Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis
Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Yong [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 18(2013), 7 vom: 23. Okt., Seite 1337-1352 |
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| Übergeordnetes Werk: |
volume:18 ; year:2013 ; number:7 ; day:23 ; month:10 ; pages:1337-1352 |
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| DOI / URN: |
10.1007/s00500-013-1147-y |
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OLC2034875699 |
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| 520 | |a Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. | ||
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| 700 | 1 | |a Sun, Xiao-yan |4 aut | |
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10.1007/s00500-013-1147-y doi (DE-627)OLC2034875699 (DE-He213)s00500-013-1147-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhang, Yong verfasserin aut Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. Bare-bones particle swarm optimization Convergence analysis Adaptive disturbance Mutation Gong, Dun-wei aut Sun, Xiao-yan aut Geng, Na aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 18(2013), 7 vom: 23. Okt., Seite 1337-1352 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:18 year:2013 number:7 day:23 month:10 pages:1337-1352 https://doi.org/10.1007/s00500-013-1147-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 18 2013 7 23 10 1337-1352 |
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10.1007/s00500-013-1147-y doi (DE-627)OLC2034875699 (DE-He213)s00500-013-1147-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhang, Yong verfasserin aut Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. Bare-bones particle swarm optimization Convergence analysis Adaptive disturbance Mutation Gong, Dun-wei aut Sun, Xiao-yan aut Geng, Na aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 18(2013), 7 vom: 23. Okt., Seite 1337-1352 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:18 year:2013 number:7 day:23 month:10 pages:1337-1352 https://doi.org/10.1007/s00500-013-1147-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 18 2013 7 23 10 1337-1352 |
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10.1007/s00500-013-1147-y doi (DE-627)OLC2034875699 (DE-He213)s00500-013-1147-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhang, Yong verfasserin aut Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. Bare-bones particle swarm optimization Convergence analysis Adaptive disturbance Mutation Gong, Dun-wei aut Sun, Xiao-yan aut Geng, Na aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 18(2013), 7 vom: 23. Okt., Seite 1337-1352 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:18 year:2013 number:7 day:23 month:10 pages:1337-1352 https://doi.org/10.1007/s00500-013-1147-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 18 2013 7 23 10 1337-1352 |
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10.1007/s00500-013-1147-y doi (DE-627)OLC2034875699 (DE-He213)s00500-013-1147-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhang, Yong verfasserin aut Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. Bare-bones particle swarm optimization Convergence analysis Adaptive disturbance Mutation Gong, Dun-wei aut Sun, Xiao-yan aut Geng, Na aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 18(2013), 7 vom: 23. Okt., Seite 1337-1352 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:18 year:2013 number:7 day:23 month:10 pages:1337-1352 https://doi.org/10.1007/s00500-013-1147-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 18 2013 7 23 10 1337-1352 |
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Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. © Springer-Verlag Berlin Heidelberg 2013 |
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Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. © Springer-Verlag Berlin Heidelberg 2013 |
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Abstract Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. © Springer-Verlag Berlin Heidelberg 2013 |
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Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis |
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Gong, Dun-wei Sun, Xiao-yan Geng, Na |
| author2Str |
Gong, Dun-wei Sun, Xiao-yan Geng, Na |
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231970536 |
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n |
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false |
| doi_str |
10.1007/s00500-013-1147-y |
| up_date |
2024-07-03T22:48:57.882Z |
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7.397897 |