A hybrid AI-based particle bee algorithm for facility layout optimization
Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult comb...
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Gespeichert in:
| Autor*in: |
Cheng, Min-Yuan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Limited 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Engineering with computers - Springer-Verlag, 1985, 28(2011), 1 vom: 13. Apr., Seite 57-69 |
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| Übergeordnetes Werk: |
volume:28 ; year:2011 ; number:1 ; day:13 ; month:04 ; pages:57-69 |
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| DOI / URN: |
10.1007/s00366-011-0216-z |
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| 520 | |a Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. | ||
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10.1007/s00366-011-0216-z doi (DE-627)OLC2064360115 (DE-He213)s00366-011-0216-z-p DE-627 ger DE-627 rakwb eng 004 600 VZ Cheng, Min-Yuan verfasserin aut A hybrid AI-based particle bee algorithm for facility layout optimization 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2011 Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. Facility layout Swarm intelligence Bee algorithm Particle swarm optimization Particle bee algorithm Lien, Li-Chuan aut Enthalten in Engineering with computers Springer-Verlag, 1985 28(2011), 1 vom: 13. Apr., Seite 57-69 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:28 year:2011 number:1 day:13 month:04 pages:57-69 https://doi.org/10.1007/s00366-011-0216-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 28 2011 1 13 04 57-69 |
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10.1007/s00366-011-0216-z doi (DE-627)OLC2064360115 (DE-He213)s00366-011-0216-z-p DE-627 ger DE-627 rakwb eng 004 600 VZ Cheng, Min-Yuan verfasserin aut A hybrid AI-based particle bee algorithm for facility layout optimization 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2011 Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. Facility layout Swarm intelligence Bee algorithm Particle swarm optimization Particle bee algorithm Lien, Li-Chuan aut Enthalten in Engineering with computers Springer-Verlag, 1985 28(2011), 1 vom: 13. Apr., Seite 57-69 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:28 year:2011 number:1 day:13 month:04 pages:57-69 https://doi.org/10.1007/s00366-011-0216-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 28 2011 1 13 04 57-69 |
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10.1007/s00366-011-0216-z doi (DE-627)OLC2064360115 (DE-He213)s00366-011-0216-z-p DE-627 ger DE-627 rakwb eng 004 600 VZ Cheng, Min-Yuan verfasserin aut A hybrid AI-based particle bee algorithm for facility layout optimization 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2011 Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. Facility layout Swarm intelligence Bee algorithm Particle swarm optimization Particle bee algorithm Lien, Li-Chuan aut Enthalten in Engineering with computers Springer-Verlag, 1985 28(2011), 1 vom: 13. Apr., Seite 57-69 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:28 year:2011 number:1 day:13 month:04 pages:57-69 https://doi.org/10.1007/s00366-011-0216-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 28 2011 1 13 04 57-69 |
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10.1007/s00366-011-0216-z doi (DE-627)OLC2064360115 (DE-He213)s00366-011-0216-z-p DE-627 ger DE-627 rakwb eng 004 600 VZ Cheng, Min-Yuan verfasserin aut A hybrid AI-based particle bee algorithm for facility layout optimization 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2011 Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. Facility layout Swarm intelligence Bee algorithm Particle swarm optimization Particle bee algorithm Lien, Li-Chuan aut Enthalten in Engineering with computers Springer-Verlag, 1985 28(2011), 1 vom: 13. Apr., Seite 57-69 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:28 year:2011 number:1 day:13 month:04 pages:57-69 https://doi.org/10.1007/s00366-011-0216-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 28 2011 1 13 04 57-69 |
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10.1007/s00366-011-0216-z doi (DE-627)OLC2064360115 (DE-He213)s00366-011-0216-z-p DE-627 ger DE-627 rakwb eng 004 600 VZ Cheng, Min-Yuan verfasserin aut A hybrid AI-based particle bee algorithm for facility layout optimization 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2011 Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. Facility layout Swarm intelligence Bee algorithm Particle swarm optimization Particle bee algorithm Lien, Li-Chuan aut Enthalten in Engineering with computers Springer-Verlag, 1985 28(2011), 1 vom: 13. Apr., Seite 57-69 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:28 year:2011 number:1 day:13 month:04 pages:57-69 https://doi.org/10.1007/s00366-011-0216-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 28 2011 1 13 04 57-69 |
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A hybrid AI-based particle bee algorithm for facility layout optimization |
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Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. © Springer-Verlag London Limited 2011 |
| abstractGer |
Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. © Springer-Verlag London Limited 2011 |
| abstract_unstemmed |
Abstract Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality. © Springer-Verlag London Limited 2011 |
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| title_short |
A hybrid AI-based particle bee algorithm for facility layout optimization |
| url |
https://doi.org/10.1007/s00366-011-0216-z |
| remote_bool |
false |
| author2 |
Lien, Li-Chuan |
| author2Str |
Lien, Li-Chuan |
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| doi_str |
10.1007/s00366-011-0216-z |
| up_date |
2024-07-03T22:48:06.828Z |
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7.3985004 |