Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm

The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expens...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Maurya, Vikas Kumar [verfasserIn] Nanda, Satyasai Jagannath [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Dynamic multi-objective optimization Home appliances scheduling SPEA2 Fuzzy based tuning of SPEA2 parameters Time-varying Pareto optimal fronts Borda count method

Übergeordnetes Werk:	Enthalten in: Engineering applications of artificial intelligence - Amsterdam [u.a.] : Elsevier Science, 1988, 121
Übergeordnetes Werk:	volume:121

DOI / URN:	10.1016/j.engappai.2023.105944

Katalog-ID:	ELV009531998

Internformat


LEADER	01000caa a22002652 4500
001	ELV009531998
003	DE-627
005	20231027093051.0
007	cr uuu---uuuuu
008	230511s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.engappai.2023.105944 \|2 doi
035			\|a (DE-627)ELV009531998
035			\|a (ELSEVIER)S0952-1976(23)00128-8
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 004 \|q VZ
084			\|a 50.23 \|2 bkl
084			\|a 54.72 \|2 bkl
100	1		\|a Maurya, Vikas Kumar \|e verfasserin \|4 aut
245	1	0	\|a Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm
264		1	\|c 2023
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.
650		4	\|a Dynamic multi-objective optimization
650		4	\|a Home appliances scheduling
650		4	\|a SPEA2
650		4	\|a Fuzzy based tuning of SPEA2 parameters
650		4	\|a Time-varying Pareto optimal fronts
650		4	\|a Borda count method
700	1		\|a Nanda, Satyasai Jagannath \|e verfasserin \|0 (orcid)0000-0002-4005-5589 \|4 aut
773	0	8	\|i Enthalten in \|t Engineering applications of artificial intelligence \|d Amsterdam [u.a.] : Elsevier Science, 1988 \|g 121 \|h Online-Ressource \|w (DE-627)308447832 \|w (DE-600)1502275-4 \|w (DE-576)094752524 \|x 0952-1976 \|7 nnns
773	1	8	\|g volume:121
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 50.23 \|j Regelungstechnik \|j Steuerungstechnik \|q VZ
936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
952			\|d 121

