An adaptive surrogate-assisted particle swarm optimization for expensive problems

Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs...
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Autor*in:	Li, Xuemei [verfasserIn] Li, Shaojun

Format:	Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Surrogate-assisted evolutionary algorithm Ensemble model Radial basis functions Particle swarm optimization

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 25(2021), 24 vom: 08. Okt., Seite 15051-15065
Übergeordnetes Werk:	volume:25 ; year:2021 ; number:24 ; day:08 ; month:10 ; pages:15051-15065

Links:	Volltext

DOI / URN:	10.1007/s00500-021-06348-2

Katalog-ID:	OLC2077350555

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allfields	10.1007/s00500-021-06348-2 doi (DE-627)OLC2077350555 (DE-He213)s00500-021-06348-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Li, Xuemei verfasserin aut An adaptive surrogate-assisted particle swarm optimization for expensive problems 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. Surrogate-assisted evolutionary algorithm Ensemble model Radial basis functions Particle swarm optimization Li, Shaojun aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2021), 24 vom: 08. Okt., Seite 15051-15065 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2021 number:24 day:08 month:10 pages:15051-15065 https://doi.org/10.1007/s00500-021-06348-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2021 24 08 10 15051-15065
spelling	10.1007/s00500-021-06348-2 doi (DE-627)OLC2077350555 (DE-He213)s00500-021-06348-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Li, Xuemei verfasserin aut An adaptive surrogate-assisted particle swarm optimization for expensive problems 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. Surrogate-assisted evolutionary algorithm Ensemble model Radial basis functions Particle swarm optimization Li, Shaojun aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2021), 24 vom: 08. Okt., Seite 15051-15065 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2021 number:24 day:08 month:10 pages:15051-15065 https://doi.org/10.1007/s00500-021-06348-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2021 24 08 10 15051-15065
allfields_unstemmed	10.1007/s00500-021-06348-2 doi (DE-627)OLC2077350555 (DE-He213)s00500-021-06348-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Li, Xuemei verfasserin aut An adaptive surrogate-assisted particle swarm optimization for expensive problems 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. Surrogate-assisted evolutionary algorithm Ensemble model Radial basis functions Particle swarm optimization Li, Shaojun aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2021), 24 vom: 08. Okt., Seite 15051-15065 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2021 number:24 day:08 month:10 pages:15051-15065 https://doi.org/10.1007/s00500-021-06348-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2021 24 08 10 15051-15065
allfieldsGer	10.1007/s00500-021-06348-2 doi (DE-627)OLC2077350555 (DE-He213)s00500-021-06348-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Li, Xuemei verfasserin aut An adaptive surrogate-assisted particle swarm optimization for expensive problems 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. Surrogate-assisted evolutionary algorithm Ensemble model Radial basis functions Particle swarm optimization Li, Shaojun aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2021), 24 vom: 08. Okt., Seite 15051-15065 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2021 number:24 day:08 month:10 pages:15051-15065 https://doi.org/10.1007/s00500-021-06348-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2021 24 08 10 15051-15065
allfieldsSound	10.1007/s00500-021-06348-2 doi (DE-627)OLC2077350555 (DE-He213)s00500-021-06348-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Li, Xuemei verfasserin aut An adaptive surrogate-assisted particle swarm optimization for expensive problems 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. Surrogate-assisted evolutionary algorithm Ensemble model Radial basis functions Particle swarm optimization Li, Shaojun aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2021), 24 vom: 08. Okt., Seite 15051-15065 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2021 number:24 day:08 month:10 pages:15051-15065 https://doi.org/10.1007/s00500-021-06348-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2021 24 08 10 15051-15065
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title_sort	an adaptive surrogate-assisted particle swarm optimization for expensive problems
title_auth	An adaptive surrogate-assisted particle swarm optimization for expensive problems
abstract	Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstractGer	Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstract_unstemmed	Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
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title_short	An adaptive surrogate-assisted particle swarm optimization for expensive problems
url	https://doi.org/10.1007/s00500-021-06348-2
remote_bool	false
author2	Li, Shaojun
author2Str	Li, Shaojun
ppnlink	231970536
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s00500-021-06348-2
up_date	2024-07-03T15:05:22.099Z
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