A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization
Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with dif...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yu, Mingyuan [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Structural and multidisciplinary optimization - Springer Berlin Heidelberg, 2000, 61(2019), 2 vom: 07. Nov., Seite 711-729 |
|---|---|
| Übergeordnetes Werk: |
volume:61 ; year:2019 ; number:2 ; day:07 ; month:11 ; pages:711-729 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00158-019-02391-8 |
|---|
| Katalog-ID: |
OLC2051791325 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2051791325 | ||
| 003 | DE-627 | ||
| 005 | 20230401065341.0 | ||
| 007 | tu | ||
| 008 | 200820s2019 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00158-019-02391-8 |2 doi | |
| 035 | |a (DE-627)OLC2051791325 | ||
| 035 | |a (DE-He213)s00158-019-02391-8-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 510 |q VZ |
| 084 | |a 11 |2 ssgn | ||
| 084 | |a 50.03$jMethoden und Techniken der Ingenieurwissenschaften |2 bkl | ||
| 100 | 1 | |a Yu, Mingyuan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag GmbH Germany, part of Springer Nature 2019 | ||
| 520 | |a Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. | ||
| 650 | 4 | |a Evolutionary algorithm | |
| 650 | 4 | |a Adaptive surrogate model | |
| 650 | 4 | |a Expensive optimization | |
| 650 | 4 | |a Reliability | |
| 700 | 1 | |a Li, Xia |4 aut | |
| 700 | 1 | |a Liang, Jing |0 (orcid)0000-0003-0811-0223 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Structural and multidisciplinary optimization |d Springer Berlin Heidelberg, 2000 |g 61(2019), 2 vom: 07. Nov., Seite 711-729 |w (DE-627)312415958 |w (DE-600)2009366-4 |w (DE-576)090895207 |x 1615-147X |7 nnns |
| 773 | 1 | 8 | |g volume:61 |g year:2019 |g number:2 |g day:07 |g month:11 |g pages:711-729 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00158-019-02391-8 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_4277 | ||
| 936 | b | k | |a 50.03$jMethoden und Techniken der Ingenieurwissenschaften |q VZ |0 181571455 |0 (DE-625)181571455 |
| 951 | |a AR | ||
| 952 | |d 61 |j 2019 |e 2 |b 07 |c 11 |h 711-729 | ||
| author_variant |
m y my x l xl j l jl |
|---|---|
| matchkey_str |
article:1615147X:2019----::dnmcurgtasseeouinragrtmrmwrfrxesv |
| hierarchy_sort_str |
2019 |
| bklnumber |
50.03$jMethoden und Techniken der Ingenieurwissenschaften |
| publishDate |
2019 |
| allfields |
10.1007/s00158-019-02391-8 doi (DE-627)OLC2051791325 (DE-He213)s00158-019-02391-8-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn 50.03$jMethoden und Techniken der Ingenieurwissenschaften bkl Yu, Mingyuan verfasserin aut A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. Evolutionary algorithm Adaptive surrogate model Expensive optimization Reliability Li, Xia aut Liang, Jing (orcid)0000-0003-0811-0223 aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 2000 61(2019), 2 vom: 07. Nov., Seite 711-729 (DE-627)312415958 (DE-600)2009366-4 (DE-576)090895207 1615-147X nnns volume:61 year:2019 number:2 day:07 month:11 pages:711-729 https://doi.org/10.1007/s00158-019-02391-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 50.03$jMethoden und Techniken der Ingenieurwissenschaften VZ 181571455 (DE-625)181571455 AR 61 2019 2 07 11 711-729 |
| spelling |
10.1007/s00158-019-02391-8 doi (DE-627)OLC2051791325 (DE-He213)s00158-019-02391-8-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn 50.03$jMethoden und Techniken der Ingenieurwissenschaften bkl Yu, Mingyuan verfasserin aut A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. Evolutionary algorithm Adaptive surrogate model Expensive optimization Reliability Li, Xia aut Liang, Jing (orcid)0000-0003-0811-0223 aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 2000 61(2019), 2 vom: 07. Nov., Seite 711-729 (DE-627)312415958 (DE-600)2009366-4 (DE-576)090895207 1615-147X nnns volume:61 year:2019 number:2 day:07 month:11 pages:711-729 https://doi.org/10.1007/s00158-019-02391-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 50.03$jMethoden und Techniken der Ingenieurwissenschaften VZ 181571455 (DE-625)181571455 AR 61 2019 2 07 11 711-729 |
| allfields_unstemmed |
10.1007/s00158-019-02391-8 doi (DE-627)OLC2051791325 (DE-He213)s00158-019-02391-8-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn 50.03$jMethoden und Techniken der Ingenieurwissenschaften bkl Yu, Mingyuan verfasserin aut A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. Evolutionary algorithm Adaptive surrogate model Expensive optimization Reliability Li, Xia aut Liang, Jing (orcid)0000-0003-0811-0223 aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 2000 61(2019), 2 vom: 07. Nov., Seite 711-729 (DE-627)312415958 (DE-600)2009366-4 (DE-576)090895207 1615-147X nnns volume:61 year:2019 number:2 day:07 month:11 pages:711-729 https://doi.org/10.1007/s00158-019-02391-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 50.03$jMethoden und Techniken der Ingenieurwissenschaften VZ 181571455 (DE-625)181571455 AR 61 2019 2 07 11 711-729 |