Indexfelder

author_variant	v k m vk vkm s j n sj sjn
matchkey_str	article:09521976:2023----::ieaynmlibetvsatoeplacsceuigsnfzydp
hierarchy_sort_str	2023
bklnumber	50.23 54.72
publishDate	2023
allfields	10.1016/j.engappai.2023.105944 doi (DE-627)ELV009531998 (ELSEVIER)S0952-1976(23)00128-8 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Maurya, Vikas Kumar verfasserin aut Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use. Dynamic multi-objective optimization Home appliances scheduling SPEA2 Fuzzy based tuning of SPEA2 parameters Time-varying Pareto optimal fronts Borda count method Nanda, Satyasai Jagannath verfasserin (orcid)0000-0002-4005-5589 aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121
spelling	10.1016/j.engappai.2023.105944 doi (DE-627)ELV009531998 (ELSEVIER)S0952-1976(23)00128-8 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Maurya, Vikas Kumar verfasserin aut Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use. Dynamic multi-objective optimization Home appliances scheduling SPEA2 Fuzzy based tuning of SPEA2 parameters Time-varying Pareto optimal fronts Borda count method Nanda, Satyasai Jagannath verfasserin (orcid)0000-0002-4005-5589 aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121
allfields_unstemmed	10.1016/j.engappai.2023.105944 doi (DE-627)ELV009531998 (ELSEVIER)S0952-1976(23)00128-8 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Maurya, Vikas Kumar verfasserin aut Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use. Dynamic multi-objective optimization Home appliances scheduling SPEA2 Fuzzy based tuning of SPEA2 parameters Time-varying Pareto optimal fronts Borda count method Nanda, Satyasai Jagannath verfasserin (orcid)0000-0002-4005-5589 aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121
allfieldsGer	10.1016/j.engappai.2023.105944 doi (DE-627)ELV009531998 (ELSEVIER)S0952-1976(23)00128-8 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Maurya, Vikas Kumar verfasserin aut Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use. Dynamic multi-objective optimization Home appliances scheduling SPEA2 Fuzzy based tuning of SPEA2 parameters Time-varying Pareto optimal fronts Borda count method Nanda, Satyasai Jagannath verfasserin (orcid)0000-0002-4005-5589 aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121
allfieldsSound	10.1016/j.engappai.2023.105944 doi (DE-627)ELV009531998 (ELSEVIER)S0952-1976(23)00128-8 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Maurya, Vikas Kumar verfasserin aut Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use. Dynamic multi-objective optimization Home appliances scheduling SPEA2 Fuzzy based tuning of SPEA2 parameters Time-varying Pareto optimal fronts Borda count method Nanda, Satyasai Jagannath verfasserin (orcid)0000-0002-4005-5589 aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121
language	English
source	Enthalten in Engineering applications of artificial intelligence 121 volume:121
sourceStr	Enthalten in Engineering applications of artificial intelligence 121 volume:121
format_phy_str_mv	Article
bklname	Regelungstechnik Steuerungstechnik Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Dynamic multi-objective optimization Home appliances scheduling SPEA2 Fuzzy based tuning of SPEA2 parameters Time-varying Pareto optimal fronts Borda count method
dewey-raw	004
isfreeaccess_bool	false
container_title	Engineering applications of artificial intelligence
authorswithroles_txt_mv	Maurya, Vikas Kumar @@aut@@ Nanda, Satyasai Jagannath @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	308447832
dewey-sort	14
id	ELV009531998
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV009531998</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231027093051.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230511s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.engappai.2023.105944</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV009531998</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0952-1976(23)00128-8</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.23</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Maurya, Vikas Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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="520" ind1=" " ind2=" "><subfield code="a">The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamic multi-objective optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Home appliances scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SPEA2</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy based tuning of SPEA2 parameters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-varying Pareto optimal fronts</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Borda count method</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nanda, Satyasai Jagannath</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-4005-5589</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Engineering applications of artificial intelligence</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1988</subfield><subfield code="g">121</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)308447832</subfield><subfield code="w">(DE-600)1502275-4</subfield><subfield code="w">(DE-576)094752524</subfield><subfield code="x">0952-1976</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:121</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.23</subfield><subfield code="j">Regelungstechnik</subfield><subfield code="j">Steuerungstechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">121</subfield></datafield></record></collection>
author	Maurya, Vikas Kumar
spellingShingle	Maurya, Vikas Kumar ddc 004 bkl 50.23 bkl 54.72 misc Dynamic multi-objective optimization misc Home appliances scheduling misc SPEA2 misc Fuzzy based tuning of SPEA2 parameters misc Time-varying Pareto optimal fronts misc Borda count method Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm
authorStr	Maurya, Vikas Kumar
ppnlink_with_tag_str_mv	@@773@@(DE-627)308447832
format	electronic Article
dewey-ones	004 - Data processing & computer science
delete_txt_mv	keep
author_role	aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	0952-1976
topic_title	004 VZ 50.23 bkl 54.72 bkl Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm Dynamic multi-objective optimization Home appliances scheduling SPEA2 Fuzzy based tuning of SPEA2 parameters Time-varying Pareto optimal fronts Borda count method
topic	ddc 004 bkl 50.23 bkl 54.72 misc Dynamic multi-objective optimization misc Home appliances scheduling misc SPEA2 misc Fuzzy based tuning of SPEA2 parameters misc Time-varying Pareto optimal fronts misc Borda count method
topic_unstemmed	ddc 004 bkl 50.23 bkl 54.72 misc Dynamic multi-objective optimization misc Home appliances scheduling misc SPEA2 misc Fuzzy based tuning of SPEA2 parameters misc Time-varying Pareto optimal fronts misc Borda count method
topic_browse	ddc 004 bkl 50.23 bkl 54.72 misc Dynamic multi-objective optimization misc Home appliances scheduling misc SPEA2 misc Fuzzy based tuning of SPEA2 parameters misc Time-varying Pareto optimal fronts misc Borda count method
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Engineering applications of artificial intelligence
hierarchy_parent_id	308447832
dewey-tens	000 - Computer science, knowledge & systems
hierarchy_top_title	Engineering applications of artificial intelligence
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524
title	Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm
ctrlnum	(DE-627)ELV009531998 (ELSEVIER)S0952-1976(23)00128-8
title_full	Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm
author_sort	Maurya, Vikas Kumar
journal	Engineering applications of artificial intelligence
journalStr	Engineering applications of artificial intelligence
lang_code	eng
isOA_bool	false
dewey-hundreds	000 - Computer science, information & general works
recordtype	marc
publishDateSort	2023
contenttype_str_mv	zzz
author_browse	Maurya, Vikas Kumar Nanda, Satyasai Jagannath
container_volume	121
class	004 VZ 50.23 bkl 54.72 bkl
format_se	Elektronische Aufsätze
author-letter	Maurya, Vikas Kumar
doi_str_mv	10.1016/j.engappai.2023.105944
normlink	(ORCID)0000-0002-4005-5589
normlink_prefix_str_mv	(orcid)0000-0002-4005-5589
dewey-full	004
author2-role	verfasserin
title_sort	time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic spea2 algorithm
title_auth	Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm
abstract	The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.
abstractGer	The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.
abstract_unstemmed	The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.
collection_details	GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700
title_short	Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm
remote_bool	true
author2	Nanda, Satyasai Jagannath
author2Str	Nanda, Satyasai Jagannath
ppnlink	308447832
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.engappai.2023.105944
up_date	2024-07-06T23:29:06.363Z
_version_	1803874254313226240
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV009531998</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231027093051.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230511s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.engappai.2023.105944</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV009531998</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0952-1976(23)00128-8</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.23</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Maurya, Vikas Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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="520" ind1=" " ind2=" "><subfield code="a">The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamic multi-objective optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Home appliances scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SPEA2</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy based tuning of SPEA2 parameters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-varying Pareto optimal fronts</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Borda count method</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nanda, Satyasai Jagannath</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-4005-5589</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Engineering applications of artificial intelligence</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1988</subfield><subfield code="g">121</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)308447832</subfield><subfield code="w">(DE-600)1502275-4</subfield><subfield code="w">(DE-576)094752524</subfield><subfield code="x">0952-1976</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:121</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.23</subfield><subfield code="j">Regelungstechnik</subfield><subfield code="j">Steuerungstechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">121</subfield></datafield></record></collection>
score	7.3994513

Nicht das Richtige dabei?

Schreiben Sie uns!

Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?