Investigation of Kriging-based SAEAs’ metamodel samples for computationally expensive optimization problems
Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Valadão, Mônica [verfasserIn] Maravilha, André [verfasserIn] Batista, Lucas [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Evolutionary intelligence - Springer Berlin Heidelberg, 2008, 17(2023), 3 vom: 05. Juli, Seite 1783-1799 |
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| Übergeordnetes Werk: |
volume:17 ; year:2023 ; number:3 ; day:05 ; month:07 ; pages:1783-1799 |
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| DOI / URN: |
10.1007/s12065-023-00862-y |
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| 520 | |a Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. | ||
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10.1007/s12065-023-00862-y doi (DE-627)SPR055930859 (SPR)s12065-023-00862-y-e DE-627 ger DE-627 rakwb eng 004 VZ Valadão, Mônica verfasserin aut Investigation of Kriging-based SAEAs’ metamodel samples for computationally expensive optimization problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. Kriging model (dpeaa)DE-He213 SAEAs (dpeaa)DE-He213 Sampling strategy (dpeaa)DE-He213 Expensive problems (dpeaa)DE-He213 Maravilha, André verfasserin aut Batista, Lucas verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2023), 3 vom: 05. Juli, Seite 1783-1799 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2023 number:3 day:05 month:07 pages:1783-1799 https://dx.doi.org/10.1007/s12065-023-00862-y X:SPRINGER Resolving-System lizenzpflichtig 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_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 3 05 07 1783-1799 |
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10.1007/s12065-023-00862-y doi (DE-627)SPR055930859 (SPR)s12065-023-00862-y-e DE-627 ger DE-627 rakwb eng 004 VZ Valadão, Mônica verfasserin aut Investigation of Kriging-based SAEAs’ metamodel samples for computationally expensive optimization problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. Kriging model (dpeaa)DE-He213 SAEAs (dpeaa)DE-He213 Sampling strategy (dpeaa)DE-He213 Expensive problems (dpeaa)DE-He213 Maravilha, André verfasserin aut Batista, Lucas verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2023), 3 vom: 05. Juli, Seite 1783-1799 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2023 number:3 day:05 month:07 pages:1783-1799 https://dx.doi.org/10.1007/s12065-023-00862-y X:SPRINGER Resolving-System lizenzpflichtig 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_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 3 05 07 1783-1799 |
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10.1007/s12065-023-00862-y doi (DE-627)SPR055930859 (SPR)s12065-023-00862-y-e DE-627 ger DE-627 rakwb eng 004 VZ Valadão, Mônica verfasserin aut Investigation of Kriging-based SAEAs’ metamodel samples for computationally expensive optimization problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. Kriging model (dpeaa)DE-He213 SAEAs (dpeaa)DE-He213 Sampling strategy (dpeaa)DE-He213 Expensive problems (dpeaa)DE-He213 Maravilha, André verfasserin aut Batista, Lucas verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2023), 3 vom: 05. Juli, Seite 1783-1799 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2023 number:3 day:05 month:07 pages:1783-1799 https://dx.doi.org/10.1007/s12065-023-00862-y X:SPRINGER Resolving-System lizenzpflichtig 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_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 3 05 07 1783-1799 |
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10.1007/s12065-023-00862-y doi (DE-627)SPR055930859 (SPR)s12065-023-00862-y-e DE-627 ger DE-627 rakwb eng 004 VZ Valadão, Mônica verfasserin aut Investigation of Kriging-based SAEAs’ metamodel samples for computationally expensive optimization problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. Kriging model (dpeaa)DE-He213 SAEAs (dpeaa)DE-He213 Sampling strategy (dpeaa)DE-He213 Expensive problems (dpeaa)DE-He213 Maravilha, André verfasserin aut Batista, Lucas verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2023), 3 vom: 05. Juli, Seite 1783-1799 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2023 number:3 day:05 month:07 pages:1783-1799 https://dx.doi.org/10.1007/s12065-023-00862-y X:SPRINGER Resolving-System lizenzpflichtig 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_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 3 05 07 1783-1799 |
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10.1007/s12065-023-00862-y doi (DE-627)SPR055930859 (SPR)s12065-023-00862-y-e DE-627 ger DE-627 rakwb eng 004 VZ Valadão, Mônica verfasserin aut Investigation of Kriging-based SAEAs’ metamodel samples for computationally expensive optimization problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. Kriging model (dpeaa)DE-He213 SAEAs (dpeaa)DE-He213 Sampling strategy (dpeaa)DE-He213 Expensive problems (dpeaa)DE-He213 Maravilha, André verfasserin aut Batista, Lucas verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2023), 3 vom: 05. Juli, Seite 1783-1799 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2023 number:3 day:05 month:07 pages:1783-1799 https://dx.doi.org/10.1007/s12065-023-00862-y X:SPRINGER Resolving-System lizenzpflichtig 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_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 3 05 07 1783-1799 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kriging model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SAEAs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sampling strategy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Expensive problems</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Maravilha, André</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Batista, Lucas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Evolutionary intelligence</subfield><subfield code="d">Springer Berlin Heidelberg, 2008</subfield><subfield code="g">17(2023), 3 vom: 05. 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Investigation of Kriging-based SAEAs’ metamodel samples for computationally expensive optimization problems |
| abstract |
Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Surrogate model assisted evolutionary algorithms (SAEAs) are strategies widely applied to deal with computationally expensive optimization problems (CEOPs). These methods employ metamodels to drive an evolutionary algorithm (EA) to promising design regions where new evaluations on the true-objective function must be performed. To do this, SAEAs are required to handle the challenge of training a metamodel to improve its predictions. The reliability of a metamodel is strongly related to the samples used for its training. Despite this, several SAEAs are proposed without concern about the sampling strategy employed. The ideal situation is to obtain a sample not far away from the solutions predicted on the metamodel. In this sense, the contribution/novelty of this paper regards an investigative study to compare five strategies for defining the metamodel sample in a proposed SAEA Framework (SAEA/F). The SAEA/F uses a one-dimensional Ordinary Kriging (OK) metamodel, and an expected improvement (EI) merit function is applied to define on which solutions to spend the budget of true-function evaluation. In this investigation, each strategy is incorporated into SAEA/F and then used to solve a set of analytical functions of single-objective optimization problems. The computational results suggest that two of the five sampling strategies stand out the best. The first strategy chooses those solutions with the lowest distance to the centroid of the population of solutions, and the second selects the newest solutions evaluated on true-objective function. The results highlight the potential of these approaches for solving expensive optimization problems since they speed up the algorithm convergence to improved solutions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Investigation of Kriging-based SAEAs’ metamodel samples for computationally expensive optimization problems |
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https://dx.doi.org/10.1007/s12065-023-00862-y |
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Maravilha, André Batista, Lucas |
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10.1007/s12065-023-00862-y |
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| score |
7.399315 |