A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms
Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Seliverstov, E. Yu. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Pleiades Publishing, Ltd. 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of applied and industrial mathematics - Moscow : MAIK Nauka/Interperiodica Publ., 2007, 16(2022), 4 vom: Nov., Seite 776-788 |
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| Übergeordnetes Werk: |
volume:16 ; year:2022 ; number:4 ; month:11 ; pages:776-788 |
| Links: |
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| DOI / URN: |
10.1134/S1990478922040172 |
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| Katalog-ID: |
SPR051527642 |
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| 100 | 1 | |a Seliverstov, E. Yu. |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms |
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| 520 | |a Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. | ||
| 650 | 4 | |a global optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a metaheuristic algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a parameter setting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a parameter control |7 (dpeaa)DE-He213 | |
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10.1134/S1990478922040172 doi (DE-627)SPR051527642 (SPR)S1990478922040172-e DE-627 ger DE-627 rakwb eng Seliverstov, E. Yu. verfasserin aut A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022 Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. global optimization (dpeaa)DE-He213 metaheuristic algorithm (dpeaa)DE-He213 parameter setting (dpeaa)DE-He213 parameter control (dpeaa)DE-He213 Enthalten in Journal of applied and industrial mathematics Moscow : MAIK Nauka/Interperiodica Publ., 2007 16(2022), 4 vom: Nov., Seite 776-788 (DE-627)546898742 (DE-600)2391568-7 1990-4797 nnns volume:16 year:2022 number:4 month:11 pages:776-788 https://dx.doi.org/10.1134/S1990478922040172 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 16 2022 4 11 776-788 |
| spelling |
10.1134/S1990478922040172 doi (DE-627)SPR051527642 (SPR)S1990478922040172-e DE-627 ger DE-627 rakwb eng Seliverstov, E. Yu. verfasserin aut A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022 Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. global optimization (dpeaa)DE-He213 metaheuristic algorithm (dpeaa)DE-He213 parameter setting (dpeaa)DE-He213 parameter control (dpeaa)DE-He213 Enthalten in Journal of applied and industrial mathematics Moscow : MAIK Nauka/Interperiodica Publ., 2007 16(2022), 4 vom: Nov., Seite 776-788 (DE-627)546898742 (DE-600)2391568-7 1990-4797 nnns volume:16 year:2022 number:4 month:11 pages:776-788 https://dx.doi.org/10.1134/S1990478922040172 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 16 2022 4 11 776-788 |
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10.1134/S1990478922040172 doi (DE-627)SPR051527642 (SPR)S1990478922040172-e DE-627 ger DE-627 rakwb eng Seliverstov, E. Yu. verfasserin aut A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022 Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. global optimization (dpeaa)DE-He213 metaheuristic algorithm (dpeaa)DE-He213 parameter setting (dpeaa)DE-He213 parameter control (dpeaa)DE-He213 Enthalten in Journal of applied and industrial mathematics Moscow : MAIK Nauka/Interperiodica Publ., 2007 16(2022), 4 vom: Nov., Seite 776-788 (DE-627)546898742 (DE-600)2391568-7 1990-4797 nnns volume:16 year:2022 number:4 month:11 pages:776-788 https://dx.doi.org/10.1134/S1990478922040172 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 16 2022 4 11 776-788 |
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10.1134/S1990478922040172 doi (DE-627)SPR051527642 (SPR)S1990478922040172-e DE-627 ger DE-627 rakwb eng Seliverstov, E. Yu. verfasserin aut A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022 Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. global optimization (dpeaa)DE-He213 metaheuristic algorithm (dpeaa)DE-He213 parameter setting (dpeaa)DE-He213 parameter control (dpeaa)DE-He213 Enthalten in Journal of applied and industrial mathematics Moscow : MAIK Nauka/Interperiodica Publ., 2007 16(2022), 4 vom: Nov., Seite 776-788 (DE-627)546898742 (DE-600)2391568-7 1990-4797 nnns volume:16 year:2022 number:4 month:11 pages:776-788 https://dx.doi.org/10.1134/S1990478922040172 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 16 2022 4 11 776-788 |
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10.1134/S1990478922040172 doi (DE-627)SPR051527642 (SPR)S1990478922040172-e DE-627 ger DE-627 rakwb eng Seliverstov, E. Yu. verfasserin aut A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022 Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. global optimization (dpeaa)DE-He213 metaheuristic algorithm (dpeaa)DE-He213 parameter setting (dpeaa)DE-He213 parameter control (dpeaa)DE-He213 Enthalten in Journal of applied and industrial mathematics Moscow : MAIK Nauka/Interperiodica Publ., 2007 16(2022), 4 vom: Nov., Seite 776-788 (DE-627)546898742 (DE-600)2391568-7 1990-4797 nnns volume:16 year:2022 number:4 month:11 pages:776-788 https://dx.doi.org/10.1134/S1990478922040172 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 16 2022 4 11 776-788 |
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Seliverstov, E. Yu. |
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Seliverstov, E. Yu. misc global optimization misc metaheuristic algorithm misc parameter setting misc parameter control A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms |
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A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms global optimization (dpeaa)DE-He213 metaheuristic algorithm (dpeaa)DE-He213 parameter setting (dpeaa)DE-He213 parameter control (dpeaa)DE-He213 |
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hierarchical method of parameter setting for population-based metaheuristic optimization algorithms |
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A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms |
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Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. © Pleiades Publishing, Ltd. 2022 |
| abstractGer |
Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. © Pleiades Publishing, Ltd. 2022 |
| abstract_unstemmed |
Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. © Pleiades Publishing, Ltd. 2022 |
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2024-07-03T22:19:14.250Z |
| _version_ |
1803598067663896577 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR051527642</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510064757.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230510s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1134/S1990478922040172</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051527642</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)S1990478922040172-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Seliverstov, E. Yu.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Pleiades Publishing, Ltd. 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">global optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">metaheuristic algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">parameter setting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">parameter control</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of applied and industrial mathematics</subfield><subfield code="d">Moscow : MAIK Nauka/Interperiodica Publ., 2007</subfield><subfield code="g">16(2022), 4 vom: Nov., Seite 776-788</subfield><subfield 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