Dual-surrogate-assisted cooperative particle swarm optimization for expensive multimodal problems. IEEE Trans Evolut Comput 25:794\u2013808","journal-title":"IEEE Trans Evolut Comput"},{"key":"9464_CR15","doi-asserted-by":"crossref","unstructured":"Ji X, Zhang Y, Gong D, Sun X, Guo Y (2021b) Multisurrogate-assisted multitasking particle swarm optimization for expensive multimodal problems. IEEE Trans Cybern.","DOI":"10.1109\/TEVC.2021.3064835"},{"key":"9464_CR16","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s10898-016-0428-2","volume":"67","author":"H Jie","year":"2017","unstructured":"Jie H, Wu Y, Zhao J, Ding J, Liangliang, (2017) An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems. J Global Optim 67:399\u2013423","journal-title":"J Global Optim"},{"key":"9464_CR17","doi-asserted-by":"publisher","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 9:3\u201312","journal-title":"Soft Comput"},{"key":"9464_CR18","doi-asserted-by":"publisher","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 1:61\u201370","journal-title":"Swarm Evol Comput"},{"key":"9464_CR19","doi-asserted-by":"publisher","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 Global Optim 13:455\u2013492","journal-title":"J Global Optim"},{"key":"9464_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.paerosci.2017.05.003","volume":"93","author":"Z Li","year":"2017","unstructured":"Li Z, Zheng X (2017) Review of design optimization methods for turbomachinery aerodynamics. Prog Aerosp Sci 93:1\u201323","journal-title":"Prog Aerosp Sci"},{"key":"9464_CR21","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s00158-019-02227-5","volume":"60","author":"X Li","year":"2019","unstructured":"Li X, Zhao Y, Liu Z (2019) A novel global optimization algorithm and data-mining methods for turbomachinery design. Struct Multidiscip Optim 60:581\u2013612","journal-title":"Struct Multidiscip Optim"},{"key":"9464_CR22","doi-asserted-by":"crossref","unstructured":"Li F, Gao L, Shen W, Cai X, Huang S (2020a) A surrogate-assisted offspring generation method for expensive multi-objective optimization problems. In: 2020a IEEE congress on evolutionary computation (CEC), (IEEE, 2020a), pp 1\u20138.","DOI":"10.1109\/CEC48606.2020.9185691"},{"key":"9464_CR23","doi-asserted-by":"crossref","unstructured":"Li F, Gao L, Garg A, Shen W, Huang S. (2020b) A comparative study of pre-screening strategies within a surrogate-assisted multi-objective algorithm framework for computationally expensive problems. Neural Comput Appl","DOI":"10.1007\/s00521-020-05258-y"},{"key":"9464_CR24","doi-asserted-by":"crossref","unstructured":"Li F, Gao L, Garg A, Shen W, Huang S (2021) Two infill criterion driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions. Swarm Evol Comput;60","DOI":"10.1016\/j.swevo.2020.100774"},{"key":"9464_CR25","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1080\/0305215X.2014.928816","volume":"47","author":"H Liu","year":"2015","unstructured":"Liu H, Xu S, Wang X (2015) Sequential sampling designs based on space reduction. Eng Optim 47:867\u2013884","journal-title":"Eng Optim"},{"key":"9464_CR26","volume-title":"DACE\u2014a Matlab Kriging toolbox; version 2; informatics and mathematical modelling","author":"SN Lophaven","year":"2002","unstructured":"Lophaven SN, Nielsen HB, Sondergaard J (2002) DACE\u2014a Matlab Kriging toolbox; version 2; informatics and mathematical modelling. Technical University of Denmark, Copenhagen"},{"key":"9464_CR27","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/0378-3758(94)90115-5","volume":"39","author":"J Park","year":"1994","unstructured":"Park J (1994) Optimal Latin-hypercube designs for computer experiments. J Stat Plan Infer 39:95\u2013111","journal-title":"J Stat Plan Infer"},{"key":"9464_CR28","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.neucom.2012.06.043","volume":"116","author":"M Pil\u00e1t","year":"2013","unstructured":"Pil\u00e1t M, Neruda R (2013) Aggregate meta-models for evolutionary multiobjective and many-objective optimization. Neurocomputing 