Diseño óptimo de armaduras empleando optimización con ondas del agua

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2017-07-01
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Millán Páramo, Carlos
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Citar con el siguiente link: http://hdl.handle.net/11323/2466
https://revistascientificas.cuc.edu.co/ingecuc/article/view/1628

Abstract

Introducción: En los últimos años, la importancia de los aspectos económicos en el campo de las estructuras ha motivado a muchos investigadores a emplear nuevos métodos para minimizar el peso de las estructuras. El objetivo principal de la optimización estructural (diseño óptimo) es minimizar el peso de las estructuras al tiempo que se satisfacen todos los requerimientos impuestos por los códigos de diseño.Objetivo: En este estudio, el algoritmo Optimización con Ondas del Agua (Water Wave Optimization - WWO), es implementado para resolver el problema de optimización estructural de armaduras en 2D y 3D.Metodología: El estudio está compuesto por tres fases principales: 1) formulación del problema de optimización estructural; 2) estudio de los fundamentos y parámetros que controlan al algoritmo WWO y 3) evaluar el desempeño del WWO en problemas optimización de armaduras reportadas en la literatura especializada.Resultados: Los valores de peso, peso promedio, desviación estándar y número total de análisis ejecutados para converger al diseño óptimo conseguidos con WWO indican que el algoritmo es una buena herramienta para minimizar el peso de armaduras sujetas a restricciones de esfuerzo y desplazamientos.Conclusiones: Se observó que el algoritmo WWO es eficaz, eficiente y robusto, para resolver diversos tipos de problemas, con diferentes números de elementos. Además, WWO requiere menor número de análisis para converger al diseño óptimo en comparación con otros algoritmos
 
Introduction−In recent years, the importance of economic considerations in the field of structures has motivated many researchers to employ new meth-ods for minimizing the weight of the structures. The main goal of the struc-tural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes.Objective−In this study, the Water Wave Optimization (WWO) algorithm is implemented to solve the problem of structural optimization of 2D and 3D truss structures.Methodology−The study is composed of three main phases: 1) formulation of the structural optimization problem; 2) study of the fundamentals and param-eters that control the WWO algorithm and 3) evaluate the WWO performance in optimization problems of truss structures reported in the specialized lit-erature.Results− The values of weight, average weight, standard deviation and the total number of analyses executed to converge to the optimum design obtained with WWO indicate that the algorithm is a good tool to minimize the weight of truss structures subject to stress and displacements constrained. Conclusions− It was observed that the WWO algorithm is effectively, effciently and robust to solve different types of problems, with different num-bers of elements. Furthermore, WWO requires a lower number of analyses to converge to the optimum design compared to other algorithms
 
