Otimização estrutural de treliças considerando restrições de frequências naturais de vibração
DOI:
https://doi.org/10.34019/2179-3700.2018.v18.29874Palavras-chave:
Otimização estrutural, Particle swarm optimization, Frequências naturais de vibraçãoResumo
Este artigo apresenta um Algoritmo Evolucionário (AE) baseado no comportamento de enxame de partículas (Particle Swarm Optimization - PSO) adaptado para a obtenção de soluções de problemas de otimização estrutural com restrições. O PSO é um algoritmo de fácil implementação e competitivo perante os demais algoritmos populacionais inspirados na natureza. Neste artigo, são analisados problemas de otimização estrutural de treliças submetidas a restrições de frequências naturais de vibração. Para o tratamento destas restrições, incorpora-se ao PSO uma técnica de penalização adaptativa (Adaptive Penalty Method - APM), que tem demonstrado robustez e eficiência quando aplicada no tratamento de problemas de otimização com restrições. O algoritmo proposto é validado através de experimentos computacionais em problemas de otimização estrutural amplamente discutidos na literatura.
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Referências
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CARVALHO, J. P. G.; LEMONGE, A. C.; CARVALHO, E. C.; HALLAK, P. H.; BERNARDINO, H.S. Truss optimization with multiple frequency constraints and automatic member grouping. Structural and Multidisciplinary Optimization, v. 57, n. 2, p. 547-577, 2018.
CARVALHO, J. P. G.; LEMONGE, A. C. C.; HALLAK, P. H.; VARGAS, D. E. C. A Differential Evolution to Find the Best Material Groupings in Truss Optimization. International Conference on Engineering Optimization. Springer, Cham, 2019.
COLORNI, A.; DORIGO, M.; MANIEZZO, V. Distributed optimization by ant colonies. Proceedings of the first European conference on artificial life, v. 142, p. 134-142, Paris, France, 1992.
EBERHART, R; KENNEDY, J. A new optimizer using particle swarm theory. In: Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on, p. 39-43, 1995.
GOMES, H. M. Truss optimization with dynamic constraints using a particle swarm algorithm. In: Expert Systems with Applications, Elsevier, vol. 38, n.1, pp. 957-968, 2011.
HOLLAND, J. H. Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, v. 2, n. 2, p. 88-105, 1973.
KARABOGA, D. An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes.
KAVEH, A.; JAVADI, S. M. Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm. In:ActaMechanica, Springer, v. 225, n. 6, p. 1595-1605, 2014.
KAVEH, A.; ZOLGHADR, A. Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability. Computers & Structures, Elsevier, v. 102, p. 14-27, 2012.
KAVEH, A.; ZOLGHADR, A. Comparison of nine meta-heuristic algorithms for optimal design of truss structures with frequency constraints. Advances in Engineering Software, Elsevier, v. 76, p. 9-30, 2014.
MIGUEL, L. F. F. Shape and size optimization of truss structures considering dynamic constraints through modern metaheuristic algorithms. Expert Systems with Applications, Elsevier, v. 39, n. 10, p. 9458–9467, 2012.
PHOLDEE, N.; BUREERAT, S. Comparative performance of meta-heuristic algorithms for mass minimisation of trusses with dynamic constraints. In: Advances in Engineering Software, Elsevier, v. 75, p. 1-13, 2014.
SMITH, A. E.; COIT, D.W. Penalty functions. In Handbook of Evolutionary Computation.A Joint Publication of Oxford University Press and Institute of Physics, v. 97, n. 1, p. C5, 1995.
VU, T. V. Weight Minimization of Trusses with Natural Frequency Constraints. In: 11TH WORLD CONGRESS ON STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2015.
ZUO, W.; XU, T.; ZHANG, H.; XU, T. Fast structural optimization with frequency constraints by genetic algorithm using adaptive eigenvalue reanalysis methods. In: Structural and Multidisciplinary Optimization, Springer, v. 43, n. 6, p. 799-810, 2011.
WEI, L.; TANG, T.; XIE, X.; SHEN, W. Truss optimization on shape and sizing with frequency constraints based on parallel genetic algorithm. Structural and Multidisciplinary Optimization, Springer, v. 43, n. 5, p. 665-682, 2010.
