An improved multi-objective learning automata and its application in VLSI circuit design

Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule f...
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Autor*in:	Sayyadi Shahraki, Najmeh [verfasserIn] Zahiri, Seyed Hamid [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Multi-objective optimization Improved learning automata Integrated circuit design Pareto-front

Übergeordnetes Werk:	Enthalten in: Memetic computing - Berlin : Springer, 2009, 12(2020), 2 vom: 18. Mai, Seite 115-128
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:2 ; day:18 ; month:05 ; pages:115-128

Links:	Volltext

DOI / URN:	10.1007/s12293-020-00303-8

Katalog-ID:	SPR039866203

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520			\|a Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis.
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publishDate	2020
allfields	10.1007/s12293-020-00303-8 doi (DE-627)SPR039866203 (SPR)s12293-020-00303-8-e DE-627 ger DE-627 rakwb eng 004 ASE Sayyadi Shahraki, Najmeh verfasserin aut An improved multi-objective learning automata and its application in VLSI circuit design 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis. Multi-objective optimization (dpeaa)DE-He213 Improved learning automata (dpeaa)DE-He213 Integrated circuit design (dpeaa)DE-He213 Pareto-front (dpeaa)DE-He213 Zahiri, Seyed Hamid verfasserin aut Enthalten in Memetic computing Berlin : Springer, 2009 12(2020), 2 vom: 18. Mai, Seite 115-128 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:12 year:2020 number:2 day:18 month:05 pages:115-128 https://dx.doi.org/10.1007/s12293-020-00303-8 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 2 18 05 115-128
spelling	10.1007/s12293-020-00303-8 doi (DE-627)SPR039866203 (SPR)s12293-020-00303-8-e DE-627 ger DE-627 rakwb eng 004 ASE Sayyadi Shahraki, Najmeh verfasserin aut An improved multi-objective learning automata and its application in VLSI circuit design 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis. Multi-objective optimization (dpeaa)DE-He213 Improved learning automata (dpeaa)DE-He213 Integrated circuit design (dpeaa)DE-He213 Pareto-front (dpeaa)DE-He213 Zahiri, Seyed Hamid verfasserin aut Enthalten in Memetic computing Berlin : Springer, 2009 12(2020), 2 vom: 18. Mai, Seite 115-128 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:12 year:2020 number:2 day:18 month:05 pages:115-128 https://dx.doi.org/10.1007/s12293-020-00303-8 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 2 18 05 115-128
allfields_unstemmed	10.1007/s12293-020-00303-8 doi (DE-627)SPR039866203 (SPR)s12293-020-00303-8-e DE-627 ger DE-627 rakwb eng 004 ASE Sayyadi Shahraki, Najmeh verfasserin aut An improved multi-objective learning automata and its application in VLSI circuit design 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis. Multi-objective optimization (dpeaa)DE-He213 Improved learning automata (dpeaa)DE-He213 Integrated circuit design (dpeaa)DE-He213 Pareto-front (dpeaa)DE-He213 Zahiri, Seyed Hamid verfasserin aut Enthalten in Memetic computing Berlin : Springer, 2009 12(2020), 2 vom: 18. Mai, Seite 115-128 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:12 year:2020 number:2 day:18 month:05 pages:115-128 https://dx.doi.org/10.1007/s12293-020-00303-8 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 2 18 05 115-128
allfieldsGer	10.1007/s12293-020-00303-8 doi (DE-627)SPR039866203 (SPR)s12293-020-00303-8-e DE-627 ger DE-627 rakwb eng 004 ASE Sayyadi Shahraki, Najmeh verfasserin aut An improved multi-objective learning automata and its application in VLSI circuit design 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis. Multi-objective optimization (dpeaa)DE-He213 Improved learning automata (dpeaa)DE-He213 Integrated circuit design (dpeaa)DE-He213 Pareto-front (dpeaa)DE-He213 Zahiri, Seyed Hamid verfasserin aut Enthalten in Memetic computing Berlin : Springer, 2009 12(2020), 2 vom: 18. Mai, Seite 115-128 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:12 year:2020 number:2 day:18 month:05 pages:115-128 https://dx.doi.org/10.1007/s12293-020-00303-8 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 2 18 05 115-128
allfieldsSound	10.1007/s12293-020-00303-8 doi (DE-627)SPR039866203 (SPR)s12293-020-00303-8-e DE-627 ger DE-627 rakwb eng 004 ASE Sayyadi Shahraki, Najmeh verfasserin aut An improved multi-objective learning automata and its application in VLSI circuit design 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis. Multi-objective optimization (dpeaa)DE-He213 Improved learning automata (dpeaa)DE-He213 Integrated circuit design (dpeaa)DE-He213 Pareto-front (dpeaa)DE-He213 Zahiri, Seyed Hamid verfasserin aut Enthalten in Memetic computing Berlin : Springer, 2009 12(2020), 2 vom: 18. Mai, Seite 115-128 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:12 year:2020 number:2 day:18 month:05 pages:115-128 https://dx.doi.org/10.1007/s12293-020-00303-8 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 2 18 05 115-128
language	English
source	Enthalten in Memetic computing 12(2020), 2 vom: 18. Mai, Seite 115-128 volume:12 year:2020 number:2 day:18 month:05 pages:115-128
sourceStr	Enthalten in Memetic computing 12(2020), 2 vom: 18. Mai, Seite 115-128 volume:12 year:2020 number:2 day:18 month:05 pages:115-128
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multi-objective optimization Improved learning automata Integrated circuit design Pareto-front
dewey-raw	004
isfreeaccess_bool	false
container_title	Memetic computing
authorswithroles_txt_mv	Sayyadi Shahraki, Najmeh @@aut@@ Zahiri, Seyed Hamid @@aut@@
publishDateDaySort_date	2020-05-18T00:00:00Z
hierarchy_top_id	597545006
dewey-sort	14
id	SPR039866203
language_de	englisch
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author	Sayyadi Shahraki, Najmeh
spellingShingle	Sayyadi Shahraki, Najmeh ddc 004 misc Multi-objective optimization misc Improved learning automata misc Integrated circuit design misc Pareto-front An improved multi-objective learning automata and its application in VLSI circuit design
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topic_title	004 ASE An improved multi-objective learning automata and its application in VLSI circuit design Multi-objective optimization (dpeaa)DE-He213 Improved learning automata (dpeaa)DE-He213 Integrated circuit design (dpeaa)DE-He213 Pareto-front (dpeaa)DE-He213
topic	ddc 004 misc Multi-objective optimization misc Improved learning automata misc Integrated circuit design misc Pareto-front
topic_unstemmed	ddc 004 misc Multi-objective optimization misc Improved learning automata misc Integrated circuit design misc Pareto-front
topic_browse	ddc 004 misc Multi-objective optimization misc Improved learning automata misc Integrated circuit design misc Pareto-front
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title	An improved multi-objective learning automata and its application in VLSI circuit design
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title_full	An improved multi-objective learning automata and its application in VLSI circuit design
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author_browse	Sayyadi Shahraki, Najmeh Zahiri, Seyed Hamid
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title_sort	improved multi-objective learning automata and its application in vlsi circuit design
title_auth	An improved multi-objective learning automata and its application in VLSI circuit design
abstract	Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis.
abstractGer	Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis.
abstract_unstemmed	Abstract In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis.
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title_short	An improved multi-objective learning automata and its application in VLSI circuit design
url	https://dx.doi.org/10.1007/s12293-020-00303-8
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doi_str	10.1007/s12293-020-00303-8
up_date	2024-07-04T01:55:21.902Z
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The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. 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