A Stochastic Adaptive Radial Basis Function Algorithm for Costly Black-Box Optimization
In this paper, we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions. The exploration radii in local searches are generated adaptively. Each iteration point is selected from some randomly generated trial points according t...
Saved in:
| Published in | Journal of the Operations Research Society of China (Internet) Vol. 6; no. 4; pp. 587 - 609 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Beijing
Operations Research Society of China
01.12.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2194-668X 2194-6698 |
| DOI | 10.1007/s40305-018-0204-8 |
Cover
| Abstract | In this paper, we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions. The exploration radii in local searches are generated adaptively. Each iteration point is selected from some randomly generated trial points according to certain criteria. A restarting strategy is adopted to build the restarting version of the algorithm. The performance of the presented algorithm and its restarting version are tested on 13 standard numerical examples. The numerical results suggest that the algorithm and its restarting version are very effective. |
|---|---|
| AbstractList | In this paper, we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions. The exploration radii in local searches are generated adaptively. Each iteration point is selected from some randomly generated trial points according to certain criteria. A restarting strategy is adopted to build the restarting version of the algorithm. The performance of the presented algorithm and its restarting version are tested on 13 standard numerical examples. The numerical results suggest that the algorithm and its restarting version are very effective. |
| Author | Zhou, Zhe Bai, Fu-Sheng |
| Author_xml | – sequence: 1 givenname: Zhe surname: Zhou fullname: Zhou, Zhe organization: School of Mathematical Sciences, Chongqing Normal University – sequence: 2 givenname: Fu-Sheng surname: Bai fullname: Bai, Fu-Sheng email: fsbai@cqnu.edu.cn organization: School of Mathematical Sciences, Chongqing Normal University |
| BookMark | eNp1kE1LAzEQhoNUsNb-AG8Bz6tJdrNNjttiVSgU_EBvIZtN2uh2U5NUrL_elBU9eZqBed6Z4TkFg851GoBzjC4xQpOrUKAc0QxhliGCiowdgSHBvMjKkrPBb89eTsA4BFsjShilJcJD8FzBh-jUWoZoFawauY32Q8N72VjZwqkMNsD5rlPRug5W7cp5G9cbaJyHMxdiu4fTVqq3bOo-4TJlN_ZLHtgzcGxkG_T4p47A0_z6cXabLZY3d7NqkSlSspgpTDilUjNdy5w1pmasURgrjrVh6UNFTRoXhPNJbZChqCCK04lpSsNwnjf5CFz0e7feve90iOLV7XyXTgrCc8ILllOaKNxTyrsQvDZi6-1G-r3ASBwUil6hSArFQaFgKUP6TEhst9L-b_P_oW9RTXVP |
| Cites_doi | 10.1017/CBO9780511543241 10.1007/3-540-45712-7_35 10.1007/s10898-017-0599-5 10.1007/s10898-006-9040-1 10.1137/120902434 10.1137/070691814 10.1023/A:1011584207202 10.1007/s10898-005-2454-3 10.1287/ijoc.1060.0182 10.1080/0305215X.2012.687731 10.1016/S0378-3758(00)00105-1 10.1023/A:1008306431147 10.1007/s10898-004-0570-0 10.1080/02286203.2009.11442507 10.1007/978-3-0348-8696-3_14 10.1007/s10898-015-0270-y 10.1007/BF01096734 10.1002/0471722138 10.1007/978-1-4612-1478-6 10.1023/A:1011255519438 10.1007/s10898-012-9940-1 10.1007/s10898-007-9256-8 10.1016/j.cor.2010.09.013 10.1145/355744.355745 |
| ContentType | Journal Article |
| Copyright | Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature 2018. |