CARVALHO, J. P. G.; CARVALHO, E. C. R.; BERNARDINO, H. S.; HALLAK, P. H.; LEMONGE, A. C. C. Solução de problemas de otimização estrutural com restrições de frequências de vibração via PSO. In: XXXVI IBERO-LATIN AMERICAN CONGRESS ON COMPUTATIONAL ENGINEERING, p. 1-15, Rio de Janeiro, Brasil, 2015.
CARVALHO, J. P. G.; LEMONGE, A. C.; CARVALHO, E. C.; HALLAK, P. H.; BERNARDINO, H.S. Truss optimization with multiple frequency constraints and automatic member grouping. Structural and Multidisciplinary Optimization, v. 57, n. 2, p. 547-577, 2018.
CARVALHO, J. P. G.; LEMONGE, A. C. C.; HALLAK, P. H.; VARGAS, D. E. C. A Differential Evolution to Find the Best Material Groupings in Truss Optimization. International Conference on Engineering Optimization. Springer, Cham, 2019.
COLORNI, A.; DORIGO, M.; MANIEZZO, V. Distributed optimization by ant colonies. Proceedings of the first European conference on artificial life, v. 142, p. 134-142, Paris, France, 1992.
EBERHART, R; KENNEDY, J. A new optimizer using particle swarm theory. In: Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on, p. 39-43, 1995.
GOMES, H. M. Truss optimization with dynamic constraints using a particle swarm algorithm. In: Expert Systems with Applications, Elsevier, vol. 38, n.1, pp. 957-968, 2011.
HOLLAND, J. H. Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, v. 2, n. 2, p. 88-105, 1973.
KARABOGA, D. An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes.
KAVEH, A.; JAVADI, S. M. Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm. In:ActaMechanica, Springer, v. 225, n. 6, p. 1595-1605, 2014.
KAVEH, A.; ZOLGHADR, A. Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability. Computers & Structures, Elsevier, v. 102, p. 14-27, 2012.
KAVEH, A.; ZOLGHADR, A. Comparison of nine meta-heuristic algorithms for optimal design of truss structures with frequency constraints. Advances in Engineering Software, Elsevier, v. 76, p. 9-30, 2014.
MIGUEL, L. F. F. Shape and size optimization of truss structures considering dynamic constraints through modern metaheuristic algorithms. Expert Systems with Applications, Elsevier, v. 39, n. 10, p. 9458–9467, 2012.
PHOLDEE, N.; BUREERAT, S. Comparative performance of meta-heuristic algorithms for mass minimisation of trusses with dynamic constraints. In: Advances in Engineering Software, Elsevier, v. 75, p. 1-13, 2014.
SMITH, A. E.; COIT, D.W. Penalty functions. In Handbook of Evolutionary Computation.A Joint Publication of Oxford University Press and Institute of Physics, v. 97, n. 1, p. C5, 1995.
VU, T. V. Weight Minimization of Trusses with Natural Frequency Constraints. In: 11TH WORLD CONGRESS ON STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2015.
ZUO, W.; XU, T.; ZHANG, H.; XU, T. Fast structural optimization with frequency constraints by genetic algorithm using adaptive eigenvalue reanalysis methods. In: Structural and Multidisciplinary Optimization, Springer, v. 43, n. 6, p. 799-810, 2011.
WEI, L.; TANG, T.; XIE, X.; SHEN, W. Truss optimization on shape and sizing with frequency constraints based on parallel genetic algorithm. Structural and Multidisciplinary Optimization, Springer, v. 43, n. 5, p. 665-682, 2010.
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Publicado
2020-03-05
Como Citar
Lemonge, A. C. de C., Hallak, P. H., & Carvalho, J. P. G. (2020). Otimização estrutural de treliças considerando restrições de frequências naturais de vibração. Principia: Caminhos Da Iniciação Científica, 18(2), 14. https://doi.org/10.34019/2179-3700.2018.v18.29874
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Artigos originais - Engenharias e Ciência da Computação
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