Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits

This paper details the Multiobjective Differential Evolution algorithm (MODE) using crowding distance for the sizing of analog circuits. MODE is used to compute the Pareto front of a biobjective optimization problem, namely maximizing the high current cut-off frequency and minimizing the parasitic i...
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Autor*in:	Abbas El Dor [verfasserIn] Mourad Fakhfakh [verfasserIn] Patrick Siarry [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Multiobjective optimization MODE; NSGA-II CMOS Metaheuristics MODE NSGA-II Second generation current conveyor

Übergeordnetes Werk:	In: Journal of Electrical Systems - ESRGroups, 2007, 12(2016), 3, Seite 612-622
Übergeordnetes Werk:	volume:12 ; year:2016 ; number:3 ; pages:612-622

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Katalog-ID:	DOAJ027610756

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spelling	(DE-627)DOAJ027610756 (DE-599)DOAJ6a986291c86849ce9f51aefc9e7b59fc DE-627 ger DE-627 rakwb eng TK1-9971 Abbas El Dor verfasserin aut Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper details the Multiobjective Differential Evolution algorithm (MODE) using crowding distance for the sizing of analog circuits. MODE is used to compute the Pareto front of a biobjective optimization problem, namely maximizing the high current cut-off frequency and minimizing the parasitic input resistance of a second generation current conveyor. To highlight performances of MODE, comparisons with the non-sorting genetic algorithm (NSGA-II) were performed. These comparisons show that MODE outperforms NSGA-II in terms of quality of the optimal solutions, diversity of those solutions along the Pareto front, and computing time. Multiobjective optimization MODE; NSGA-II CMOS Metaheuristics MODE NSGA-II Second generation current conveyor Electrical engineering. Electronics. Nuclear engineering Mourad Fakhfakh verfasserin aut Patrick Siarry verfasserin aut In Journal of Electrical Systems ESRGroups, 2007 12(2016), 3, Seite 612-622 (DE-627)550219269 (DE-600)2397001-7 11125209 nnns volume:12 year:2016 number:3 pages:612-622 https://doaj.org/article/6a986291c86849ce9f51aefc9e7b59fc kostenfrei http://journal.esrgroups.org/jes/papers/12_3_14.pdf kostenfrei https://doaj.org/toc/1112-5209 Journal toc kostenfrei https://doaj.org/toc/1112-5209 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 3 612-622
allfields_unstemmed	(DE-627)DOAJ027610756 (DE-599)DOAJ6a986291c86849ce9f51aefc9e7b59fc DE-627 ger DE-627 rakwb eng TK1-9971 Abbas El Dor verfasserin aut Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper details the Multiobjective Differential Evolution algorithm (MODE) using crowding distance for the sizing of analog circuits. MODE is used to compute the Pareto front of a biobjective optimization problem, namely maximizing the high current cut-off frequency and minimizing the parasitic input resistance of a second generation current conveyor. To highlight performances of MODE, comparisons with the non-sorting genetic algorithm (NSGA-II) were performed. These comparisons show that MODE outperforms NSGA-II in terms of quality of the optimal solutions, diversity of those solutions along the Pareto front, and computing time. Multiobjective optimization MODE; NSGA-II CMOS Metaheuristics MODE NSGA-II Second generation current conveyor Electrical engineering. Electronics. Nuclear engineering Mourad Fakhfakh verfasserin aut Patrick Siarry verfasserin aut In Journal of Electrical Systems ESRGroups, 2007 12(2016), 3, Seite 612-622 (DE-627)550219269 (DE-600)2397001-7 11125209 nnns volume:12 year:2016 number:3 pages:612-622 https://doaj.org/article/6a986291c86849ce9f51aefc9e7b59fc kostenfrei http://journal.esrgroups.org/jes/papers/12_3_14.pdf kostenfrei https://doaj.org/toc/1112-5209 Journal toc kostenfrei https://doaj.org/toc/1112-5209 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 3 612-622
allfieldsGer	(DE-627)DOAJ027610756 (DE-599)DOAJ6a986291c86849ce9f51aefc9e7b59fc DE-627 ger DE-627 rakwb eng TK1-9971 Abbas El Dor verfasserin aut Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper details the Multiobjective Differential Evolution algorithm (MODE) using crowding distance for the sizing of analog circuits. MODE is used to compute the Pareto front of a biobjective optimization problem, namely maximizing the high current cut-off frequency and minimizing the parasitic input resistance of a second generation current conveyor. To highlight performances of MODE, comparisons with the non-sorting genetic algorithm (NSGA-II) were performed. These comparisons show that MODE outperforms NSGA-II in terms of quality of the optimal solutions, diversity of those solutions along the Pareto front, and computing time. Multiobjective optimization MODE; NSGA-II CMOS Metaheuristics MODE NSGA-II Second generation current conveyor Electrical engineering. Electronics. Nuclear engineering Mourad Fakhfakh verfasserin aut Patrick Siarry verfasserin aut In Journal of Electrical Systems ESRGroups, 2007 12(2016), 3, Seite 612-622 (DE-627)550219269 (DE-600)2397001-7 11125209 nnns volume:12 year:2016 number:3 pages:612-622 https://doaj.org/article/6a986291c86849ce9f51aefc9e7b59fc kostenfrei http://journal.esrgroups.org/jes/papers/12_3_14.pdf kostenfrei https://doaj.org/toc/1112-5209 Journal toc kostenfrei https://doaj.org/toc/1112-5209 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 3 612-622