| Copyright_xml | – notice: Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature 2018 – notice: Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature 2018. |
| DBID | AAYXX CITATION 3V. 7RO 7XB 8AI 8FE 8FG 8FK ABJCF ABUWG AFKRA AXJJW BENPR BEZIV BGLVJ CCPQU DWQXO FREBS FRNLG HCIFZ K60 K6~ L6V M7S PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PTHSS Q9U |
| DOI | 10.1007/s40305-018-0204-8 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Asian Business Database ProQuest Central (purchase pre-March 2016) Asian Business Database (Alumni Edition) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Asian & European Business Collection ProQuest Central Business Premium Collection Technology Collection ProQuest One ProQuest Central Asian & European business collection Business Premium Collection (Alumni) SciTech Premium Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection ProQuest Engineering Collection Engineering Database (Proquest) ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection ProQuest Central Basic |
| DatabaseTitle | CrossRef ProQuest Business Collection (Alumni Edition) ProQuest One Business Technology Collection ProQuest One Academic Middle East (New) ProQuest Asian Business & Reference Asian & European Business Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Asian & European Business Collection (Alumni) ProQuest Central ProQuest One Applied & Life Sciences ProQuest Asian Business and Reference (Alumni Edition) ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Engineering Collection Business Premium Collection Engineering Database ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Business (Alumni) ProQuest One Academic ProQuest Central (Alumni) Business Premium Collection (Alumni) ProQuest One Academic (New) |
| DatabaseTitleList | ProQuest Business Collection (Alumni Edition) |
| Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Mathematics |
| EISSN | 2194-6698 |
| EndPage | 609 |
| ExternalDocumentID | 10_1007_s40305_018_0204_8 |
| GroupedDBID | -EM 0R~ 30V 4.4 406 96X AAAVM AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAZMS ABAKF ABBXA ABDZT ABECU ABFTV ABJCF ABJNI ABJOX ABKCH ABMQK ABQBU ABTEG ABTKH ABTMW ABUWG ABXPI ACAOD ACDTI ACGFS ACHSB ACIWK ACKNC ACMLO ACOKC ACPIV ACZOJ ADHHG ADHIR ADINQ ADKNI ADRFC ADURQ ADYFF ADZKW AEBTG AEFQL AEGNC AEJHL AEJRE AEMSY AEOHA AEPYU AESKC AETCA AEVLU AEXYK AFBBN AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AILAN AITGF AJBLW AJRNO AJZVZ AKLTO ALFXC ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AMYQR ANMIH ASPBG AUKKA AVWKF AVXWI AXJJW AXYYD AZFZN BAPOH BENPR BEZIV BGLVJ BGNMA CCPQU DNIVK DPUIP DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FYJPI GGCAI GGRSB GJIRD HCIFZ HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I0C IKXTQ IWAJR J-C JBSCW JCJTX JZLTJ KOV LLZTM M4Y M7S NPVJJ NQJWS NU0 O9- O93 O9J PQBIZ PQBZA PQQKQ PT4 PTHSS RLLFE ROL RSV SCL SHX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE TSG UG4 UOJIU UTJUX UZXMN VFIZW W48 ZMTXR AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB PUEGO 3V. 7RO 7XB 8AI 8FE 8FG 8FK K60 K6~ L6V PKEHL PQEST PQUKI Q9U |
| ID | FETCH-LOGICAL-c268t-c12955ae8eba38dfb88dc11c91ef8560c5f55a42997bf0f5042c957fd6f8133d3 |
| IEDL.DBID | AGYKE |
| ISSN | 2194-668X |
