A Novel Evolutionary Algorithm Inspired by Beans Dispersal

Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating...
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Autor*in:	Zhang, Xiaoming [verfasserIn] Sun, Bingyu Mei, Tao Wang, Rujing

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	evolutionary algorithm swarm intelligence particle swarm optimization bean optimization algorithm

Anmerkung:	© the authors 2013

Übergeordnetes Werk:	Enthalten in: International journal of computational intelligence systems - Paris : Atlantis Press, 2008, 6(2013), 1 vom: Jan., Seite 79-86
Übergeordnetes Werk:	volume:6 ; year:2013 ; number:1 ; month:01 ; pages:79-86

Links:	Volltext

DOI / URN:	10.1080/18756891.2013.756225

Katalog-ID:	SPR054190363

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520			\|a Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications.
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allfields	10.1080/18756891.2013.756225 doi (DE-627)SPR054190363 (SPR)18756891.2013.756225-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoming verfasserin aut A Novel Evolutionary Algorithm Inspired by Beans Dispersal 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2013 Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. evolutionary algorithm (dpeaa)DE-He213 swarm intelligence (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 bean optimization algorithm (dpeaa)DE-He213 Sun, Bingyu aut Mei, Tao aut Wang, Rujing aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 6(2013), 1 vom: Jan., Seite 79-86 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:6 year:2013 number:1 month:01 pages:79-86 https://dx.doi.org/10.1080/18756891.2013.756225 kostenfrei 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_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_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 6 2013 1 01 79-86
spelling	10.1080/18756891.2013.756225 doi (DE-627)SPR054190363 (SPR)18756891.2013.756225-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoming verfasserin aut A Novel Evolutionary Algorithm Inspired by Beans Dispersal 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2013 Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. evolutionary algorithm (dpeaa)DE-He213 swarm intelligence (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 bean optimization algorithm (dpeaa)DE-He213 Sun, Bingyu aut Mei, Tao aut Wang, Rujing aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 6(2013), 1 vom: Jan., Seite 79-86 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:6 year:2013 number:1 month:01 pages:79-86 https://dx.doi.org/10.1080/18756891.2013.756225 kostenfrei 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_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_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 6 2013 1 01 79-86
allfields_unstemmed	10.1080/18756891.2013.756225 doi (DE-627)SPR054190363 (SPR)18756891.2013.756225-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoming verfasserin aut A Novel Evolutionary Algorithm Inspired by Beans Dispersal 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2013 Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. evolutionary algorithm (dpeaa)DE-He213 swarm intelligence (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 bean optimization algorithm (dpeaa)DE-He213 Sun, Bingyu aut Mei, Tao aut Wang, Rujing aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 6(2013), 1 vom: Jan., Seite 79-86 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:6 year:2013 number:1 month:01 pages:79-86 https://dx.doi.org/10.1080/18756891.2013.756225 kostenfrei 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_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_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 6 2013 1 01 79-86
allfieldsGer	10.1080/18756891.2013.756225 doi (DE-627)SPR054190363 (SPR)18756891.2013.756225-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoming verfasserin aut A Novel Evolutionary Algorithm Inspired by Beans Dispersal 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2013 Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. evolutionary algorithm (dpeaa)DE-He213 swarm intelligence (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 bean optimization algorithm (dpeaa)DE-He213 Sun, Bingyu aut Mei, Tao aut Wang, Rujing aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 6(2013), 1 vom: Jan., Seite 79-86 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:6 year:2013 number:1 month:01 pages:79-86 https://dx.doi.org/10.1080/18756891.2013.756225 kostenfrei 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_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_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 6 2013 1 01 79-86
allfieldsSound	10.1080/18756891.2013.756225 doi (DE-627)SPR054190363 (SPR)18756891.2013.756225-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoming verfasserin aut A Novel Evolutionary Algorithm Inspired by Beans Dispersal 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2013 Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. evolutionary algorithm (dpeaa)DE-He213 swarm intelligence (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 bean optimization algorithm (dpeaa)DE-He213 Sun, Bingyu aut Mei, Tao aut Wang, Rujing aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 6(2013), 1 vom: Jan., Seite 79-86 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:6 year:2013 number:1 month:01 pages:79-86 https://dx.doi.org/10.1080/18756891.2013.756225 kostenfrei 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_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_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 6 2013 1 01 79-86
language	English
source	Enthalten in International journal of computational intelligence systems 6(2013), 1 vom: Jan., Seite 79-86 volume:6 year:2013 number:1 month:01 pages:79-86
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authorswithroles_txt_mv	Zhang, Xiaoming @@aut@@ Sun, Bingyu @@aut@@ Mei, Tao @@aut@@ Wang, Rujing @@aut@@
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author	Zhang, Xiaoming
spellingShingle	Zhang, Xiaoming misc evolutionary algorithm misc swarm intelligence misc particle swarm optimization misc bean optimization algorithm A Novel Evolutionary Algorithm Inspired by Beans Dispersal
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topic_title	A Novel Evolutionary Algorithm Inspired by Beans Dispersal evolutionary algorithm (dpeaa)DE-He213 swarm intelligence (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 bean optimization algorithm (dpeaa)DE-He213
topic	misc evolutionary algorithm misc swarm intelligence misc particle swarm optimization misc bean optimization algorithm
topic_unstemmed	misc evolutionary algorithm misc swarm intelligence misc particle swarm optimization misc bean optimization algorithm
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title	A Novel Evolutionary Algorithm Inspired by Beans Dispersal
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title_full	A Novel Evolutionary Algorithm Inspired by Beans Dispersal
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author_browse	Zhang, Xiaoming Sun, Bingyu Mei, Tao Wang, Rujing
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title_sort	novel evolutionary algorithm inspired by beans dispersal
title_auth	A Novel Evolutionary Algorithm Inspired by Beans Dispersal
abstract	Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. © the authors 2013
abstractGer	Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. © the authors 2013
abstract_unstemmed	Abstract Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets’ research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. © the authors 2013
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title_short	A Novel Evolutionary Algorithm Inspired by Beans Dispersal
url	https://dx.doi.org/10.1080/18756891.2013.756225
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author2	Sun, Bingyu Mei, Tao Wang, Rujing
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