Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model

To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimizatio...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	LI, Zengcong [verfasserIn] TIAN, Kuo [verfasserIn] ZHANG, Shu [verfasserIn] WANG, Bo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Covariance matrix adaptation evolution strategy Model management Multi-objective optimization Surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model

Übergeordnetes Werk:	Enthalten in: Chinese journal of aeronautics - Amsterdam [u.a.] : Elsevier, 2002, 36, Seite 213-232
Übergeordnetes Werk:	volume:36 ; pages:213-232

DOI / URN:	10.1016/j.cja.2022.09.020

Katalog-ID:	ELV05992425X

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245	1	0	\|a Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model
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520			\|a To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.
650		4	\|a Covariance matrix adaptation evolution strategy
650		4	\|a Model management
650		4	\|a Multi-objective optimization
650		4	\|a Surrogate-assisted evolutionary algorithm
650		4	\|a Variable-fidelity surrogate model
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700	1		\|a WANG, Bo \|e verfasserin \|4 aut
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allfields	10.1016/j.cja.2022.09.020 doi (DE-627)ELV05992425X (ELSEVIER)S1000-9361(22)00233-3 DE-627 ger DE-627 rda eng 380 VZ 6,25 ssgn ASIEN DE-1a fid LI, Zengcong verfasserin aut Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms. Covariance matrix adaptation evolution strategy Model management Multi-objective optimization Surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model TIAN, Kuo verfasserin aut ZHANG, Shu verfasserin aut WANG, Bo verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 36, Seite 213-232 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:36 pages:213-232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-ASIEN GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 213-232
spelling	10.1016/j.cja.2022.09.020 doi (DE-627)ELV05992425X (ELSEVIER)S1000-9361(22)00233-3 DE-627 ger DE-627 rda eng 380 VZ 6,25 ssgn ASIEN DE-1a fid LI, Zengcong verfasserin aut Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms. Covariance matrix adaptation evolution strategy Model management Multi-objective optimization Surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model TIAN, Kuo verfasserin aut ZHANG, Shu verfasserin aut WANG, Bo verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 36, Seite 213-232 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:36 pages:213-232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-ASIEN GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 213-232
allfields_unstemmed	10.1016/j.cja.2022.09.020 doi (DE-627)ELV05992425X (ELSEVIER)S1000-9361(22)00233-3 DE-627 ger DE-627 rda eng 380 VZ 6,25 ssgn ASIEN DE-1a fid LI, Zengcong verfasserin aut Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms. Covariance matrix adaptation evolution strategy Model management Multi-objective optimization Surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model TIAN, Kuo verfasserin aut ZHANG, Shu verfasserin aut WANG, Bo verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 36, Seite 213-232 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:36 pages:213-232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-ASIEN GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 213-232
allfieldsGer	10.1016/j.cja.2022.09.020 doi (DE-627)ELV05992425X (ELSEVIER)S1000-9361(22)00233-3 DE-627 ger DE-627 rda eng 380 VZ 6,25 ssgn ASIEN DE-1a fid LI, Zengcong verfasserin aut Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms. Covariance matrix adaptation evolution strategy Model management Multi-objective optimization Surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model TIAN, Kuo verfasserin aut ZHANG, Shu verfasserin aut WANG, Bo verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 36, Seite 213-232 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:36 pages:213-232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-ASIEN GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 213-232
allfieldsSound	10.1016/j.cja.2022.09.020 doi (DE-627)ELV05992425X (ELSEVIER)S1000-9361(22)00233-3 DE-627 ger DE-627 rda eng 380 VZ 6,25 ssgn ASIEN DE-1a fid LI, Zengcong verfasserin aut Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms. Covariance matrix adaptation evolution strategy Model management Multi-objective optimization Surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model TIAN, Kuo verfasserin aut ZHANG, Shu verfasserin aut WANG, Bo verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 36, Seite 213-232 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:36 pages:213-232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-ASIEN GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 213-232
language	English
source	Enthalten in Chinese journal of aeronautics 36, Seite 213-232 volume:36 pages:213-232
sourceStr	Enthalten in Chinese journal of aeronautics 36, Seite 213-232 volume:36 pages:213-232
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Covariance matrix adaptation evolution strategy Model management Multi-objective optimization Surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model
dewey-raw	380
isfreeaccess_bool	false
container_title	Chinese journal of aeronautics
authorswithroles_txt_mv	LI, Zengcong @@aut@@ TIAN, Kuo @@aut@@ ZHANG, Shu @@aut@@ WANG, Bo @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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author	LI, Zengcong
spellingShingle	LI, Zengcong ddc 380 ssgn 6,25 fid ASIEN misc Covariance matrix adaptation evolution strategy misc Model management misc Multi-objective optimization misc Surrogate-assisted evolutionary algorithm misc Variable-fidelity surrogate model Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model
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topic_title	380 VZ 6,25 ssgn ASIEN DE-1a fid Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model Covariance matrix adaptation evolution strategy Model management Multi-objective optimization Surrogate-assisted evolutionary algorithm Variable-fidelity surrogate model
topic	ddc 380 ssgn 6,25 fid ASIEN misc Covariance matrix adaptation evolution strategy misc Model management misc Multi-objective optimization misc Surrogate-assisted evolutionary algorithm misc Variable-fidelity surrogate model
topic_unstemmed	ddc 380 ssgn 6,25 fid ASIEN misc Covariance matrix adaptation evolution strategy misc Model management misc Multi-objective optimization misc Surrogate-assisted evolutionary algorithm misc Variable-fidelity surrogate model
topic_browse	ddc 380 ssgn 6,25 fid ASIEN misc Covariance matrix adaptation evolution strategy misc Model management misc Multi-objective optimization misc Surrogate-assisted evolutionary algorithm misc Variable-fidelity surrogate model
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title	Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model
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title_full	Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model
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title_sort	efficient multi-objective cma-es algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model
title_auth	Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model
abstract	To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.
abstractGer	To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.
abstract_unstemmed	To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions (LF-NDS). Secondly, aiming to obtain the High-Fidelity (HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally, the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement (MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts, the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.
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title_short	Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model
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author2	TIAN, Kuo ZHANG, Shu WANG, Bo
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up_date	2024-07-06T23:23:06.687Z
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Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model

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