A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions
Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Ke [verfasserIn] Hao, Wen-Ning [verfasserIn] Yu, Xiao-Han [verfasserIn] Jin, Da-Wei [verfasserIn] Zhang, Zhong-Hui [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 22(2020), 8 vom: 07. Okt., Seite 2449-2465 |
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| Übergeordnetes Werk: |
volume:22 ; year:2020 ; number:8 ; day:07 ; month:10 ; pages:2449-2465 |
| Links: |
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| DOI / URN: |
10.1007/s40815-020-00954-2 |
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| Katalog-ID: |
SPR042274303 |
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| 520 | |a Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. | ||
| 650 | 4 | |a Multitasking genetic fuzzy system |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Evolutionary multitasking |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multifactorial optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mamdani fuzzy system |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fully overlapping triangle membership functions |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Hao, Wen-Ning |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Xiao-Han |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Da-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhong-Hui |e verfasserin |4 aut | |
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10.1007/s40815-020-00954-2 doi (DE-627)SPR042274303 (DE-599)SPRs40815-020-00954-2-e (SPR)s40815-020-00954-2-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Zhang, Ke verfasserin aut A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. Multitasking genetic fuzzy system (dpeaa)DE-He213 Evolutionary multitasking (dpeaa)DE-He213 Multifactorial optimization (dpeaa)DE-He213 Mamdani fuzzy system (dpeaa)DE-He213 Fully overlapping triangle membership functions (dpeaa)DE-He213 Hao, Wen-Ning verfasserin aut Yu, Xiao-Han verfasserin aut Jin, Da-Wei verfasserin aut Zhang, Zhong-Hui verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 22(2020), 8 vom: 07. Okt., Seite 2449-2465 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:22 year:2020 number:8 day:07 month:10 pages:2449-2465 https://dx.doi.org/10.1007/s40815-020-00954-2 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 31.78 ASE AR 22 2020 8 07 10 2449-2465 |
| spelling |
10.1007/s40815-020-00954-2 doi (DE-627)SPR042274303 (DE-599)SPRs40815-020-00954-2-e (SPR)s40815-020-00954-2-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Zhang, Ke verfasserin aut A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. Multitasking genetic fuzzy system (dpeaa)DE-He213 Evolutionary multitasking (dpeaa)DE-He213 Multifactorial optimization (dpeaa)DE-He213 Mamdani fuzzy system (dpeaa)DE-He213 Fully overlapping triangle membership functions (dpeaa)DE-He213 Hao, Wen-Ning verfasserin aut Yu, Xiao-Han verfasserin aut Jin, Da-Wei verfasserin aut Zhang, Zhong-Hui verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 22(2020), 8 vom: 07. Okt., Seite 2449-2465 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:22 year:2020 number:8 day:07 month:10 pages:2449-2465 https://dx.doi.org/10.1007/s40815-020-00954-2 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 31.78 ASE AR 22 2020 8 07 10 2449-2465 |
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10.1007/s40815-020-00954-2 doi (DE-627)SPR042274303 (DE-599)SPRs40815-020-00954-2-e (SPR)s40815-020-00954-2-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Zhang, Ke verfasserin aut A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. Multitasking genetic fuzzy system (dpeaa)DE-He213 Evolutionary multitasking (dpeaa)DE-He213 Multifactorial optimization (dpeaa)DE-He213 Mamdani fuzzy system (dpeaa)DE-He213 Fully overlapping triangle membership functions (dpeaa)DE-He213 Hao, Wen-Ning verfasserin aut Yu, Xiao-Han verfasserin aut Jin, Da-Wei verfasserin aut Zhang, Zhong-Hui verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 22(2020), 8 vom: 07. Okt., Seite 2449-2465 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:22 year:2020 number:8 day:07 month:10 pages:2449-2465 https://dx.doi.org/10.1007/s40815-020-00954-2 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 31.78 ASE AR 22 2020 8 07 10 2449-2465 |
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10.1007/s40815-020-00954-2 doi (DE-627)SPR042274303 (DE-599)SPRs40815-020-00954-2-e (SPR)s40815-020-00954-2-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Zhang, Ke verfasserin aut A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. Multitasking genetic fuzzy system (dpeaa)DE-He213 Evolutionary multitasking (dpeaa)DE-He213 Multifactorial optimization (dpeaa)DE-He213 Mamdani fuzzy system (dpeaa)DE-He213 Fully overlapping triangle membership functions (dpeaa)DE-He213 Hao, Wen-Ning verfasserin aut Yu, Xiao-Han verfasserin aut Jin, Da-Wei verfasserin aut Zhang, Zhong-Hui verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 22(2020), 8 vom: 07. Okt., Seite 2449-2465 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:22 year:2020 number:8 day:07 month:10 pages:2449-2465 https://dx.doi.org/10.1007/s40815-020-00954-2 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 31.78 ASE AR 22 2020 8 07 10 2449-2465 |