| IngestDate | Wed Aug 13 09:46:45 EDT 2025 Wed Oct 01 04:27:29 EDT 2025 Fri Feb 21 02:27:33 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Radial basis function Global optimization 90C56 Stochastic algorithm 90C26 90C59 Costly black-box optimization |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c268t-c12955ae8eba38dfb88dc11c91ef8560c5f55a42997bf0f5042c957fd6f8133d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2932948355 |
| PQPubID | 2043982 |
| PageCount | 23 |
| ParticipantIDs | proquest_journals_2932948355 crossref_primary_10_1007_s40305_018_0204_8 springer_journals_10_1007_s40305_018_0204_8 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20181200 2018-12-00 20181201 |
| PublicationDateYYYYMMDD | 2018-12-01 |
| PublicationDate_xml | – month: 12 year: 2018 text: 20181200 |
| PublicationDecade | 2010 |
| PublicationPlace | Beijing |
| PublicationPlace_xml | – name: Beijing – name: Heidelberg |
| PublicationTitle | Journal of the Operations Research Society of China (Internet) |
| PublicationTitleAbbrev | J. Oper. Res. Soc. China |
| PublicationYear | 2018 |
| Publisher | Operations Research Society of China Springer Nature B.V |
| Publisher_xml | – name: Operations Research Society of China – name: Springer Nature B.V |
| References | WildSMRegisRGShoemakerCAORBIT: optimization by radial basis function interpolation in trust-regionsSIAM J. Sci. Comput.200830631973219245238510.1137/070691814 BoxGEPDraperNREmpirical Model-Building and Response Surfaces1987New YorkWiley0614.62104 BuhmannMDRadial Basis Functions2003CambridgeCambridge University Press10.1017/CBO9780511543241 DixonLCWSzegöGDixonLCWSzegöGThe global optimization problem: an introductionTowards Global Optimization 21978AmsterdamNorth-Holland115 BjörkmanMHolmströmKGlobal optimization of costly nonconvex functions using radial basis functionsOptim. Eng.200014373397191732710.1023/A:1011584207202 RegisRGStochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functionsComput. Oper. Res.2011385837853273527110.1016/j.cor.2010.09.013 OeuvrayRBierlaireMBOOSTERS: a derivative-free algorithm based on radial basis functionsInt. J. Model. Simul.2009291263610.1080/02286203.2009.11442507 Oeuvray, R.: Trust-region methods based on radial basis functions with application to biomedical imaging. Ph.D. thesis, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne (2005) RegisRGShoemakerCAA quasi-multistart framework for global optimization of expensive functions using response surface methodsJ. Global Optim.20135617191753307833010.1007/s10898-012-9940-1 GutmannH-MA radial basis function method for global optimizationJ. Global Optim.2001193201227183321710.1023/A:1011255519438 PowellMJDLightWThe theory of radial basis function approximation in 1990Advances in Numerical Analysis1990OxfordOxford University Press105210 McKayMBeckmanRConoverWA Comparison of three methods for selecting values of input variables in the analysis of output from a computer codeTechnometrics1979212392465332520415.62011 WildSMShoemakerCAGlobal convergence of radial basis function trust-region algorithms for derivative-free optimizationSIAM Rev.2013552349371304992410.1137/120902434 RegisRShoemakerCAA stochastic radial basis function method for the global optimization of expensive functionsINFORMS J. Comput.200719497509236400710.1287/ijoc.1060.0182 HedayatASSloaneNJAStufkenJOrthogonal Arrays: Theory and Applications1999New YorkSpringer10.1007/978-1-4612-1478-6 