{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T23:29:43Z","timestamp":1756682983856},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T00:00:00Z","timestamp":1664150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T00:00:00Z","timestamp":1664150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China","award":["2018AAA0101301"],"award-info":[{"award-number":["2018AAA0101301"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s12530-022-09470-0","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T18:05:25Z","timestamp":1664215525000},"page":"839-858","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A trust region based local Bayesian optimization without exhausted optimization of acquisition function"],"prefix":"10.1007","volume":"14","author":[{"given":"Qingxia","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anbing","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhong","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"9470_CR1","first-page":"5","volume":"23","author":"B Beldjilali","year":"2020","unstructured":"Beldjilali B, Benadda B, Sadouni Z (2020) Vehicles circuits optimization by combining GPS \/ GSM information with metaheuristic algorithms. Romanian J Inf Sci Technol 23:5\u201317","journal-title":"Romanian J Inf Sci Technol"},{"key":"9470_CR2","doi-asserted-by":"crossref","unstructured":"Binois M, Ginsbourger D, Roustant O (2015) AWarped Kernel improving robustness in bayesian optimization via random embeddings. LION.","DOI":"10.1007\/978-3-319-19084-6_28"},{"key":"9470_CR3","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10898-019-00839-1","volume":"76","author":"M Binois","year":"2020","unstructured":"Binois M, Ginsbourger D, Roustant O (2020) On the choice of the low-dimensional domain for global optimization via random embeddings. J Global Optim 76:69\u201390","journal-title":"J Global Optim"},{"key":"9470_CR4","unstructured":"Brochu E, Freitas ND, Ghosh A (2007) Active preference learning with discrete choice data. NIPS"},{"key":"9470_CR5","unstructured":"Brochu E, Cora VM, Freitas ND (2010) A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. ArXiv, https:\/\/arxiv.org\/abs\/1012.2599"},{"key":"9470_CR6","unstructured":"Chen B, Castro RM, Krause A (2012) Joint optimization and variable selection of high-dimensional gaussian processes. ICML"},{"key":"9470_CR7","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/BF02614326","volume":"79","author":"AR Conn","year":"1997","unstructured":"Conn AR, Scheinberg K, Toint PL (1997) Recent progress in unconstrained nonlinear optimization without derivatives. Math Program 79:397\u2013414","journal-title":"Math Program"},{"key":"9470_CR8","doi-asserted-by":"crossref","unstructured":"Contal E, Buffoni D, Robicquet A, Vayatis N (2013) Parallel gaussian process optimization with upper confidence bound and pure exploration. ECML\/PKDD","DOI":"10.1007\/978-3-642-40988-2_15"},{"key":"9470_CR9","first-page":"3873","volume":"15","author":"T Desautels","year":"2012","unstructured":"Desautels T, Krause A, Burdick JW (2012) Parallelizing exploration-exploitation tradeoffs with gaussian process bandit optimization. J Mach Learn Res 15:3873\u20133923","journal-title":"J Mach Learn Res"},{"key":"9470_CR10","unstructured":"Eriksson D, Pearce M, Gardner JR, Turner RD, Poloczek M (2019) Scalable global optimization via local bayesian optimization. NeurIPS"},{"key":"9470_CR11","unstructured":"Gardner JR, Guo C, Weinberger KQ, Garnett R, Grosse RB (2017) Discovering and exploiting additive structure for bayesian optimization. AISTATS"},{"key":"9470_CR12","unstructured":"Gardner JR, Pleiss G, Bindel DS, Weinberger KQ, Wilson AG (2018) GPyTorch: blackbox matrix-matrix gaussian process inference with GPU acceleration. NeurIPS"},{"key":"9470_CR13","doi-asserted-by":"crossref","unstructured":"Ginsbourger D, Riche RL, Carraro L (2010) Kriging is well-suited to parallelize optimization","DOI":"10.1007\/978-3-642-10701-6_6"},{"key":"9470_CR14","unstructured":"Hennig