| allfieldsGer |
10.1007/s00158-019-02391-8 doi (DE-627)OLC2051791325 (DE-He213)s00158-019-02391-8-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn 50.03$jMethoden und Techniken der Ingenieurwissenschaften bkl Yu, Mingyuan verfasserin aut A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. Evolutionary algorithm Adaptive surrogate model Expensive optimization Reliability Li, Xia aut Liang, Jing (orcid)0000-0003-0811-0223 aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 2000 61(2019), 2 vom: 07. Nov., Seite 711-729 (DE-627)312415958 (DE-600)2009366-4 (DE-576)090895207 1615-147X nnns volume:61 year:2019 number:2 day:07 month:11 pages:711-729 https://doi.org/10.1007/s00158-019-02391-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 50.03$jMethoden und Techniken der Ingenieurwissenschaften VZ 181571455 (DE-625)181571455 AR 61 2019 2 07 11 711-729 |
| allfieldsSound |
10.1007/s00158-019-02391-8 doi (DE-627)OLC2051791325 (DE-He213)s00158-019-02391-8-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn 50.03$jMethoden und Techniken der Ingenieurwissenschaften bkl Yu, Mingyuan verfasserin aut A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. Evolutionary algorithm Adaptive surrogate model Expensive optimization Reliability Li, Xia aut Liang, Jing (orcid)0000-0003-0811-0223 aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 2000 61(2019), 2 vom: 07. Nov., Seite 711-729 (DE-627)312415958 (DE-600)2009366-4 (DE-576)090895207 1615-147X nnns volume:61 year:2019 number:2 day:07 month:11 pages:711-729 https://doi.org/10.1007/s00158-019-02391-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 50.03$jMethoden und Techniken der Ingenieurwissenschaften VZ 181571455 (DE-625)181571455 AR 61 2019 2 07 11 711-729 |
| language |
English |
| source |
Enthalten in Structural and multidisciplinary optimization 61(2019), 2 vom: 07. Nov., Seite 711-729 volume:61 year:2019 number:2 day:07 month:11 pages:711-729 |
| sourceStr |
Enthalten in Structural and multidisciplinary optimization 61(2019), 2 vom: 07. Nov., Seite 711-729 volume:61 year:2019 number:2 day:07 month:11 pages:711-729 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Evolutionary algorithm Adaptive surrogate model Expensive optimization Reliability |
| dewey-raw |
510 |
| isfreeaccess_bool |
false |
| container_title |
Structural and multidisciplinary optimization |
| authorswithroles_txt_mv |
Yu, Mingyuan @@aut@@ Li, Xia @@aut@@ Liang, Jing @@aut@@ |
| publishDateDaySort_date |
2019-11-07T00:00:00Z |
| hierarchy_top_id |
312415958 |
| dewey-sort |
3510 |
| id |
OLC2051791325 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2051791325</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401065341.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00158-019-02391-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2051791325</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00158-019-02391-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.03$jMethoden und Techniken der Ingenieurwissenschaften</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yu, Mingyuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive surrogate model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Expensive optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reliability</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Xia</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Jing</subfield><subfield code="0">(orcid)0000-0003-0811-0223</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Structural and multidisciplinary optimization</subfield><subfield code="d">Springer Berlin Heidelberg, 2000</subfield><subfield code="g">61(2019), 2 vom: 07. Nov., Seite 711-729</subfield><subfield code="w">(DE-627)312415958</subfield><subfield code="w">(DE-600)2009366-4</subfield><subfield code="w">(DE-576)090895207</subfield><subfield code="x">1615-147X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:61</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">day:07</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:711-729</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00158-019-02391-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.03$jMethoden und Techniken der Ingenieurwissenschaften</subfield><subfield code="q">VZ</subfield><subfield code="0">181571455</subfield><subfield code="0">(DE-625)181571455</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">61</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="b">07</subfield><subfield code="c">11</subfield><subfield code="h">711-729</subfield></datafield></record></collection>
|
| author |
Yu, Mingyuan |
| spellingShingle |
Yu, Mingyuan ddc 510 ssgn 11 bkl 50.03$jMethoden und Techniken der Ingenieurwissenschaften misc Evolutionary algorithm misc Adaptive surrogate model misc Expensive optimization misc Reliability A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization |
| authorStr |