JonesDRSchonlauMWelchWJEfficient global optimization of expensive black-box functionsJ. Global Optim.1998134455492167346010.1023/A:1008306431147 RegisRGShoemakerCAImproved strategies for radial basis function methods for global optimizationJ. Global Optim.2007371113135228456210.1007/s10898-006-9040-1 FriedmanJHBentelyJFinkelRAAn algorithm for finding best matches in logarithmic expected timeACM Trans. Math. Softw.1977320922610.1145/355744.355745 AkhtarTShoemakerCAMulti objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selectionJ. Global Optim.20166411732343797110.1007/s10898-015-0270-y HolmströmKAn adaptive radial basis algorithm for expensive black-box global optimizationJ. Global Optim.2008413447464242447810.1007/s10898-007-9256-8 RegisRGShoemakerCACombining radial basis function surrogates dynamic coordinate search in high dimensional expensive black-box optimizationEng. Optim.2013455529555304644610.1080/0305215X.2012.687731 PowellMJDMüllerMBuhmannMMacheDFeltenMRecent research at Cambridge on radial basis functionsNew Developments in Approximation Theory, International Series of Numerical Mathematics1999BaselBirkhauser Verlag21523210.1007/978-3-0348-8696-3_14 KhuriAICornellJAResponse Surfaces1987New YorkMarcel Dekker Inc.0632.62069 SchoenFA wide class of test functions for global optimizationJ. Global Optim.19933133137126384210.1007/BF01096734 RegisRGShoemakerCAConstrained global optimization of expensive black box functions using radial basis functionsJ. Global Optim.200531153171214217110.1007/s10898-004-0570-0 SpallJCIntroduction to Stochastic Search and Optimization2003HobokenWiley10.1002/0471722138 HuangDAllenTTNotzWIZengNGlobal optimization of stochastic black-box systems via sequential kriging meta-modelsJ. Global Optim.2006343441466222228310.1007/s10898-005-2454-3 MyersRHMontgomeryDCResponse Surface Methodology: Process and Product Optimization Using Designed Experiments1995New YorkWiley1161.62392 ZhouZBaiFAn adaptive framework for expensive black-box global optimization based on radial basis function interpolationJ. Global Optim.2018704757781378048110.1007/s10898-017-0599-5 EmmerichMGiotisAÖzdemirMBäckTGiannakoglouKMereloJJAdamidisPBeyerHGMetamodel-assisted evolution strategiesParallel Problem Solving from Nature VII2002BerlinSpringer36137010.1007/3-540-45712-7_35 YeKQLiWSudjiantoAAlgorithmic construction of optimal symmetric latin hypercube designsJ. Stat. Plan. Inference2000901145159179158610.1016/S0378-3758(00)00105-1 M McKay (204_CR25) 1979; 21 RG Regis (204_CR21) 2013; 56 JC Spall (204_CR29) 2003 DR Jones (204_CR6) 1998; 13 JH Friedman (204_CR24) 1977; 3 R Oeuvray (204_CR23) 2009; 29 RH Myers (204_CR3) 1995 LCW Dixon (204_CR30) 1978 F Schoen (204_CR31) 1993; 3 MD Buhmann (204_CR7) 2003 Z Zhou (204_CR18) 2018; 70 T Akhtar (204_CR15) 2016; 64 AS Hedayat (204_CR26) 1999 D Huang (204_CR5) 2006; 34 KQ Ye (204_CR27) 2000; 90 GEP Box (204_CR1) 1987 R Regis (204_CR16) 2007; 19 204_CR22 M Emmerich (204_CR4) 2002 RG Regis (204_CR28) 2011; 38 SM Wild (204_CR13) 2008; 30 RG Regis (204_CR17) 2013; 45 MJD Powell (204_CR19) 1999 AI Khuri (204_CR2) 1987 RG Regis (204_CR11) 2007; 37 RG Regis (204_CR12) 2005; 31 H-M Gutmann (204_CR9) 2001; 19 MJD Powell (204_CR8) 1990 K Holmström (204_CR10) 2008; 41 M Björkman (204_CR20) 2000; 1 SM Wild (204_CR14) 2013; 55 |
