A novel hybrid backtracking search optimization algorithm for continuous function optimization

Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sukanta Nama [verfasserIn] Apu Kumar Saha [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Backtracking Search Optimization Algorithm (BSA) Quadratic approximation (QA) Hybrid Algorithm Unconstrained non-linear function optimization

Übergeordnetes Werk:	In: Decision Science Letters - Growing Science, 2013, 8(2019), 2, Seite 163-174
Übergeordnetes Werk:	volume:8 ; year:2019 ; number:2 ; pages:163-174

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.5267/j.dsl.2018.7.002

Katalog-ID:	DOAJ00513045X

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ00513045X
003	DE-627
005	20230309191112.0
007	cr uuu---uuuuu
008	230225s2019 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.5267/j.dsl.2018.7.002 \|2 doi
035			\|a (DE-627)DOAJ00513045X
035			\|a (DE-599)DOAJbee42851f7ce4db58aa152f149165cc9
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a QA299.6-433
050		0	\|a HF5691-5716
100	0		\|a Sukanta Nama \|e verfasserin \|4 aut
245	1	2	\|a A novel hybrid backtracking search optimization algorithm for continuous function optimization
264		1	\|c 2019
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed.
650		4	\|a Backtracking Search Optimization Algorithm (BSA)
650		4	\|a Quadratic approximation (QA)
650		4	\|a Hybrid Algorithm
650		4	\|a Unconstrained non-linear function optimization
653		0	\|a Analysis
653		0	\|a Business mathematics. Commercial arithmetic. Including tables, etc.
700	0		\|a Apu Kumar Saha \|e verfasserin \|4 aut
773	0	8	\|i In \|t Decision Science Letters \|d Growing Science, 2013 \|g 8(2019), 2, Seite 163-174 \|w (DE-627)782181988 \|w (DE-600)2764756-0 \|x 19295812 \|7 nnns
773	1	8	\|g volume:8 \|g year:2019 \|g number:2 \|g pages:163-174
856	4	0	\|u https://doi.org/10.5267/j.dsl.2018.7.002 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9 \|z kostenfrei
856	4	0	\|u http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1929-5804 \|y Journal toc \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1929-5812 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
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_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 8 \|j 2019 \|e 2 \|h 163-174

