A social learning particle swarm optimization algorithm for scalable optimization
Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cheng, Ran [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Rechteinformationen: |
Nutzungsrecht: © COPYRIGHT 2015 Elsevier B.V. |
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| Schlagwörter: |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: Information sciences - New York, NY : Elsevier Science Inc., 1968, 291(2014), Seite 43 |
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| Übergeordnetes Werk: |
volume:291 ; year:2014 ; pages:43 |
| Links: |
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| DOI / URN: |
10.1016/j.ins.2014.08.039 |
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| 520 | |a Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well. | ||
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10.1016/j.ins.2014.08.039 doi PQ20160617 (DE-627)OLC1964396107 (DE-599)GBVOLC1964396107 (PRQ)c1013-8ef6fbe5419654614537c79b0d7c0eb095c93453cd89779e9e48f0df2c6792d10 (KEY)0030488320140000291000000043sociallearningparticleswarmoptimizationalgorithmfo DE-627 ger DE-627 rakwb eng 400 070 004 DNB LING fid SA 5420 AVZ rvk Cheng, Ran verfasserin aut A social learning particle swarm optimization algorithm for scalable optimization 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well. Nutzungsrecht: © COPYRIGHT 2015 Elsevier B.V. Algorithms Social aspects Mathematical optimization Jin, Yaochu oth Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 291(2014), Seite 43 (DE-627)129548200 (DE-600)218760-7 (DE-576)01499996X 0020-0255 nnns volume:291 year:2014 pages:43 http://dx.doi.org/10.1016/j.ins.2014.08.039 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2009 GBV_ILN_4012 SA 5420 AR 291 2014 43 |
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10.1016/j.ins.2014.08.039 doi PQ20160617 (DE-627)OLC1964396107 (DE-599)GBVOLC1964396107 (PRQ)c1013-8ef6fbe5419654614537c79b0d7c0eb095c93453cd89779e9e48f0df2c6792d10 (KEY)0030488320140000291000000043sociallearningparticleswarmoptimizationalgorithmfo DE-627 ger DE-627 rakwb eng 400 070 004 DNB LING fid SA 5420 AVZ rvk Cheng, Ran verfasserin aut A social learning particle swarm optimization algorithm for scalable optimization 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well. Nutzungsrecht: © COPYRIGHT 2015 Elsevier B.V. Algorithms Social aspects Mathematical optimization Jin, Yaochu oth Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 291(2014), Seite 43 (DE-627)129548200 (DE-600)218760-7 (DE-576)01499996X 0020-0255 nnns volume:291 year:2014 pages:43 http://dx.doi.org/10.1016/j.ins.2014.08.039 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2009 GBV_ILN_4012 SA 5420 AR 291 2014 43 |
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10.1016/j.ins.2014.08.039 doi PQ20160617 (DE-627)OLC1964396107 (DE-599)GBVOLC1964396107 (PRQ)c1013-8ef6fbe5419654614537c79b0d7c0eb095c93453cd89779e9e48f0df2c6792d10 (KEY)0030488320140000291000000043sociallearningparticleswarmoptimizationalgorithmfo DE-627 ger DE-627 rakwb eng 400 070 004 DNB LING fid SA 5420 AVZ rvk Cheng, Ran verfasserin aut A social learning particle swarm optimization algorithm for scalable optimization 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well. Nutzungsrecht: © COPYRIGHT 2015 Elsevier B.V. Algorithms Social aspects Mathematical optimization Jin, Yaochu oth Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 291(2014), Seite 43 (DE-627)129548200 (DE-600)218760-7 (DE-576)01499996X 0020-0255 nnns volume:291 year:2014 pages:43 http://dx.doi.org/10.1016/j.ins.2014.08.039 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2009 GBV_ILN_4012 SA 5420 AR 291 2014 43 |
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10.1016/j.ins.2014.08.039 doi PQ20160617 (DE-627)OLC1964396107 (DE-599)GBVOLC1964396107 (PRQ)c1013-8ef6fbe5419654614537c79b0d7c0eb095c93453cd89779e9e48f0df2c6792d10 (KEY)0030488320140000291000000043sociallearningparticleswarmoptimizationalgorithmfo DE-627 ger DE-627 rakwb eng 400 070 004 DNB LING fid SA 5420 AVZ rvk Cheng, Ran verfasserin aut A social learning particle swarm optimization algorithm for scalable optimization 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well. Nutzungsrecht: © COPYRIGHT 2015 Elsevier B.V. Algorithms Social aspects Mathematical optimization Jin, Yaochu oth Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 291(2014), Seite 43 (DE-627)129548200 (DE-600)218760-7 (DE-576)01499996X 0020-0255 nnns volume:291 year:2014 pages:43 http://dx.doi.org/10.1016/j.ins.2014.08.039 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2009 GBV_ILN_4012 SA 5420 AR 291 2014 43 |
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10.1016/j.ins.2014.08.039 |
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| title_sort |
social learning particle swarm optimization algorithm for scalable optimization |
| title_auth |
A social learning particle swarm optimization algorithm for scalable optimization |
| abstract |
Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well. |
| abstractGer |
Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well. |
| abstract_unstemmed |
Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well. |
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| title_short |
A social learning particle swarm optimization algorithm for scalable optimization |
| url |
http://dx.doi.org/10.1016/j.ins.2014.08.039 |
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