Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy
Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutio...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Hao [verfasserIn] Li, Weikun [verfasserIn] Cui, Weicheng [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
Surrogate-assisted optimization |
|---|
| Übergeordnetes Werk: |
Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 232 |
|---|---|
| Übergeordnetes Werk: |
volume:232 |
| DOI / URN: |
10.1016/j.eswa.2023.120826 |
|---|
| Katalog-ID: |
ELV062011936 |
|---|
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| 245 | 1 | 0 | |a Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy |
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| 520 | |a Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have attracted massive attention recently due to their high efficiency and applicability in solving real-world optimization problems. As the dimension of the optimization problem increases, the computational cost of constructing surrogates increases, and the surrogate model’s prediction accuracy may be severely degraded. High-dimensional model representation (HDMR) is a promising technique to partition a high-dimensional function into low-dimensional component functions. However, HDMR’s hierarchical structure limits its applicability in online SAEAs. To address these problems, this paper develops a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS). In this work, we propose a novel hierarchical surrogate technique, in which a composite surrogate model is constructed by the first-order HDMR model and an error value-based surrogate model, then, using the internal contrastive analysis method, a hierarchical surrogate model (HSM) combining the composite surrogate with the fitness value-based surrogate is established. In addition, an adaptive infill strategy is developed to balance the exploration and exploitation of the surrogate-assisted evolutionary search. Various test functions and an antenna optimization problem are employed to compare SAEA-HAS with several well-known SAEAs. The experimental results validate the effectiveness of SAEA-HAS. | ||
| 650 | 4 | |a Surrogate-assisted optimization | |
| 650 | 4 | |a High-dimensional model representation | |
| 650 | 4 | |a Infill sampling strategy | |
| 650 | 4 | |a Surrogate modeling | |
| 700 | 1 | |a Li, Weikun |e verfasserin |4 aut | |
| 700 | 1 | |a Cui, Weicheng |e verfasserin |0 (orcid)0000-0003-1871-2658 |4 aut | |
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10.1016/j.eswa.2023.120826 doi (DE-627)ELV062011936 (ELSEVIER)S0957-4174(23)01328-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Chen, Hao verfasserin aut Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have attracted massive attention recently due to their high efficiency and applicability in solving real-world optimization problems. As the dimension of the optimization problem increases, the computational cost of constructing surrogates increases, and the surrogate model’s prediction accuracy may be severely degraded. High-dimensional model representation (HDMR) is a promising technique to partition a high-dimensional function into low-dimensional component functions. However, HDMR’s hierarchical structure limits its applicability in online SAEAs. To address these problems, this paper develops a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS). In this work, we propose a novel hierarchical surrogate technique, in which a composite surrogate model is constructed by the first-order HDMR model and an error value-based surrogate model, then, using the internal contrastive analysis method, a hierarchical surrogate model (HSM) combining the composite surrogate with the fitness value-based surrogate is established. In addition, an adaptive infill strategy is developed to balance the exploration and exploitation of the surrogate-assisted evolutionary search. Various test functions and an antenna optimization problem are employed to compare SAEA-HAS with several well-known SAEAs. The experimental results validate the effectiveness of SAEA-HAS. Surrogate-assisted optimization High-dimensional model representation Infill sampling strategy Surrogate modeling Li, Weikun verfasserin aut Cui, Weicheng verfasserin (orcid)0000-0003-1871-2658 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 232 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 232 |
| spelling |
10.1016/j.eswa.2023.120826 doi (DE-627)ELV062011936 (ELSEVIER)S0957-4174(23)01328-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Chen, Hao verfasserin aut Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have attracted massive attention recently due to their high efficiency and applicability in solving real-world optimization problems. As the dimension of the optimization problem increases, the computational cost of constructing surrogates increases, and the surrogate model’s prediction accuracy may be severely degraded. High-dimensional model representation (HDMR) is a promising technique to partition a high-dimensional function into low-dimensional component functions. However, HDMR’s hierarchical structure limits its applicability in online SAEAs. To address these problems, this paper develops a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS). In this work, we propose a novel hierarchical surrogate technique, in which a composite surrogate model is constructed by the first-order HDMR model and an error value-based surrogate model, then, using the internal contrastive analysis method, a hierarchical surrogate model (HSM) combining the composite surrogate with the fitness value-based surrogate is established. In addition, an adaptive infill strategy is developed to balance the exploration and exploitation of the surrogate-assisted evolutionary search. Various test functions and an antenna optimization problem are employed to compare SAEA-HAS with several well-known SAEAs. The experimental results validate the effectiveness of SAEA-HAS. Surrogate-assisted optimization High-dimensional model representation Infill sampling strategy Surrogate modeling Li, Weikun verfasserin aut Cui, Weicheng verfasserin (orcid)0000-0003-1871-2658 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 232 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 232 |
| allfields_unstemmed |
10.1016/j.eswa.2023.120826 doi (DE-627)ELV062011936 (ELSEVIER)S0957-4174(23)01328-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Chen, Hao verfasserin aut Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have attracted massive attention recently due to their high efficiency and applicability in solving real-world optimization problems. As the dimension of the optimization problem increases, the computational cost of constructing surrogates increases, and the surrogate model’s prediction accuracy may be severely degraded. High-dimensional model representation (HDMR) is a promising technique to partition a high-dimensional function into low-dimensional component functions. However, HDMR’s hierarchical structure limits its applicability in online SAEAs. To address these problems, this paper develops a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS). In this work, we propose a novel hierarchical surrogate technique, in which a composite surrogate model is constructed by the first-order HDMR model and an error value-based surrogate model, then, using the internal contrastive analysis method, a hierarchical surrogate model (HSM) combining the composite surrogate with the fitness value-based surrogate is established. In addition, an adaptive infill strategy is developed to balance the exploration and exploitation of the surrogate-assisted evolutionary search. Various test functions and an antenna optimization problem are employed to compare SAEA-HAS with several well-known SAEAs. The experimental results validate the effectiveness of SAEA-HAS. Surrogate-assisted optimization High-dimensional model representation Infill sampling strategy Surrogate modeling Li, Weikun verfasserin aut Cui, Weicheng verfasserin (orcid)0000-0003-1871-2658 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 232 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 232 |