Indexfelder

author_variant	s n sn a k s aks
matchkey_str	article:19295812:2019----::nvlyrdakrcigerhpiiainloihfrotno
hierarchy_sort_str	2019
callnumber-subject-code	QA
publishDate	2019
allfields	10.5267/j.dsl.2018.7.002 doi (DE-627)DOAJ00513045X (DE-599)DOAJbee42851f7ce4db58aa152f149165cc9 DE-627 ger DE-627 rakwb eng QA299.6-433 HF5691-5716 Sukanta Nama verfasserin aut A novel hybrid backtracking search optimization algorithm for continuous function optimization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed. Backtracking Search Optimization Algorithm (BSA) Quadratic approximation (QA) Hybrid Algorithm Unconstrained non-linear function optimization Analysis Business mathematics. Commercial arithmetic. Including tables, etc. Apu Kumar Saha verfasserin aut In Decision Science Letters Growing Science, 2013 8(2019), 2, Seite 163-174 (DE-627)782181988 (DE-600)2764756-0 19295812 nnns volume:8 year:2019 number:2 pages:163-174 https://doi.org/10.5267/j.dsl.2018.7.002 kostenfrei https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9 kostenfrei http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf kostenfrei https://doaj.org/toc/1929-5804 Journal toc kostenfrei https://doaj.org/toc/1929-5812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2019 2 163-174
spelling	10.5267/j.dsl.2018.7.002 doi (DE-627)DOAJ00513045X (DE-599)DOAJbee42851f7ce4db58aa152f149165cc9 DE-627 ger DE-627 rakwb eng QA299.6-433 HF5691-5716 Sukanta Nama verfasserin aut A novel hybrid backtracking search optimization algorithm for continuous function optimization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed. Backtracking Search Optimization Algorithm (BSA) Quadratic approximation (QA) Hybrid Algorithm Unconstrained non-linear function optimization Analysis Business mathematics. Commercial arithmetic. Including tables, etc. Apu Kumar Saha verfasserin aut In Decision Science Letters Growing Science, 2013 8(2019), 2, Seite 163-174 (DE-627)782181988 (DE-600)2764756-0 19295812 nnns volume:8 year:2019 number:2 pages:163-174 https://doi.org/10.5267/j.dsl.2018.7.002 kostenfrei https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9 kostenfrei http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf kostenfrei https://doaj.org/toc/1929-5804 Journal toc kostenfrei https://doaj.org/toc/1929-5812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2019 2 163-174
allfields_unstemmed	10.5267/j.dsl.2018.7.002 doi (DE-627)DOAJ00513045X (DE-599)DOAJbee42851f7ce4db58aa152f149165cc9 DE-627 ger DE-627 rakwb eng QA299.6-433 HF5691-5716 Sukanta Nama verfasserin aut A novel hybrid backtracking search optimization algorithm for continuous function optimization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed. Backtracking Search Optimization Algorithm (BSA) Quadratic approximation (QA) Hybrid Algorithm Unconstrained non-linear function optimization Analysis Business mathematics. Commercial arithmetic. Including tables, etc. Apu Kumar Saha verfasserin aut In Decision Science Letters Growing Science, 2013 8(2019), 2, Seite 163-174 (DE-627)782181988 (DE-600)2764756-0 19295812 nnns volume:8 year:2019 number:2 pages:163-174 https://doi.org/10.5267/j.dsl.2018.7.002 kostenfrei https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9 kostenfrei http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf kostenfrei https://doaj.org/toc/1929-5804 Journal toc kostenfrei https://doaj.org/toc/1929-5812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2019 2 163-174
allfieldsGer	10.5267/j.dsl.2018.7.002 doi (DE-627)DOAJ00513045X (DE-599)DOAJbee42851f7ce4db58aa152f149165cc9 DE-627 ger DE-627 rakwb eng QA299.6-433 HF5691-5716 Sukanta Nama verfasserin aut A novel hybrid backtracking search optimization algorithm for continuous function optimization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed. Backtracking Search Optimization Algorithm (BSA) Quadratic approximation (QA) Hybrid Algorithm Unconstrained non-linear function optimization Analysis Business mathematics. Commercial arithmetic. Including tables, etc. Apu Kumar Saha verfasserin aut In Decision Science Letters Growing Science, 2013 8(2019), 2, Seite 163-174 (DE-627)782181988 (DE-600)2764756-0 19295812 nnns volume:8 year:2019 number:2 pages:163-174 https://doi.org/10.5267/j.dsl.2018.7.002 kostenfrei https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9 kostenfrei http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf kostenfrei https://doaj.org/toc/1929-5804 Journal toc kostenfrei https://doaj.org/toc/1929-5812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2019 2 163-174
allfieldsSound	10.5267/j.dsl.2018.7.002 doi (DE-627)DOAJ00513045X (DE-599)DOAJbee42851f7ce4db58aa152f149165cc9 DE-627 ger DE-627 rakwb eng QA299.6-433 HF5691-5716 Sukanta Nama verfasserin aut A novel hybrid backtracking search optimization algorithm for continuous function optimization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed. Backtracking Search Optimization Algorithm (BSA) Quadratic approximation (QA) Hybrid Algorithm Unconstrained non-linear function optimization Analysis Business mathematics. Commercial arithmetic. Including tables, etc. Apu Kumar Saha verfasserin aut In Decision Science Letters Growing Science, 2013 8(2019), 2, Seite 163-174 (DE-627)782181988 (DE-600)2764756-0 19295812 nnns volume:8 year:2019 number:2 pages:163-174 https://doi.org/10.5267/j.dsl.2018.7.002 kostenfrei https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9 kostenfrei http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf kostenfrei https://doaj.org/toc/1929-5804 Journal toc kostenfrei https://doaj.org/toc/1929-5812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2019 2 163-174
language	English
source	In Decision Science Letters 8(2019), 2, Seite 163-174 volume:8 year:2019 number:2 pages:163-174
sourceStr	In Decision Science Letters 8(2019), 2, Seite 163-174 volume:8 year:2019 number:2 pages:163-174
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Backtracking Search Optimization Algorithm (BSA) Quadratic approximation (QA) Hybrid Algorithm Unconstrained non-linear function optimization Analysis Business mathematics. Commercial arithmetic. Including tables, etc.
isfreeaccess_bool	true
container_title	Decision Science Letters
authorswithroles_txt_mv	Sukanta Nama @@aut@@ Apu Kumar Saha @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	782181988
id	DOAJ00513045X
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">DOAJ00513045X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309191112.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.5267/j.dsl.2018.7.002</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ00513045X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJbee42851f7ce4db58aa152f149165cc9</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA299.6-433</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HF5691-5716</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Sukanta Nama</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A novel hybrid backtracking search optimization algorithm for continuous function optimization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Backtracking Search Optimization Algorithm (BSA)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quadratic approximation (QA)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybrid Algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unconstrained non-linear function optimization</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Business mathematics. Commercial arithmetic. Including tables, etc.</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Apu Kumar Saha</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Decision Science Letters</subfield><subfield code="d">Growing Science, 2013</subfield><subfield code="g">8(2019), 2, Seite 163-174</subfield><subfield code="w">(DE-627)782181988</subfield><subfield code="w">(DE-600)2764756-0</subfield><subfield code="x">19295812</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:163-174</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.5267/j.dsl.2018.7.002</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1929-5804</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1929-5812</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</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_39</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_63</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_95</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_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</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_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</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_4126</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_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_4335</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_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="h">163-174</subfield></datafield></record></collection>