| References_xml | – reference: BuhmannMDRadial Basis Functions2003CambridgeCambridge University Press10.1017/CBO9780511543241 – reference: MyersRHMontgomeryDCResponse Surface Methodology: Process and Product Optimization Using Designed Experiments1995New YorkWiley1161.62392 – reference: RegisRGShoemakerCAImproved strategies for radial basis function methods for global optimizationJ. Global Optim.2007371113135228456210.1007/s10898-006-9040-1 – reference: SchoenFA wide class of test functions for global optimizationJ. Global Optim.19933133137126384210.1007/BF01096734 – reference: GutmannH-MA radial basis function method for global optimizationJ. Global Optim.2001193201227183321710.1023/A:1011255519438 – reference: WildSMShoemakerCAGlobal convergence of radial basis function trust-region algorithms for derivative-free optimizationSIAM Rev.2013552349371304992410.1137/120902434 – reference: PowellMJDLightWThe theory of radial basis function approximation in 1990Advances in Numerical Analysis1990OxfordOxford University Press105210 – reference: FriedmanJHBentelyJFinkelRAAn algorithm for finding best matches in logarithmic expected timeACM Trans. Math. Softw.1977320922610.1145/355744.355745 – reference: KhuriAICornellJAResponse Surfaces1987New YorkMarcel Dekker Inc.0632.62069 – reference: SpallJCIntroduction to Stochastic Search and Optimization2003HobokenWiley10.1002/0471722138 – reference: RegisRGShoemakerCAA quasi-multistart framework for global optimization of expensive functions using response surface methodsJ. Global Optim.20135617191753307833010.1007/s10898-012-9940-1 – reference: Oeuvray, R.: Trust-region methods based on radial basis functions with application to biomedical imaging. Ph.D. thesis, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne (2005) – reference: ZhouZBaiFAn adaptive framework for expensive black-box global optimization based on radial basis function interpolationJ. Global Optim.2018704757781378048110.1007/s10898-017-0599-5 – reference: WildSMRegisRGShoemakerCAORBIT: optimization by radial basis function interpolation in trust-regionsSIAM J. Sci. Comput.200830631973219245238510.1137/070691814 – reference: HuangDAllenTTNotzWIZengNGlobal optimization of stochastic black-box systems via sequential kriging meta-modelsJ. Global Optim.2006343441466222228310.1007/s10898-005-2454-3 – reference: McKayMBeckmanRConoverWA Comparison of three methods for selecting values of input variables in the analysis of output from a computer codeTechnometrics1979212392465332520415.62011 – reference: JonesDRSchonlauMWelchWJEfficient global optimization of expensive black-box functionsJ. Global Optim.1998134455492167346010.1023/A:1008306431147 – reference: YeKQLiWSudjiantoAAlgorithmic construction of optimal symmetric latin hypercube designsJ. Stat. Plan. Inference2000901145159179158610.1016/S0378-3758(00)00105-1 – reference: EmmerichMGiotisAÖzdemirMBäckTGiannakoglouKMereloJJAdamidisPBeyerHGMetamodel-assisted evolution strategiesParallel Problem Solving from Nature VII2002BerlinSpringer36137010.1007/3-540-45712-7_35 – reference: HedayatASSloaneNJAStufkenJOrthogonal Arrays: Theory and Applications1999New YorkSpringer10.1007/978-1-4612-1478-6 – reference: DixonLCWSzegöGDixonLCWSzegöGThe global optimization problem: an introductionTowards Global Optimization 21978AmsterdamNorth-Holland115 – reference: HolmströmKAn adaptive radial basis algorithm for expensive black-box global optimizationJ. Global Optim.2008413447464242447810.1007/s10898-007-9256-8 – reference: BjörkmanMHolmströmKGlobal optimization of costly nonconvex functions using radial basis functionsOptim. Eng.200014373397191732710.1023/A:1011584207202 – reference: RegisRGStochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functionsComput. Oper. Res.2011385837853273527110.1016/j.cor.2010.09.013 – reference: PowellMJDMüllerMBuhmannMMacheDFeltenMRecent research at Cambridge on radial basis functionsNew Developments in Approximation Theory, International Series of Numerical Mathematics1999BaselBirkhauser Verlag21523210.1007/978-3-0348-8696-3_14 – reference: RegisRGShoemakerCACombining radial basis function surrogates dynamic coordinate search in high dimensional expensive black-box optimizationEng. Optim.2013455529555304644610.1080/0305215X.2012.687731 – reference: BoxGEPDraperNREmpirical Model-Building and Response Surfaces1987New YorkWiley0614.62104 – reference: RegisRGShoemakerCAConstrained global optimization of expensive black box functions using radial basis functionsJ. Global Optim.200531153171214217110.1007/s10898-004-0570-0 – reference: AkhtarTShoemakerCAMulti objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selectionJ. Global Optim.20166411732343797110.1007/s10898-015-0270-y – reference: RegisRShoemakerCAA stochastic radial basis function method for the global optimization of expensive functionsINFORMS J. Comput.200719497509236400710.1287/ijoc.1060.0182 – reference: OeuvrayRBierlaireMBOOSTERS: a derivative-free algorithm based on radial basis functionsInt. J. Model. Simul.2009291263610.1080/02286203.2009.11442507 – volume-title: Radial Basis Functions year: 2003 ident: 204_CR7 doi: 10.1017/CBO9780511543241 – volume-title: Response Surface Methodology: Process and Product Optimization Using Designed Experiments year: 1995 ident: 204_CR3 – start-page: 361 volume-title: Parallel Problem Solving from Nature VII year: 2002 ident: 204_CR4 doi: 10.1007/3-540-45712-7_35 – volume: 70 start-page: 757 issue: 4 year: 2018 ident: 204_CR18 publication-title: J. Global Optim. doi: 10.1007/s10898-017-0599-5 – volume-title: Empirical Model-Building and Response Surfaces year: 1987 ident: 204_CR1 – volume: 37 start-page: 113 issue: 1 year: 2007 ident: 204_CR11 publication-title: J. Global Optim. doi: 10.1007/s10898-006-9040-1 – volume: 55 start-page: 349 issue: 2 year: 2013 ident: 204_CR14 publication-title: SIAM Rev. doi: 10.1137/120902434 – volume: 30 start-page: 3197 issue: 6 year: 2008 ident: 204_CR13 publication-title: SIAM J. Sci. Comput. doi: 10.1137/070691814 – volume: 1 start-page: 373 issue: 4 year: 2000 ident: 204_CR20 publication-title: Optim. Eng. doi: 10.1023/A:1011584207202 – volume: 34 start-page: 441 issue: 3 year: 2006 ident: 204_CR5 publication-title: J. Global Optim. doi: 10.1007/s10898-005-2454-3 – volume: 19 start-page: 497 year: 2007 ident: 204_CR16 publication-title: INFORMS J. Comput. doi: 10.1287/ijoc.1060.0182 – volume: 45 start-page: 529 issue: 5 year: 2013 ident: 204_CR17 publication-title: Eng. Optim. doi: 10.1080/0305215X.2012.687731 – volume: 90 start-page: 145 issue: 1 year: 2000 ident: 204_CR27 publication-title: J. Stat. Plan. Inference doi: 10.1016/S0378-3758(00)00105-1 – volume: 