An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable
Abstract Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, t...
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Gespeichert in:
| Autor*in: |
Wang, Wenxiang [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: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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: Neural computing & applications - London : Springer, 1993, 34(2022), 22 vom: 28. Aug., Seite 19703-19721 |
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| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:22 ; day:28 ; month:08 ; pages:19703-19721 |
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| DOI / URN: |
10.1007/s00521-022-07695-3 |
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| Katalog-ID: |
SPR048397466 |
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| 245 | 1 | 3 | |a An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable |
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| 520 | |a Abstract Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. | ||
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| 650 | 4 | |a Scalable histogram |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Li, Kangshun |0 (orcid)0000-0002-0429-446X |4 aut | |
| 700 | 1 | |a Jalil, Hassan |4 aut | |
| 700 | 1 | |a Wang, Hui |4 aut | |
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10.1007/s00521-022-07695-3 doi (DE-627)SPR048397466 (SPR)s00521-022-07695-3-e DE-627 ger DE-627 rakwb eng Wang, Wenxiang verfasserin aut An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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 Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. Multi-objective optimization (dpeaa)DE-He213 Mixed-variable (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Estimation of distribution algorithm (dpeaa)DE-He213 Scalable histogram (dpeaa)DE-He213 Li, Kangshun (orcid)0000-0002-0429-446X aut Jalil, Hassan aut Wang, Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 22 vom: 28. Aug., Seite 19703-19721 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:22 day:28 month:08 pages:19703-19721 https://dx.doi.org/10.1007/s00521-022-07695-3 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2119 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 34 2022 22 28 08 19703-19721 |
| spelling |
10.1007/s00521-022-07695-3 doi (DE-627)SPR048397466 (SPR)s00521-022-07695-3-e DE-627 ger DE-627 rakwb eng Wang, Wenxiang verfasserin aut An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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 Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. Multi-objective optimization (dpeaa)DE-He213 Mixed-variable (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Estimation of distribution algorithm (dpeaa)DE-He213 Scalable histogram (dpeaa)DE-He213 Li, Kangshun (orcid)0000-0002-0429-446X aut Jalil, Hassan aut Wang, Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 22 vom: 28. Aug., Seite 19703-19721 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:22 day:28 month:08 pages:19703-19721 https://dx.doi.org/10.1007/s00521-022-07695-3 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2119 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 34 2022 22 28 08 19703-19721 |
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10.1007/s00521-022-07695-3 doi (DE-627)SPR048397466 (SPR)s00521-022-07695-3-e DE-627 ger DE-627 rakwb eng Wang, Wenxiang verfasserin aut An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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 Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. Multi-objective optimization (dpeaa)DE-He213 Mixed-variable (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Estimation of distribution algorithm (dpeaa)DE-He213 Scalable histogram (dpeaa)DE-He213 Li, Kangshun (orcid)0000-0002-0429-446X aut Jalil, Hassan aut Wang, Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 22 vom: 28. Aug., Seite 19703-19721 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:22 day:28 month:08 pages:19703-19721 https://dx.doi.org/10.1007/s00521-022-07695-3 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2119 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 34 2022 22 28 08 19703-19721 |
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10.1007/s00521-022-07695-3 doi (DE-627)SPR048397466 (SPR)s00521-022-07695-3-e DE-627 ger DE-627 rakwb eng Wang, Wenxiang verfasserin aut An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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 Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. Multi-objective optimization (dpeaa)DE-He213 Mixed-variable (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Estimation of distribution algorithm (dpeaa)DE-He213 Scalable histogram (dpeaa)DE-He213 Li, Kangshun (orcid)0000-0002-0429-446X aut Jalil, Hassan aut Wang, Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 22 vom: 28. Aug., Seite 19703-19721 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:22 day:28 month:08 pages:19703-19721 https://dx.doi.org/10.1007/s00521-022-07695-3 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2119 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 34 2022 22 28 08 19703-19721 |
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10.1007/s00521-022-07695-3 doi (DE-627)SPR048397466 (SPR)s00521-022-07695-3-e DE-627 ger DE-627 rakwb eng Wang, Wenxiang verfasserin aut An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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 Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. Multi-objective optimization (dpeaa)DE-He213 Mixed-variable (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Estimation of distribution algorithm (dpeaa)DE-He213 Scalable histogram (dpeaa)DE-He213 Li, Kangshun (orcid)0000-0002-0429-446X aut Jalil, Hassan aut Wang, Hui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 22 vom: 28. Aug., Seite 19703-19721 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:22 day:28 month:08 pages:19703-19721 https://dx.doi.org/10.1007/s00521-022-07695-3 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2119 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 34 2022 22 28 08 19703-19721 |
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Wang, Wenxiang |
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An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable Multi-objective optimization (dpeaa)DE-He213 Mixed-variable (dpeaa)DE-He213 Evolutionary algorithm (dpeaa)DE-He213 Estimation of distribution algorithm (dpeaa)DE-He213 Scalable histogram (dpeaa)DE-He213 |
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improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable |
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An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable |
| abstract |
Abstract Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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 Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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 Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor 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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An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable |
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https://dx.doi.org/10.1007/s00521-022-07695-3 |
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Li, Kangshun Jalil, Hassan Wang, Hui |
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Li, Kangshun Jalil, Hassan Wang, Hui |
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10.1007/s00521-022-07695-3 |
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2024-07-03T18:56:53.376Z |
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| score |
7.4015837 |