allfieldsSound	(DE-627)DOAJ027610756 (DE-599)DOAJ6a986291c86849ce9f51aefc9e7b59fc DE-627 ger DE-627 rakwb eng TK1-9971 Abbas El Dor verfasserin aut Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper details the Multiobjective Differential Evolution algorithm (MODE) using crowding distance for the sizing of analog circuits. MODE is used to compute the Pareto front of a biobjective optimization problem, namely maximizing the high current cut-off frequency and minimizing the parasitic input resistance of a second generation current conveyor. To highlight performances of MODE, comparisons with the non-sorting genetic algorithm (NSGA-II) were performed. These comparisons show that MODE outperforms NSGA-II in terms of quality of the optimal solutions, diversity of those solutions along the Pareto front, and computing time. Multiobjective optimization MODE; NSGA-II CMOS Metaheuristics MODE NSGA-II Second generation current conveyor Electrical engineering. Electronics. Nuclear engineering Mourad Fakhfakh verfasserin aut Patrick Siarry verfasserin aut In Journal of Electrical Systems ESRGroups, 2007 12(2016), 3, Seite 612-622 (DE-627)550219269 (DE-600)2397001-7 11125209 nnns volume:12 year:2016 number:3 pages:612-622 https://doaj.org/article/6a986291c86849ce9f51aefc9e7b59fc kostenfrei http://journal.esrgroups.org/jes/papers/12_3_14.pdf kostenfrei https://doaj.org/toc/1112-5209 Journal toc kostenfrei https://doaj.org/toc/1112-5209 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 3 612-622
language	English
source	In Journal of Electrical Systems 12(2016), 3, Seite 612-622 volume:12 year:2016 number:3 pages:612-622
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topic_facet	Multiobjective optimization MODE; NSGA-II CMOS Metaheuristics MODE NSGA-II Second generation current conveyor Electrical engineering. Electronics. Nuclear engineering
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container_title	Journal of Electrical Systems
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callnumber-first	T - Technology
author	Abbas El Dor
spellingShingle	Abbas El Dor misc TK1-9971 misc Multiobjective optimization misc MODE; NSGA-II misc CMOS misc Metaheuristics misc MODE misc NSGA-II misc Second generation current conveyor misc Electrical engineering. Electronics. Nuclear engineering Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits
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topic_title	TK1-9971 Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits Multiobjective optimization MODE; NSGA-II CMOS Metaheuristics MODE NSGA-II Second generation current conveyor
topic	misc TK1-9971 misc Multiobjective optimization misc MODE; NSGA-II misc CMOS misc Metaheuristics misc MODE misc NSGA-II misc Second generation current conveyor misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Multiobjective optimization misc MODE; NSGA-II misc CMOS misc Metaheuristics misc MODE misc NSGA-II misc Second generation current conveyor misc Electrical engineering. Electronics. Nuclear engineering
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title	Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits
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title_sort	multiobjective differential evolution algorithm using crowding distance for the optimal design of analog circuits
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title_auth	Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits
abstract	This paper details the Multiobjective Differential Evolution algorithm (MODE) using crowding distance for the sizing of analog circuits. MODE is used to compute the Pareto front of a biobjective optimization problem, namely maximizing the high current cut-off frequency and minimizing the parasitic input resistance of a second generation current conveyor. To highlight performances of MODE, comparisons with the non-sorting genetic algorithm (NSGA-II) were performed. These comparisons show that MODE outperforms NSGA-II in terms of quality of the optimal solutions, diversity of those solutions along the Pareto front, and computing time.
abstractGer	This paper details the Multiobjective Differential Evolution algorithm (MODE) using crowding distance for the sizing of analog circuits. MODE is used to compute the Pareto front of a biobjective optimization problem, namely maximizing the high current cut-off frequency and minimizing the parasitic input resistance of a second generation current conveyor. To highlight performances of MODE, comparisons with the non-sorting genetic algorithm (NSGA-II) were performed. These comparisons show that MODE outperforms NSGA-II in terms of quality of the optimal solutions, diversity of those solutions along the Pareto front, and computing time.
abstract_unstemmed	This paper details the Multiobjective Differential Evolution algorithm (MODE) using crowding distance for the sizing of analog circuits. MODE is used to compute the Pareto front of a biobjective optimization problem, namely maximizing the high current cut-off frequency and minimizing the parasitic input resistance of a second generation current conveyor. To highlight performances of MODE, comparisons with the non-sorting genetic algorithm (NSGA-II) were performed. These comparisons show that MODE outperforms NSGA-II in terms of quality of the optimal solutions, diversity of those solutions along the Pareto front, and computing time.
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title_short	Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits
url	https://doaj.org/article/6a986291c86849ce9f51aefc9e7b59fc http://journal.esrgroups.org/jes/papers/12_3_14.pdf https://doaj.org/toc/1112-5209
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Multiobjective Differential Evolution Algorithm using Crowding Distance for the Optimal Design of Analog Circuits

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