13 start-page: 455 issue: 4 year: 1998 ident: 204_CR6 publication-title: J. Global Optim. doi: 10.1023/A:1008306431147 – volume: 31 start-page: 153 year: 2005 ident: 204_CR12 publication-title: J. Global Optim. doi: 10.1007/s10898-004-0570-0 – volume: 21 start-page: 239 year: 1979 ident: 204_CR25 publication-title: Technometrics – volume: 29 start-page: 26 issue: 1 year: 2009 ident: 204_CR23 publication-title: Int. J. Model. Simul. doi: 10.1080/02286203.2009.11442507 – start-page: 215 volume-title: New Developments in Approximation Theory, International Series of Numerical Mathematics year: 1999 ident: 204_CR19 doi: 10.1007/978-3-0348-8696-3_14 – ident: 204_CR22 – volume: 64 start-page: 17 issue: 1 year: 2016 ident: 204_CR15 publication-title: J. Global Optim. doi: 10.1007/s10898-015-0270-y – volume: 3 start-page: 133 year: 1993 ident: 204_CR31 publication-title: J. Global Optim. doi: 10.1007/BF01096734 – start-page: 105 volume-title: Advances in Numerical Analysis year: 1990 ident: 204_CR8 – volume-title: Response Surfaces year: 1987 ident: 204_CR2 – volume-title: Introduction to Stochastic Search and Optimization year: 2003 ident: 204_CR29 doi: 10.1002/0471722138 – start-page: 1 volume-title: Towards Global Optimization 2 year: 1978 ident: 204_CR30 – volume-title: Orthogonal Arrays: Theory and Applications year: 1999 ident: 204_CR26 doi: 10.1007/978-1-4612-1478-6 – volume: 19 start-page: 201 issue: 3 year: 2001 ident: 204_CR9 publication-title: J. Global Optim. doi: 10.1023/A:1011255519438 – volume: 56 start-page: 1719 year: 2013 ident: 204_CR21 publication-title: J. Global Optim. doi: 10.1007/s10898-012-9940-1 – volume: 41 start-page: 447 issue: 3 year: 2008 ident: 204_CR10 publication-title: J. Global Optim. doi: 10.1007/s10898-007-9256-8 – volume: 38 start-page: 837 issue: 5 year: 2011 ident: 204_CR28 publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2010.09.013 – volume: 3 start-page: 209 year: 1977 ident: 204_CR24 publication-title: ACM Trans. Math. Softw. doi: 10.1145/355744.355745 |
| SSID | ssib052855601 ssj0002962227 |
| Score | 2.0666072 |
| Snippet | In this paper, we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions. The... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 587 |
| SubjectTerms | Adaptive algorithms Algorithms Black boxes Global optimization Iterative methods Management Science Mathematics Mathematics and Statistics Operations Research Optimization Radial basis function Restarting |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3fb9MwED6V9gUepo2B6OgmP_A0ZNGktuM8oKmdWlWT1qGOir5Fjn8wpLYpNEjw33OXJSubBM9WLOWzffedz_cdwDs0-1YIr3iwScKFjzXXTjtuBn2d94UX3tHVwPVMTRfiaimXLZg1tTD0rLKxiZWhdoWlO_IP6JbiVCBfkBfb75y6RlF2tWmhYerWCu5jJTH2DDoxKWO1oTMazz7Nmx0mYy2lqhOtZKvjVFExKHWgw2ieK6WXTeqT6usEHQeMtjWnGlKuHzuvPSN9kkStfNPkEA5qUsmG97vgCFp-8xJe_CU1eAxfhuy2LOydIV1mNnRmS3aOzUmaYMVGZvdtxybo42id2HD1FX-9vFszpLTsstiVq9-suurjo-IXu8Fv13X95itYTMafL6e8bqrAbax0yS06eCmN1z43A-1CrrWzUWTTyAeN2FgZcJi8VJKHfpB4qG0qk-BU0BjPusFraG-KjX8DDMkK0h9KtRoj0pCnOG1kMQITITepUV04b9DKtvfaGdmDSnIFbYbQZgRtprvQa_DM6mO0y_aL3oX3Dcb74X9OdvL_yd7C85gWtXqV0oN2-eOnP0VuUeZn9Yb5AxkfyHg priority: 102 providerName: ProQuest |
| Title | A Stochastic Adaptive Radial Basis Function Algorithm for Costly Black-Box Optimization |
| URI | https://link.springer.com/article/10.1007/s40305-018-0204-8 https://www.proquest.com/docview/2932948355 |
