A review of surrogate-assisted evolutionary algorithms for expensive optimization problems

Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	He, Chunlin [verfasserIn] Zhang, Yong [verfasserIn] Gong, Dunwei [verfasserIn] Ji, Xinfang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Evolutionary algorithms Expensive optimization Swarm intelligence Surrogate model

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 217
Übergeordnetes Werk:	volume:217

DOI / URN:	10.1016/j.eswa.2022.119495

Katalog-ID:	ELV062744682

Internformat


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520			\|a Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future.
650		4	\|a Evolutionary algorithms
650		4	\|a Expensive optimization
650		4	\|a Swarm intelligence
650		4	\|a Surrogate model
700	1		\|a Zhang, Yong \|e verfasserin \|0 (orcid)0000-0003-0026-8181 \|4 aut
700	1		\|a Gong, Dunwei \|e verfasserin \|0 (orcid)0000-0003-2838-4301 \|4 aut
700	1		\|a Ji, Xinfang \|e verfasserin \|0 (orcid)0000-0002-3514-6777 \|4 aut
773	0	8	\|i Enthalten in \|t Expert systems with applications \|d Amsterdam [u.a.] : Elsevier Science, 1990 \|g 217 \|h Online-Ressource \|w (DE-627)320577961 \|w (DE-600)2017237-0 \|w (DE-576)11481807X \|7 nnns
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912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
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912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
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author_variant	c h ch y z yz d g dg x j xj
matchkey_str	hechunlinzhangyonggongdunweijixinfang:2023----:rveosroaesitdvltoayloihsoepnie
hierarchy_sort_str	2023
bklnumber	54.72
publishDate	2023
allfields	10.1016/j.eswa.2022.119495 doi (DE-627)ELV062744682 (ELSEVIER)S0957-4174(22)02514-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl He, Chunlin verfasserin (orcid)0000-0001-5980-0684 aut A review of surrogate-assisted evolutionary algorithms for expensive optimization problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future. Evolutionary algorithms Expensive optimization Swarm intelligence Surrogate model Zhang, Yong verfasserin (orcid)0000-0003-0026-8181 aut Gong, Dunwei verfasserin (orcid)0000-0003-2838-4301 aut Ji, Xinfang verfasserin (orcid)0000-0002-3514-6777 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
spelling	10.1016/j.eswa.2022.119495 doi (DE-627)ELV062744682 (ELSEVIER)S0957-4174(22)02514-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl He, Chunlin verfasserin (orcid)0000-0001-5980-0684 aut A review of surrogate-assisted evolutionary algorithms for expensive optimization problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future. Evolutionary algorithms Expensive optimization Swarm intelligence Surrogate model Zhang, Yong verfasserin (orcid)0000-0003-0026-8181 aut Gong, Dunwei verfasserin (orcid)0000-0003-2838-4301 aut Ji, Xinfang verfasserin (orcid)0000-0002-3514-6777 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
allfields_unstemmed	10.1016/j.eswa.2022.119495 doi (DE-627)ELV062744682 (ELSEVIER)S0957-4174(22)02514-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl He, Chunlin verfasserin (orcid)0000-0001-5980-0684 aut A review of surrogate-assisted evolutionary algorithms for expensive optimization problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future. Evolutionary algorithms Expensive optimization Swarm intelligence Surrogate model Zhang, Yong verfasserin (orcid)0000-0003-0026-8181 aut Gong, Dunwei verfasserin (orcid)0000-0003-2838-4301 aut Ji, Xinfang verfasserin (orcid)0000-0002-3514-6777 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
allfieldsGer	10.1016/j.eswa.2022.119495 doi (DE-627)ELV062744682 (ELSEVIER)S0957-4174(22)02514-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl He, Chunlin verfasserin (orcid)0000-0001-5980-0684 aut A review of surrogate-assisted evolutionary algorithms for expensive optimization problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future. Evolutionary algorithms Expensive optimization Swarm intelligence Surrogate model Zhang, Yong verfasserin (orcid)0000-0003-0026-8181 aut Gong, Dunwei verfasserin (orcid)0000-0003-2838-4301 aut Ji, Xinfang verfasserin (orcid)0000-0002-3514-6777 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
allfieldsSound	10.1016/j.eswa.2022.119495 doi (DE-627)ELV062744682 (ELSEVIER)S0957-4174(22)02514-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl He, Chunlin verfasserin (orcid)0000-0001-5980-0684 aut A review of surrogate-assisted evolutionary algorithms for expensive optimization problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future. Evolutionary algorithms Expensive optimization Swarm intelligence Surrogate model Zhang, Yong verfasserin (orcid)0000-0003-0026-8181 aut Gong, Dunwei verfasserin (orcid)0000-0003-2838-4301 aut Ji, Xinfang verfasserin (orcid)0000-0002-3514-6777 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
language	English
source	Enthalten in Expert systems with applications 217 volume:217
sourceStr	Enthalten in Expert systems with applications 217 volume:217
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Evolutionary algorithms Expensive optimization Swarm intelligence Surrogate model
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	He, Chunlin @@aut@@ Zhang, Yong @@aut@@ Gong, Dunwei @@aut@@ Ji, Xinfang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV062744682
language_de	englisch
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author	He, Chunlin
spellingShingle	He, Chunlin ddc 004 bkl 54.72 misc Evolutionary algorithms misc Expensive optimization misc Swarm intelligence misc Surrogate model A review of surrogate-assisted evolutionary algorithms for expensive optimization problems
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topic_title	004 VZ 54.72 bkl A review of surrogate-assisted evolutionary algorithms for expensive optimization problems Evolutionary algorithms Expensive optimization Swarm intelligence Surrogate model
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title	A review of surrogate-assisted evolutionary algorithms for expensive optimization problems
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title_full	A review of surrogate-assisted evolutionary algorithms for expensive optimization problems
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title_sort	a review of surrogate-assisted evolutionary algorithms for expensive optimization problems
title_auth	A review of surrogate-assisted evolutionary algorithms for expensive optimization problems
abstract	Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future.
abstractGer	Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future.
abstract_unstemmed	Many problems in real life can be seen as Expensive Optimization Problems (EOPs). Compared with traditional optimization problems, the evaluation cost of candidate solutions for EOPs is expensive and even unaffordable. Surrogate-assisted evolutionary algorithms (SAEAs) has become a hot technology to solve EOPs in recent year, because they can effectively reduce computational cost and improve solving efficiency. However, few literatures provide a systematic overview for SAEAs. This paper systematically summarizes the existing research results of SAEAs from the aspects of algorithms and applications. Firstly, the necessity of studying SAEAs and several commonly used surrogate models are introduced. Subsequently, according to the type of objective functions and constraints, the existing SAEAs are classified and discussed. Then, the application of SAEAs in many fields are reviewed. Finally, we indicate several promising lines of research that are worthy of devotion in future.
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title_short	A review of surrogate-assisted evolutionary algorithms for expensive optimization problems
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author2	Zhang, Yong Gong, Dunwei Ji, Xinfang
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A review of surrogate-assisted evolutionary algorithms for expensive optimization problems

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