P, Schuler CJ (2012) Entropy search for information-efficient global optimization. ArXiv, https:\/\/arxiv.org\/abs\/1112.1217"},{"key":"9470_CR15","unstructured":"Hern\u00e1ndez-Lobato J, Hoffman MW, Ghahramani Z (2014) Predictive entropy search for efficient global optimization of black-box functions. NIPS"},{"key":"9470_CR16","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/BF00941892","volume":"79","author":"DR Jones","year":"1993","unstructured":"Jones DR, Perttunen CD, Stuckman BE (1993) Lipschitzian optimization without the Lipschitz constant. J Optim Theory Appl 79:157\u2013181","journal-title":"J Optim Theory Appl"},{"key":"9470_CR17","unstructured":"Kandasamy K, Schneider JG, P\u00f3czos B (2015) High dimensional bayesian optimisation and bandits via additive models. ICML"},{"key":"9470_CR18","doi-asserted-by":"crossref","unstructured":"Kocsis L, Szepesvari C (2006) Bandit based monte-carlo planning. ECML","DOI":"10.1007\/11871842_29"},{"key":"9470_CR19","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1115\/1.3653121","volume":"86","author":"HJ Kushner","year":"1964","unstructured":"Kushner HJ (1964) A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J Basic Eng 86:97\u2013106","journal-title":"J Basic Eng"},{"key":"9470_CR20","doi-asserted-by":"publisher","first-page":"107266","DOI":"10.1016\/j.jcsr.2022.107266","volume":"193","author":"L La\u00edm","year":"2022","unstructured":"La\u00edm L, Mendes J, Craveiro H\u00e9lder D et al (2022) Structural optimization of closed built-up cold-formed steel columns. J Constr Steel Res 193:107266","journal-title":"J Constr Steel Res"},{"key":"9470_CR21","unstructured":"Lakshminarayanan B, Roy DM, Teh YW (2016) Mondrian forests for large-scale regression when uncertainty matters. ArXiv, https:\/\/arxiv.org\/abs\/1506.03805"},{"key":"9470_CR22","volume-title":"Introduction to stochastic processes","author":"GF Lawler","year":"2006","unstructured":"Lawler GF (2006) Introduction to stochastic processes, 2nd edn. Houghton Mifflin Co., Boston","edition":"2"},{"key":"9470_CR23","doi-asserted-by":"crossref","unstructured":"Marmin S, Chevalier C, Ginsbourger D (2015) Differentiating the multipoint expected improvement for optimal batch design. MOD.","DOI":"10.1007\/978-3-319-27926-8_4"},{"key":"9470_CR24","unstructured":"McIntire M, Ratner D, Ermon S (2016) Sparse gaussian processes for bayesian optimization. UAI"},{"key":"9470_CR25","unstructured":"Mockus J (1977) On bayesian methods for seeking the extremum and their application. IFIP Congress"},{"key":"9470_CR26","unstructured":"Mutn\u00fd M, Krause A (2018) Efficient high dimensional bayesian optimization with additivity and quadrature fourier features. NeurIPS."},{"key":"9470_CR27","unstructured":"Nayebi A, Munteanu A, Poloczek M (2019) A framework for bayesian optimization in embedded subspaces. ICML"},{"key":"9470_CR28","doi-asserted-by":"crossref","unstructured":"Pozna C, Precup R-E, Horvath E et al (2022) Hybrid particle filter-particle swarm optimization algorithm and application to fuzzy controlled servo systems. IEEE Transactions on Fuzzy Systems","DOI":"10.1109\/TFUZZ.2022.3146986"},{"key":"9470_CR29","unstructured":"Rasmussen CE, Williams CK (2009) Gaussian processes for machine learning. Adaptive computation and machine learning"},{"key":"9470_CR30","unstructured":"Rolland P, Scarlett J, Bogunovic I, Cevher V (2018) High-dimensional bayesian optimization via additive models with overlapping groups. ArXiv, https:\/\/arxiv.org\/abs\/1802.07028"},{"key":"9470_CR31","doi-asserted-by":"crossref","unstructured":"Ross SM (1985) Stochastic processes, 2nd ed. J Am Stat Assoc 80(389)","DOI":"10.2307\/2288101"},{"key":"9470_CR32","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1137\/100801275","volume":"21","author":"WR Scott","year":"2011","unstructured":"Scott WR, Frazier P, Powell WB (2011) The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression. SIAM J Optim 