| Volume | 6 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 2194-6698 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002962227 issn: 2194-668X databaseCode: AFBBN dateStart: 20130301 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 2194-6698 dateEnd: 20241101 omitProxy: true ssIdentifier: ssj0002962227 issn: 2194-668X databaseCode: BENPR dateStart: 20130301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 2194-6698 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002962227 issn: 2194-668X databaseCode: AGYKE dateStart: 20130101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT-MwEB5BucBhgWUR3QXkA6dFRm1qu5NjiloQCHbFQ3RPkePYgCgNIkFa-PWM06Q8BAdOUeTEij2TmW88ns8AW2T2jRBWcWe6XS5sgBxTTLnutDBpCSts6pcGjo7V_rk4GMphVced17vd65RkaamnxW7C6yaFvsh9QSfHWZgr6bYaMBft_Tvs12okA5RSVdlUb5CDUPmKT3_MHIXsXCkc1vnNj_p966FeYOe7TGnpgAaLcFZ_-mTfyc3OQ5HsmKd3rI5fHNsSfKsAKYsmGrQMM3b8HRZe0RTS3dGU2zVfgYuInRaZudKe4ZlFqb7zFpOdeJKDEevp_DpnA_KWXuIsGl1m99fF1S0jcMx2s7wYPbJy0ZD3sv_sD717W1WC_oDzQf9sd59XxzNwEygsuCGoIKW2aBPdwdQliKlpt03Ytg5JAEY6avb-rpu4lpNkHkwouy5VDikyTjur0BhnY7sGjGAPASmftNVahC4Jqdu2oVhOuESHWjXhdy2S-G7CwhFP-ZbLuYtp7mI_dzE2Yb0WWlz9kHlMqCYIBcFN2YTtWgYvzZ929vNLT_-C-cALsdzusg6N4v7BbhBoKZJNmMXB3malqnTt9Y__njwDBm_iKw |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxEB6V9gAcUHmJQCk-wAVkkd3Yjn2oUFIapbQNqLQit63XD4qUZgO7CPrn-ts6s90lgAS3ni3P4fN45huPZwbgOZp9J0RQPLp-n4uQaq699tz2ujrviiCCp6eBg4kaH4t3UzldgYu2Foa-VbY2sTbUvnD0Rv4a3VJqBPIF-WbxldPUKMqutiM0bDNawW_VLcaawo69cP4DQ7hya_ctnveLNB3tHG2PeTNlgLtU6Yo79HhS2qBDbnvax1xr75LEmSREjXzAyYjLZLb7eexGiVrujOxHr6LGAM_3UO4NWBM9YTD4WxvuTD4cthotUy2lahK75BtSo6j4lCbeJUZwpfS0TbVSPZ-g64fRveZUs8r1n85yyYD_StrWvnC0DncaEssGV1p3F1bC_B7c_q214X34NGAfq8KdWuoDzQbeLsiuskNqhTBjQ1t-KdkIfSrpBRvMPiPU1ekZQwrNtouymp2z-mmRD4uf7D3uPWvqRR_A8bXA-xBW58U8PAKG5AjpFqV2rRUm5gbFJg4jPhFza6zqwMsWrWxx1asj-9WVuYY2Q2gzgjbTHdho8cyaa1tmSyXrwKsW4-XyP4U9_r-wZ3BzfHSwn-3vTvaewK2UDrj-EbMBq9W37-Ep8poq32yUh8HJdevrJXjCBds |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB5RkKpyoA9asXRpfeipyLCbtR3nGB4LLYWitqjLKXX8AMTuZrXJSi2_nnEeS4vooeoxcmLJnsnMN56ZzwDv0OxrxqygTochZTaQVBppqOp1ZNphllnjjwaOT8ThGfs44IP6ntO8qXZvUpJVT4NnaRoX2xPjtueNb8zrKYbBkvrmTiofwRJGJiEq-lJ8cH6036gUDyTnos6seuMcRMJ3f_or5zB8p0LIQZPrfGjeP73VHQS9lzUtnVH_KfxollHVoFxvzYp0S9_cY3j8j3U-g5UaqJK40qznsGDHL2D5N_pCfDqec77mq_A9Jl-LTF8qz_xMYqMm3pKSL578YEh2VH6Vkz56Ua8JJB5eZNOr4nJEEDST3Swvhr9IeZhId7Kf5DN-O6o7RF_CWX__2-4hra9toDoQsqAaIQTnykqbqp40LpXS6G5XR13rJApDc4fD3g-Gqes4jmZDRzx0RjiJEbPpvYLFcTa2a0AQDiHA8slcpVjk0gin7WqM8ZhLVaREC9434kkmFTtHMudhLvcuwb1L_N4lsgXtRoBJ_aPmCaKdIGIIQ3kLNht53A3_dbL1f3r7LTw-3esnnz6cHL2GJ4GXZ1kR04bFYjqzG4hrivRNrbu34ejrbg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Stochastic+Adaptive+Radial+Basis+Function+Algorithm+for+Costly+Black-Box+Optimization&rft.jtitle=Journal+of+the+Operations+Research+Society+of+China+%28Internet%29&rft.au=Zhou%2C+Zhe&rft.au=Bai%2C+Fu-Sheng&rft.date=2018-12-01&rft.issn=2194-668X&rft.eissn=2194-6698&rft.volume=6&rft.issue=4&rft.spage=587&rft.epage=609&rft_id=info:doi/10.1007%2Fs40305-018-0204-8&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s40305_018_0204_8 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2194-668X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2194-668X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2194-668X&client=summon |