116:392\u2013402","journal-title":"Neurocomputing"},{"key":"9464_CR29","doi-asserted-by":"crossref","unstructured":"Roy PC, Hussein R, Blank J, Deb K (2019) Trust-region based multi-objective optimization for low budget scenarios. In: International conference on evolutionary multi-criterion optimization, Springer, pp. 373\u2013385","DOI":"10.1007\/978-3-030-12598-1_30"},{"key":"9464_CR30","doi-asserted-by":"crossref","unstructured":"Ruan X, Li K, Derbel B, Liefooghe A (2020) Surrogate assisted evolutionary algorithm for medium scale multi-objective optimisation problems, p 560\u2013568","DOI":"10.1145\/3377930.3390191"},{"key":"9464_CR31","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1080\/03052150211751","volume":"34","author":"MJ Sasena","year":"2002","unstructured":"Sasena MJ, Papalambros P, Goovaerts P (2002) Exploration of metamodeling sampling criterion for constrained global optimization. Eng Optim 34:263\u2013278","journal-title":"Eng Optim"},{"key":"9464_CR32","doi-asserted-by":"crossref","unstructured":"Viana FAC, Gogu C, Haftka RT (2010) Making the most out of surrogate models: tricks of the trade. p 587\u2013598","DOI":"10.1115\/DETC2010-28813"},{"key":"9464_CR33","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1115\/1.2429697","volume":"129","author":"GG Wang","year":"2007","unstructured":"Wang GG, Shan S (2007) Review of metamodeling techniques in support of engineering design optimization. J Mech Design 129:370\u2013380","journal-title":"J Mech Design"},{"key":"9464_CR34","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1243\/09576509JPE201","volume":"220","author":"XF Wang","year":"2006","unstructured":"Wang XF, Xi G, Wang ZH (2006) Aerodynamic optimization design of centrifugal compressor\u2019s impeller with Kriging model. Proc Inst Mech Eng A-J Pow 220:589\u2013597","journal-title":"Proc Inst Mech Eng A-J Pow"},{"key":"9464_CR35","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/j.cma.2010.11.014","volume":"200","author":"XD Wang","year":"2011","unstructured":"Wang XD, Hirsch C, Kang S, Lacor C (2011) Multi-objective optimization of turbomachinery using improved NSGA-II and approximation model. Comput Method Appl M 200:883\u2013895","journal-title":"Comput Method Appl M"},{"key":"9464_CR36","doi-asserted-by":"publisher","first-page":"071009","DOI":"10.1115\/1.4027161","volume":"136","author":"S Xu","year":"2014","unstructured":"Xu S, Liu H, Wang X, Jiang X (2014) A robust error-pursuing sequential sampling approach for global metamodeling based on Voronoi diagram and cross validation. J Mech Design 136:071009","journal-title":"J Mech Design"},{"key":"9464_CR37","doi-asserted-by":"publisher","first-page":"106958","DOI":"10.1016\/j.oceaneng.2020.106958","volume":"198","author":"Q Yang","year":"2020","unstructured":"Yang Q, Lin Y, Guan G (2020) Improved sequential sampling for meta-modeling promotes design optimization of SWATH. Ocean Eng 198:106958","journal-title":"Ocean Eng"},{"key":"9464_CR38","doi-asserted-by":"crossref","unstructured":"Zhou Y, Lu Z (2020) An enhanced Kriging surrogate modeling technique for high-dimensional problems. Mech Syst Signal Pr 2020","DOI":"10.1016\/j.ymssp.2020.106687"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-09464-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-09464-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-09464-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T14:31:20Z","timestamp":1719412280000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-09464-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,21]]},"references-count":38,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9464"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-09464-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-442735\/v1","asserted-by":"object"}]},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,21]]},"assertion":[{"value":"14 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2023","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Informed consent was obtained from all individual participants included in the study","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}