21:996\u20131026","journal-title":"SIAM J Optim"},{"key":"9470_CR33","unstructured":"Shah A, Ghahramani Z (2015) Parallel predictive entropy search for batch global optimization of expensive objective functions. NIPS"},{"key":"9470_CR34","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2016","unstructured":"Shahriari B, Swersky K, Wang Z, Adams RP, Freitas ND (2016) Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 104:148\u2013175","journal-title":"Proc IEEE"},{"key":"9470_CR35","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. NIPS"},{"key":"9470_CR36","unstructured":"Snoek J, Rippel O, Swersky K, Kiros R, Satish N, Sundaram N, Patwary MM, Prabhat, Adams RP (2015a) Scalable bayesian optimization using deep neural networks. ICML"},{"key":"9470_CR37","unstructured":"Snoek J, Rippel O, Swersky K, Kiros R, Satish N, Sundaram N, Patwary MM, Prabhat, Adams RP (2015b). Scalable bayesian optimization using deep neural networks. ICML"},{"key":"9470_CR38","unstructured":"Srinivas N, Krause A, Kakade SM, Seeger MW (2010) Gaussian process optimization in the bandit setting: No Regret and Experimental Design. ICML"},{"key":"9470_CR39","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1093\/biomet\/25.3-4.285","volume":"25","author":"WR Thompson","year":"1933","unstructured":"Thompson WR (1933) On the likelihood that one unknown probability exceeds another in view of the evidence of two sampleS. Biometrika 25:285\u2013294","journal-title":"Biometrika"},{"key":"9470_CR40","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s10898-008-9354-2","volume":"44","author":"J Villemonteix","year":"2009","unstructured":"Villemonteix J, V\u00e1zquez E, Walter E (2009) An informational approach to the global optimization of expensive-to-evaluate functions. J Global Optim 44:509\u2013534","journal-title":"J Global Optim"},{"key":"9470_CR41","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1613\/jair.4806","volume":"55","author":"Z Wang","year":"2016","unstructured":"Wang Z, Zoghi M, Hutter F, Matheson D, Freitas ND (2016) Bayesian optimization in a billion dimensions via random embeddings. J Artif Intell Res 55:361\u2013387","journal-title":"J Artif Intell Res"},{"key":"9470_CR42","unstructured":"Wang Z, Li C, Jegelka S, Kohli P (2017) Batched high-dimensional Bayesian optimization via structural kernel learning. In: International conference on machine learning (ICML)"},{"key":"9470_CR43","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1287\/opre.2019.1966","volume":"68","author":"J Wang","year":"2020","unstructured":"Wang J, Clark SC, Liu E, Frazier P (2020) Parallel bayesian global optimization of expensive functions. Oper Res 68:1850\u20131865","journal-title":"Oper Res"},{"key":"9470_CR44","unstructured":"Wang Z, Gehring C, Kohli P, Jegelka S (2018a) Batched Large-scale Bayesian Optimization in High-dimensional Spaces. ArXiv, https:\/\/arxiv.org\/abs\/1706.01445"},{"key":"9470_CR45","unstructured":"Wang Z, Gehring C, Kohli P, Jegelka S (2018b) Batched largescale Bayesian optimization in highdimensional spaces. In: International conference on artificial intelligence and statistics, pp 745\u2013754"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-022-09470-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-022-09470-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-022-09470-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T04:16:38Z","timestamp":1695874598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-022-09470-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,26]]},"references-count":45,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["9470"],"URL":"https:\/\/doi.org\/10.1007\/s12530-022-09470-0","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,26]]},"assertion":[{"value":"10 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 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 declare that they have non-financial and no conflicts of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}