callnumber-first	Q - Science
author	Sukanta Nama
spellingShingle	Sukanta Nama misc QA299.6-433 misc HF5691-5716 misc Backtracking Search Optimization Algorithm (BSA) misc Quadratic approximation (QA) misc Hybrid Algorithm misc Unconstrained non-linear function optimization misc Analysis misc Business mathematics. Commercial arithmetic. Including tables, etc. A novel hybrid backtracking search optimization algorithm for continuous function optimization
authorStr	Sukanta Nama
ppnlink_with_tag_str_mv	@@773@@(DE-627)782181988
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	QA299
illustrated	Not Illustrated
issn	19295812
topic_title	QA299.6-433 HF5691-5716 A novel hybrid backtracking search optimization algorithm for continuous function optimization Backtracking Search Optimization Algorithm (BSA) Quadratic approximation (QA) Hybrid Algorithm Unconstrained non-linear function optimization
topic	misc QA299.6-433 misc HF5691-5716 misc Backtracking Search Optimization Algorithm (BSA) misc Quadratic approximation (QA) misc Hybrid Algorithm misc Unconstrained non-linear function optimization misc Analysis misc Business mathematics. Commercial arithmetic. Including tables, etc.
topic_unstemmed	misc QA299.6-433 misc HF5691-5716 misc Backtracking Search Optimization Algorithm (BSA) misc Quadratic approximation (QA) misc Hybrid Algorithm misc Unconstrained non-linear function optimization misc Analysis misc Business mathematics. Commercial arithmetic. Including tables, etc.
topic_browse	misc QA299.6-433 misc HF5691-5716 misc Backtracking Search Optimization Algorithm (BSA) misc Quadratic approximation (QA) misc Hybrid Algorithm misc Unconstrained non-linear function optimization misc Analysis misc Business mathematics. Commercial arithmetic. Including tables, etc.
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Decision Science Letters
hierarchy_parent_id	782181988
hierarchy_top_title	Decision Science Letters
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)782181988 (DE-600)2764756-0
title	A novel hybrid backtracking search optimization algorithm for continuous function optimization
ctrlnum	(DE-627)DOAJ00513045X (DE-599)DOAJbee42851f7ce4db58aa152f149165cc9
title_full	A novel hybrid backtracking search optimization algorithm for continuous function optimization
author_sort	Sukanta Nama
journal	Decision Science Letters
journalStr	Decision Science Letters
callnumber-first-code	Q
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2019
contenttype_str_mv	txt
container_start_page	163
author_browse	Sukanta Nama Apu Kumar Saha
container_volume	8
class	QA299.6-433 HF5691-5716
format_se	Elektronische Aufsätze
author-letter	Sukanta Nama
doi_str_mv	10.5267/j.dsl.2018.7.002
author2-role	verfasserin
title_sort	novel hybrid backtracking search optimization algorithm for continuous function optimization
callnumber	QA299.6-433
title_auth	A novel hybrid backtracking search optimization algorithm for continuous function optimization
abstract	Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed.
abstractGer	Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed.
abstract_unstemmed	Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	2
title_short	A novel hybrid backtracking search optimization algorithm for continuous function optimization
url	https://doi.org/10.5267/j.dsl.2018.7.002 https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9 http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf https://doaj.org/toc/1929-5804 https://doaj.org/toc/1929-5812
remote_bool	true
author2	Apu Kumar Saha
author2Str	Apu Kumar Saha
ppnlink	782181988
callnumber-subject	QA - Mathematics
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.5267/j.dsl.2018.7.002
callnumber-a	QA299.6-433
up_date	2024-07-04T02:24:59.678Z
_version_	1803613529367904256
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">DOAJ00513045X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309191112.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.5267/j.dsl.2018.7.002</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ00513045X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJbee42851f7ce4db58aa152f149165cc9</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA299.6-433</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HF5691-5716</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Sukanta Nama</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A novel hybrid backtracking search optimization algorithm for continuous function optimization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Backtracking Search Optimization Algorithm (BSA)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quadratic approximation (QA)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybrid Algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unconstrained non-linear function optimization</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Business mathematics. Commercial arithmetic. Including tables, etc.</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Apu Kumar Saha</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Decision Science Letters</subfield><subfield code="d">Growing Science, 2013</subfield><subfield code="g">8(2019), 2, Seite 163-174</subfield><subfield code="w">(DE-627)782181988</subfield><subfield code="w">(DE-600)2764756-0</subfield><subfield code="x">19295812</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:163-174</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.5267/j.dsl.2018.7.002</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/bee42851f7ce4db58aa152f149165cc9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.growingscience.com/dsl/Vol8/dsl_2018_22.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1929-5804</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1929-5812</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</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_39</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_63</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_95</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_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</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_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</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_4126</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_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_4335</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_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="h">163-174</subfield></datafield></record></collection>
score	7.3993654

Nicht das Richtige dabei?

Schreiben Sie uns!

A novel hybrid backtracking search optimization algorithm for continuous function optimization

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?