A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment

Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential imp...
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Autor*in:	Li, Guoping [verfasserIn] Liu, Yanmin [verfasserIn] Deng, Xicai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Dynamic multiobjective optimization Prediction method Subspace alignment Correlation alignment

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Complex & intelligent systems - Springer International Publishing, 2015, 10(2024), 3 vom: 11. März, Seite 4421-4444
Übergeordnetes Werk:	volume:10 ; year:2024 ; number:3 ; day:11 ; month:03 ; pages:4421-4444

Links:	Volltext

DOI / URN:	10.1007/s40747-024-01369-4

Katalog-ID:	SPR055887554

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520			\|a Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms.
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allfields	10.1007/s40747-024-01369-4 doi (DE-627)SPR055887554 (SPR)s40747-024-01369-4-e DE-627 ger DE-627 rakwb eng 004 VZ Li, Guoping verfasserin aut A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms. Dynamic multiobjective optimization (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Subspace alignment (dpeaa)DE-He213 Correlation alignment (dpeaa)DE-He213 Liu, Yanmin verfasserin (orcid)0000-0002-1571-5370 aut Deng, Xicai verfasserin aut Enthalten in Complex & intelligent systems Springer International Publishing, 2015 10(2024), 3 vom: 11. März, Seite 4421-4444 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:10 year:2024 number:3 day:11 month:03 pages:4421-4444 https://dx.doi.org/10.1007/s40747-024-01369-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2024 3 11 03 4421-4444
spelling	10.1007/s40747-024-01369-4 doi (DE-627)SPR055887554 (SPR)s40747-024-01369-4-e DE-627 ger DE-627 rakwb eng 004 VZ Li, Guoping verfasserin aut A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms. Dynamic multiobjective optimization (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Subspace alignment (dpeaa)DE-He213 Correlation alignment (dpeaa)DE-He213 Liu, Yanmin verfasserin (orcid)0000-0002-1571-5370 aut Deng, Xicai verfasserin aut Enthalten in Complex & intelligent systems Springer International Publishing, 2015 10(2024), 3 vom: 11. März, Seite 4421-4444 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:10 year:2024 number:3 day:11 month:03 pages:4421-4444 https://dx.doi.org/10.1007/s40747-024-01369-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2024 3 11 03 4421-4444
allfields_unstemmed	10.1007/s40747-024-01369-4 doi (DE-627)SPR055887554 (SPR)s40747-024-01369-4-e DE-627 ger DE-627 rakwb eng 004 VZ Li, Guoping verfasserin aut A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms. Dynamic multiobjective optimization (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Subspace alignment (dpeaa)DE-He213 Correlation alignment (dpeaa)DE-He213 Liu, Yanmin verfasserin (orcid)0000-0002-1571-5370 aut Deng, Xicai verfasserin aut Enthalten in Complex & intelligent systems Springer International Publishing, 2015 10(2024), 3 vom: 11. März, Seite 4421-4444 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:10 year:2024 number:3 day:11 month:03 pages:4421-4444 https://dx.doi.org/10.1007/s40747-024-01369-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2024 3 11 03 4421-4444
allfieldsGer	10.1007/s40747-024-01369-4 doi (DE-627)SPR055887554 (SPR)s40747-024-01369-4-e DE-627 ger DE-627 rakwb eng 004 VZ Li, Guoping verfasserin aut A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms. Dynamic multiobjective optimization (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Subspace alignment (dpeaa)DE-He213 Correlation alignment (dpeaa)DE-He213 Liu, Yanmin verfasserin (orcid)0000-0002-1571-5370 aut Deng, Xicai verfasserin aut Enthalten in Complex & intelligent systems Springer International Publishing, 2015 10(2024), 3 vom: 11. März, Seite 4421-4444 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:10 year:2024 number:3 day:11 month:03 pages:4421-4444 https://dx.doi.org/10.1007/s40747-024-01369-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2024 3 11 03 4421-4444
allfieldsSound	10.1007/s40747-024-01369-4 doi (DE-627)SPR055887554 (SPR)s40747-024-01369-4-e DE-627 ger DE-627 rakwb eng 004 VZ Li, Guoping verfasserin aut A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms. Dynamic multiobjective optimization (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Subspace alignment (dpeaa)DE-He213 Correlation alignment (dpeaa)DE-He213 Liu, Yanmin verfasserin (orcid)0000-0002-1571-5370 aut Deng, Xicai verfasserin aut Enthalten in Complex & intelligent systems Springer International Publishing, 2015 10(2024), 3 vom: 11. März, Seite 4421-4444 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:10 year:2024 number:3 day:11 month:03 pages:4421-4444 https://dx.doi.org/10.1007/s40747-024-01369-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER 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_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2024 3 11 03 4421-4444
language	English
source	Enthalten in Complex & intelligent systems 10(2024), 3 vom: 11. März, Seite 4421-4444 volume:10 year:2024 number:3 day:11 month:03 pages:4421-4444
sourceStr	Enthalten in Complex & intelligent systems 10(2024), 3 vom: 11. März, Seite 4421-4444 volume:10 year:2024 number:3 day:11 month:03 pages:4421-4444
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Dynamic multiobjective optimization Prediction method Subspace alignment Correlation alignment
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container_title	Complex & intelligent systems
authorswithroles_txt_mv	Li, Guoping @@aut@@ Liu, Yanmin @@aut@@ Deng, Xicai @@aut@@
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Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. 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author	Li, Guoping
spellingShingle	Li, Guoping ddc 004 misc Dynamic multiobjective optimization misc Prediction method misc Subspace alignment misc Correlation alignment A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment
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topic_title	004 VZ A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment Dynamic multiobjective optimization (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Subspace alignment (dpeaa)DE-He213 Correlation alignment (dpeaa)DE-He213
topic	ddc 004 misc Dynamic multiobjective optimization misc Prediction method misc Subspace alignment misc Correlation alignment
topic_unstemmed	ddc 004 misc Dynamic multiobjective optimization misc Prediction method misc Subspace alignment misc Correlation alignment
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title	A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment
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title_full	A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment
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title_sort	a prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment
title_auth	A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment
abstract	Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms. © The Author(s) 2024
abstractGer	Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms. © The Author(s) 2024
abstract_unstemmed	Abstract Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms. © The Author(s) 2024
collection_details	SYSFLAG_0 GBV_SPRINGER 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_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
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title_short	A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment
url	https://dx.doi.org/10.1007/s40747-024-01369-4
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author2	Liu, Yanmin Deng, Xicai
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up_date	2024-07-03T18:42:13.391Z
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A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment

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