Referencias:
[1] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by Simulated Annealing," Science 80, vol. 220, no. 4598, pp. 671–680, 1983, DOI: https://doi.org/10.1126/science.220.4598.671 [2] Z. W. Geem, J. H. Kim, and G. V. Loganathan, "A New Heuristic Optimization Algorithm: Harmony Search," Simulation, vol. 76, no. 2, pp. 60–68, 2001, DOI: https://doi.org/10.1177/003754970107600201 [3] J. H. Holland, "Adaptation in Natural and Artificial Systems," Ann Arbor MI Univ. Michigan Press, vol. Ann Arbor, p. 183, 1975, DOI: https://doi.org/10.1137/1018105 [4] X.-S. Yang and S. Deb, "Cuckoo search: recent advances and applications," Neural Comput. Appl., vol. 24, no. 1, pp. 169–174, 2014, DOI: https://doi.org/10.1007/s00521-013-1367-1 [5] J. Kennedy and R. Eberhart, "Particle swarm optimization," 1995 IEEE Int. Conf. Neural Networks (ICNN 95), vol. 4, pp. 1942–1948, 1995, DOI: https://doi.org/10.1109/ICNN.1995.488968 [6] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of cooperating agents," IEEE Trans. Syst. Man Cybern. Part B, vol. 26, no. 1, pp. 29– 41, 1996, DOI: https://doi.org/10.1109/3477.484436 [7] F. Erbatur, O. Hasançebi, İ. Tütüncü, and H. Kılıç, "Optimal design of planar and space structures with genetic algorithms," Comput. Struct., vol. 75, no. 2, pp. 209–224, 2000, DOI: https://doi.org/10.1016/S0045-7949(99)00084-X [8] J. F. Schutte and A. A. Groenwold, "Sizing design of truss structures using particle swarms," Struct. Multidiscip. Optim., vol. 25, no. 4, pp. 261–269, oct. 2003, DOI: https://doi.org/10.1007/s00158-003-0316-5 [9] C. V. Camp and B. J. Bichon, "Design of Space Trusses Using Ant Colony Optimization," J. Struct. Eng., vol. 130, no. 5, pp. 741–751, 2004, DOI: https://doi.org/10.1061/(ASCE)0733-9445(2004)130:5(741) [10] K. S. Lee and Z. W. Geem, "A new structural optimization method based on the harmony search algorithm," Comput. Struct., vol. 82, no. 9–10, pp. 781–798, 2004, DOI: https://doi.org/10.1016/j.compstruc.2004.01.002 [11] K. S. Lee and Z. W. Geem, "A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice," Comput. Methods Appl. Mech. Eng., vol. 194, no. 36–38, pp. 3902–3933, 2005, DOI: https://doi.org/10.1016/j.cma.2004.09.007 [12] O. K. Erol and I. Eksin, "A new optimization method: Big Bang–Big Crunch," Adv. Eng. Softw., vol. 37, no. 2, pp. 106–111, 2006, DOI: https://doi.org/10.1016/j.advengsoft.2005.04.005 [13] C. V. Camp, "Design of Space Trusses Using Big Bang– Big Crunch Optimization," J. Struct. Eng., vol. 133, no. 7, pp. 999–1008, 2007, DOI: https://doi.org/10.1061/(ASCE)0733-9445(2007)133:7(999) [14] L. J. Li, Z. B. Huang, F. Liu, and Q. H. Wu, "A heuristic particle swarm optimizer for optimization of pin connected structures," Comput. Struct., vol. 85, no. 7–8, pp. 340–349, 2007, DOI: https://doi.org/10.1016/j.compstruc.2006.11.020 [15] R. E. Perez and K. Behdinan, "Particle swarm approach for structural design optimization," Comput. Struct., vol. 85, no. 19–20, pp. 1579–1588, 2007, DOI: https://doi.org/10.1016/j.compstruc.2006.10.013 [16] L. Lamberti, "An efficient simulated annealing algorithm for design optimization of truss structures," Comput.Struct., vol. 86, no. 19–20, pp. 1936–1953, 2008, DOI: https://doi.org/10.1016/j.compstruc.2008.02.004 [17] A. Kaveh and S. Talatahari, "Size optimization of space trusses using Big Bang–Big Crunch algorithm," Comput. Struct., vol. 87, no. 17–18, pp. 1129–1140, 2009, DOI: https://doi.org/10.1016/j.compstruc.2009.04.011 [18] A. Kaveh and S. Talatahari, "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures," Comput. Struct., vol. 87, no. 5–6, pp. 267–283, 2009, DOI: https://doi.org/10.1016/j.compstruc.2009.01.003 [19] A. Kaveh and S. Talatahari, "A particle swarm ant colony optimization for truss structures with discrete variables," J. Constr. Steel Res., vol. 65, no. 8–9, pp. 1558–1568, 2009, DOI: https://doi.org/10.1016/j.jcsr.2009.04.021 [20] M. Sonmez, "Artificial Bee Colony algorithm for optimization of truss structures," Appl. Soft Comput., vol. 11, no. 2, pp. 2406–2418, 2011, DOI: https://doi.org/10.1016/j.asoc.2010.09.003 [21] S. O. Degertekin, "Improved harmony search algorithms for sizing optimization of truss structures," Comput. Struct., vol. 92–93, pp. 229–241, 2012, DOI: https://doi.org/10.1016/j.compstruc.2011.10.022 [22] S. O. Degertekin and M. S. Hayalioglu, "Sizing truss structures using teaching-learning-based optimization," Comput. Struct., vol. 119, pp. 177–188, 2013, DOI: https://doi.org/10.1016/j.compstruc.2012.12.011 [23] C. V. Camp and M. Farshchin, "Design of space trusses using modified teaching-learning based optimization," Eng. Struct., vol. 62–63, pp. 87–97, 2014, DOI: https://doi.org/10.1016/j.engstruct.2014.01.020 [24] A. Kaveh, T. Bakhshpoori, and E. Afshari, "An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm," Comput. Struct., vol. 143, pp. 40–59, 2014, DOI: https://doi.org/10.1016/j.compstruc.2014.07.012 [25] A. Kaveh and M. Ilchi Ghazaan, "Enhanced colliding bodies optimization for design problems with continuous and discrete variables," Adv. Eng. Softw., vol. 77, pp. 66–75, 2014, DOI: https://doi.org/10.1016/j.advengsoft.2014.08.003 [26] A. Kaveh, R. Sheikholeslami, S. Talatahari, and M. Keshvari-Ilkhichi, "Chaotic swarming of particles: A new method for size optimization of truss structures," Adv. Eng. Softw., vol. 67, pp. 136–147, 2014, DOI: https://doi.org/10.1016/j.advengsoft.2013.09.006 [27] A. Kaveh, B. Mirzaei, and A. Jafarvand, "An improved magnetic charged system search for optimization of truss structures with continuous and discrete variables," Appl. Soft Comput. J., vol. 28, pp. 400–410, 2015, DOI: https://doi.org/10.1016/j.asoc.2014.11.056 [28] A. Kaveh and V. R. Mahdavi, "Colliding Bodies Optimization method for optimum design of truss structures with continuous variables," Adv. Eng. Softw., vol. 70, pp. 1–12, 2014, DOI: https://doi.org/10.1016/j.advengsoft.2014.01.002 [29] Y.-J. Zheng, "Water wave optimization: A new natureinspired metaheuristic," Comput. Oper. Res., vol. 55, pp. 1–11, 2015, DOI: https://doi.org/10.1016/j.cor.2014.10.008 [30] C. Millán Páramo and E. Millán Romero, "Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas," INGE CUC, vol. 12, no. 2, pp. 9–16, 2016, DOI: https://doi.org/10.17981/ingecuc.12.2.2016.01
URI: http://hdl.handle.net/11323/2466
https://revistascientificas.cuc.edu.co/ingecuc/article/view/1628
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