| allfieldsGer |
10.1016/j.eswa.2023.120826 doi (DE-627)ELV062011936 (ELSEVIER)S0957-4174(23)01328-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Chen, Hao verfasserin aut Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have attracted massive attention recently due to their high efficiency and applicability in solving real-world optimization problems. As the dimension of the optimization problem increases, the computational cost of constructing surrogates increases, and the surrogate model’s prediction accuracy may be severely degraded. High-dimensional model representation (HDMR) is a promising technique to partition a high-dimensional function into low-dimensional component functions. However, HDMR’s hierarchical structure limits its applicability in online SAEAs. To address these problems, this paper develops a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS). In this work, we propose a novel hierarchical surrogate technique, in which a composite surrogate model is constructed by the first-order HDMR model and an error value-based surrogate model, then, using the internal contrastive analysis method, a hierarchical surrogate model (HSM) combining the composite surrogate with the fitness value-based surrogate is established. In addition, an adaptive infill strategy is developed to balance the exploration and exploitation of the surrogate-assisted evolutionary search. Various test functions and an antenna optimization problem are employed to compare SAEA-HAS with several well-known SAEAs. The experimental results validate the effectiveness of SAEA-HAS. Surrogate-assisted optimization High-dimensional model representation Infill sampling strategy Surrogate modeling Li, Weikun verfasserin aut Cui, Weicheng verfasserin (orcid)0000-0003-1871-2658 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 232 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:232 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 232 |
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Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy |
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Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy |
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10.1016/j.eswa.2023.120826 |
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surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy |
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Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy |
| abstract |
Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have attracted massive attention recently due to their high efficiency and applicability in solving real-world optimization problems. As the dimension of the optimization problem increases, the computational cost of constructing surrogates increases, and the surrogate model’s prediction accuracy may be severely degraded. High-dimensional model representation (HDMR) is a promising technique to partition a high-dimensional function into low-dimensional component functions. However, HDMR’s hierarchical structure limits its applicability in online SAEAs. To address these problems, this paper develops a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS). In this work, we propose a novel hierarchical surrogate technique, in which a composite surrogate model is constructed by the first-order HDMR model and an error value-based surrogate model, then, using the internal contrastive analysis method, a hierarchical surrogate model (HSM) combining the composite surrogate with the fitness value-based surrogate is established. In addition, an adaptive infill strategy is developed to balance the exploration and exploitation of the surrogate-assisted evolutionary search. Various test functions and an antenna optimization problem are employed to compare SAEA-HAS with several well-known SAEAs. The experimental results validate the effectiveness of SAEA-HAS. |
| abstractGer |
Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have attracted massive attention recently due to their high efficiency and applicability in solving real-world optimization problems. As the dimension of the optimization problem increases, the computational cost of constructing surrogates increases, and the surrogate model’s prediction accuracy may be severely degraded. High-dimensional model representation (HDMR) is a promising technique to partition a high-dimensional function into low-dimensional component functions. However, HDMR’s hierarchical structure limits its applicability in online SAEAs. To address these problems, this paper develops a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS). In this work, we propose a novel hierarchical surrogate technique, in which a composite surrogate model is constructed by the first-order HDMR model and an error value-based surrogate model, then, using the internal contrastive analysis method, a hierarchical surrogate model (HSM) combining the composite surrogate with the fitness value-based surrogate is established. In addition, an adaptive infill strategy is developed to balance the exploration and exploitation of the surrogate-assisted evolutionary search. Various test functions and an antenna optimization problem are employed to compare SAEA-HAS with several well-known SAEAs. The experimental results validate the effectiveness of SAEA-HAS. |
| abstract_unstemmed |
Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in substantial computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have attracted massive attention recently due to their high efficiency and applicability in solving real-world optimization problems. As the dimension of the optimization problem increases, the computational cost of constructing surrogates increases, and the surrogate model’s prediction accuracy may be severely degraded. High-dimensional model representation (HDMR) is a promising technique to partition a high-dimensional function into low-dimensional component functions. However, HDMR’s hierarchical structure limits its applicability in online SAEAs. To address these problems, this paper develops a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS). In this work, we propose a novel hierarchical surrogate technique, in which a composite surrogate model is constructed by the first-order HDMR model and an error value-based surrogate model, then, using the internal contrastive analysis method, a hierarchical surrogate model (HSM) combining the composite surrogate with the fitness value-based surrogate is established. In addition, an adaptive infill strategy is developed to balance the exploration and exploitation of the surrogate-assisted evolutionary search. Various test functions and an antenna optimization problem are employed to compare SAEA-HAS with several well-known SAEAs. The experimental results validate the effectiveness of SAEA-HAS. |
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Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy |
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