Yu, Mingyuan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)312415958 |
| format |
Article |
| dewey-ones |
510 - Mathematics |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1615-147X |
| topic_title |
510 VZ 11 ssgn 50.03$jMethoden und Techniken der Ingenieurwissenschaften bkl A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization Evolutionary algorithm Adaptive surrogate model Expensive optimization Reliability |
| topic |
ddc 510 ssgn 11 bkl 50.03$jMethoden und Techniken der Ingenieurwissenschaften misc Evolutionary algorithm misc Adaptive surrogate model misc Expensive optimization misc Reliability |
| topic_unstemmed |
ddc 510 ssgn 11 bkl 50.03$jMethoden und Techniken der Ingenieurwissenschaften misc Evolutionary algorithm misc Adaptive surrogate model misc Expensive optimization misc Reliability |
| topic_browse |
ddc 510 ssgn 11 bkl 50.03$jMethoden und Techniken der Ingenieurwissenschaften misc Evolutionary algorithm misc Adaptive surrogate model misc Expensive optimization misc Reliability |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Structural and multidisciplinary optimization |
| hierarchy_parent_id |
312415958 |
| dewey-tens |
510 - Mathematics |
| hierarchy_top_title |
Structural and multidisciplinary optimization |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)312415958 (DE-600)2009366-4 (DE-576)090895207 |
| title |
A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization |
| ctrlnum |
(DE-627)OLC2051791325 (DE-He213)s00158-019-02391-8-p |
| title_full |
A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization |
| author_sort |
Yu, Mingyuan |
| journal |
Structural and multidisciplinary optimization |
| journalStr |
Structural and multidisciplinary optimization |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
txt |
| container_start_page |
711 |
| author_browse |
Yu, Mingyuan Li, Xia Liang, Jing |
| container_volume |
61 |
| class |
510 VZ 11 ssgn 50.03$jMethoden und Techniken der Ingenieurwissenschaften bkl |
| format_se |
Aufsätze |
| author-letter |
Yu, Mingyuan |
| doi_str_mv |
10.1007/s00158-019-02391-8 |
| normlink |
(ORCID)0000-0003-0811-0223 181571455 |
| normlink_prefix_str_mv |
(orcid)0000-0003-0811-0223 181571455 (DE-625)181571455 |
| dewey-full |
510 |
| title_sort |
a dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization |
| title_auth |
A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization |
| abstract |
Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstractGer |
Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 |
| container_issue |
2 |
| title_short |
A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization |
| url |
https://doi.org/10.1007/s00158-019-02391-8 |
| remote_bool |
false |
| author2 |
Li, Xia Liang, Jing |
| author2Str |
Li, Xia Liang, Jing |
| ppnlink |
312415958 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00158-019-02391-8 |
| up_date |
2024-07-04T05:18:43.053Z |
| _version_ |
1803624459054088192 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2051791325</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401065341.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00158-019-02391-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2051791325</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00158-019-02391-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.03$jMethoden und Techniken der Ingenieurwissenschaften</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yu, Mingyuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive surrogate model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Expensive optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reliability</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Xia</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Jing</subfield><subfield code="0">(orcid)0000-0003-0811-0223</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Structural and multidisciplinary optimization</subfield><subfield code="d">Springer Berlin Heidelberg, 2000</subfield><subfield code="g">61(2019), 2 vom: 07. Nov., Seite 711-729</subfield><subfield code="w">(DE-627)312415958</subfield><subfield code="w">(DE-600)2009366-4</subfield><subfield code="w">(DE-576)090895207</subfield><subfield code="x">1615-147X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:61</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">day:07</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:711-729</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00158-019-02391-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.03$jMethoden und Techniken der Ingenieurwissenschaften</subfield><subfield code="q">VZ</subfield><subfield code="0">181571455</subfield><subfield code="0">(DE-625)181571455</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">61</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="b">07</subfield><subfield code="c">11</subfield><subfield code="h">711-729</subfield></datafield></record></collection>
|
| score |
7.399315 |