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10.1007/s40815-020-00954-2 doi (DE-627)SPR042274303 (DE-599)SPRs40815-020-00954-2-e (SPR)s40815-020-00954-2-e DE-627 ger DE-627 rakwb eng 510 ASE 31.78 bkl Zhang, Ke verfasserin aut A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. Multitasking genetic fuzzy system (dpeaa)DE-He213 Evolutionary multitasking (dpeaa)DE-He213 Multifactorial optimization (dpeaa)DE-He213 Mamdani fuzzy system (dpeaa)DE-He213 Fully overlapping triangle membership functions (dpeaa)DE-He213 Hao, Wen-Ning verfasserin aut Yu, Xiao-Han verfasserin aut Jin, Da-Wei verfasserin aut Zhang, Zhong-Hui verfasserin aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 22(2020), 8 vom: 07. Okt., Seite 2449-2465 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:22 year:2020 number:8 day:07 month:10 pages:2449-2465 https://dx.doi.org/10.1007/s40815-020-00954-2 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 31.78 ASE AR 22 2020 8 07 10 2449-2465 |
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Enthalten in International journal of fuzzy systems 22(2020), 8 vom: 07. Okt., Seite 2449-2465 volume:22 year:2020 number:8 day:07 month:10 pages:2449-2465 |
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Multitasking genetic fuzzy system Evolutionary multitasking Multifactorial optimization Mamdani fuzzy system Fully overlapping triangle membership functions |
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Zhang, Ke @@aut@@ Hao, Wen-Ning @@aut@@ Yu, Xiao-Han @@aut@@ Jin, Da-Wei @@aut@@ Zhang, Zhong-Hui @@aut@@ |
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By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multitasking genetic fuzzy system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary multitasking</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multifactorial optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mamdani fuzzy system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fully overlapping triangle membership functions</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hao, Wen-Ning</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Xiao-Han</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jin, Da-Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Zhong-Hui</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">International journal of fuzzy systems</subfield><subfield code="d">Taibei : Association, 2006</subfield><subfield code="g">22(2020), 8 vom: 07. 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Zhang, Ke |
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Zhang, Ke ddc 510 bkl 31.78 misc Multitasking genetic fuzzy system misc Evolutionary multitasking misc Multifactorial optimization misc Mamdani fuzzy system misc Fully overlapping triangle membership functions A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions |
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510 ASE 31.78 bkl A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions Multitasking genetic fuzzy system (dpeaa)DE-He213 Evolutionary multitasking (dpeaa)DE-He213 Multifactorial optimization (dpeaa)DE-He213 Mamdani fuzzy system (dpeaa)DE-He213 Fully overlapping triangle membership functions (dpeaa)DE-He213 |
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ddc 510 bkl 31.78 misc Multitasking genetic fuzzy system misc Evolutionary multitasking misc Multifactorial optimization misc Mamdani fuzzy system misc Fully overlapping triangle membership functions |
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ddc 510 bkl 31.78 misc Multitasking genetic fuzzy system misc Evolutionary multitasking misc Multifactorial optimization misc Mamdani fuzzy system misc Fully overlapping triangle membership functions |
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Zhang, Ke Hao, Wen-Ning Yu, Xiao-Han Jin, Da-Wei Zhang, Zhong-Hui |
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multitasking genetic algorithm for mamdani fuzzy system with fully overlapping triangle membership functions |
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A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions |
| abstract |
Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. |
| abstractGer |
Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. |
| abstract_unstemmed |
Abstract Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average. |
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| container_issue |
8 |
| title_short |
A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions |
| url |
https://dx.doi.org/10.1007/s40815-020-00954-2 |
| remote_bool |
true |
| author2 |
Hao, Wen-Ning Yu, Xiao-Han Jin, Da-Wei Zhang, Zhong-Hui |
| author2Str |
Hao, Wen-Ning Yu, Xiao-Han Jin, Da-Wei Zhang, Zhong-Hui |
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| doi_str |
10.1007/s40815-020-00954-2 |
| up_date |
2024-07-04T01:29:54.906Z |
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| score |
7.399722 |