Structural optimization for post-buckling behavior using particle swarms
Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bochenek, B. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2006 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2006 |
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| Übergeordnetes Werk: |
Enthalten in: Structural and multidisciplinary optimization - Berlin : Springer, 1989, 32(2006), 6 vom: 02. Sept., Seite 521-531 |
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| Übergeordnetes Werk: |
volume:32 ; year:2006 ; number:6 ; day:02 ; month:09 ; pages:521-531 |
| Links: |
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| DOI / URN: |
10.1007/s00158-006-0044-8 |
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SPR001308637 |
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| 245 | 1 | 0 | |a Structural optimization for post-buckling behavior using particle swarms |
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| 520 | |a Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. | ||
| 650 | 4 | |a Particle swarm optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Instability |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Post-buckling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Foryś, P. |4 aut | |
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10.1007/s00158-006-0044-8 doi (DE-627)SPR001308637 (SPR)s00158-006-0044-8-e DE-627 ger DE-627 rakwb eng Bochenek, B. verfasserin aut Structural optimization for post-buckling behavior using particle swarms 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2006 Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. Particle swarm optimization (dpeaa)DE-He213 Instability (dpeaa)DE-He213 Post-buckling (dpeaa)DE-He213 Foryś, P. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 32(2006), 6 vom: 02. Sept., Seite 521-531 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:32 year:2006 number:6 day:02 month:09 pages:521-531 https://dx.doi.org/10.1007/s00158-006-0044-8 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2006 6 02 09 521-531 |
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10.1007/s00158-006-0044-8 doi (DE-627)SPR001308637 (SPR)s00158-006-0044-8-e DE-627 ger DE-627 rakwb eng Bochenek, B. verfasserin aut Structural optimization for post-buckling behavior using particle swarms 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2006 Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. Particle swarm optimization (dpeaa)DE-He213 Instability (dpeaa)DE-He213 Post-buckling (dpeaa)DE-He213 Foryś, P. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 32(2006), 6 vom: 02. Sept., Seite 521-531 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:32 year:2006 number:6 day:02 month:09 pages:521-531 https://dx.doi.org/10.1007/s00158-006-0044-8 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2006 6 02 09 521-531 |
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10.1007/s00158-006-0044-8 doi (DE-627)SPR001308637 (SPR)s00158-006-0044-8-e DE-627 ger DE-627 rakwb eng Bochenek, B. verfasserin aut Structural optimization for post-buckling behavior using particle swarms 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2006 Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. Particle swarm optimization (dpeaa)DE-He213 Instability (dpeaa)DE-He213 Post-buckling (dpeaa)DE-He213 Foryś, P. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 32(2006), 6 vom: 02. Sept., Seite 521-531 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:32 year:2006 number:6 day:02 month:09 pages:521-531 https://dx.doi.org/10.1007/s00158-006-0044-8 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2006 6 02 09 521-531 |
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10.1007/s00158-006-0044-8 doi (DE-627)SPR001308637 (SPR)s00158-006-0044-8-e DE-627 ger DE-627 rakwb eng Bochenek, B. verfasserin aut Structural optimization for post-buckling behavior using particle swarms 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2006 Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. Particle swarm optimization (dpeaa)DE-He213 Instability (dpeaa)DE-He213 Post-buckling (dpeaa)DE-He213 Foryś, P. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 32(2006), 6 vom: 02. Sept., Seite 521-531 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:32 year:2006 number:6 day:02 month:09 pages:521-531 https://dx.doi.org/10.1007/s00158-006-0044-8 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2006 6 02 09 521-531 |
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10.1007/s00158-006-0044-8 doi (DE-627)SPR001308637 (SPR)s00158-006-0044-8-e DE-627 ger DE-627 rakwb eng Bochenek, B. verfasserin aut Structural optimization for post-buckling behavior using particle swarms 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2006 Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. Particle swarm optimization (dpeaa)DE-He213 Instability (dpeaa)DE-He213 Post-buckling (dpeaa)DE-He213 Foryś, P. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 32(2006), 6 vom: 02. Sept., Seite 521-531 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:32 year:2006 number:6 day:02 month:09 pages:521-531 https://dx.doi.org/10.1007/s00158-006-0044-8 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2006 6 02 09 521-531 |
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Bochenek, B. |
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Bochenek, B. misc Particle swarm optimization misc Instability misc Post-buckling Structural optimization for post-buckling behavior using particle swarms |
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Structural optimization for post-buckling behavior using particle swarms Particle swarm optimization (dpeaa)DE-He213 Instability (dpeaa)DE-He213 Post-buckling (dpeaa)DE-He213 |
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structural optimization for post-buckling behavior using particle swarms |
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Structural optimization for post-buckling behavior using particle swarms |
| abstract |
Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. © Springer-Verlag 2006 |
| abstractGer |
Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. © Springer-Verlag 2006 |
| abstract_unstemmed |
Abstract The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve. © Springer-Verlag 2006 |
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Structural optimization for post-buckling behavior using particle swarms |
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https://dx.doi.org/10.1007/s00158-006-0044-8 |
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Foryś, P. |
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10.1007/s00158-006-0044-8 |
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2024-07-03T21:41:32.409Z |
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| score |
7.400338 |