Estudo de Sensibilidade do Algoritmo de Colônia de Vagalumes para um Problema de Engenharia Envolvendo Dimensionamento de Treliças
DOI:
https://doi.org/10.5540/tema.2020.021.03.583Keywords:
Optimization, Plain trusses, Steel Structures, Firefly AlgorithmAbstract
A treliça é uma estrutura triangular rígida, com resistência aos esforços normais, podendo ser utilizada em telhados, mezaninos, torres de energia de telecomunicações e pontes. Logo é possível armar que esse sistema estrutural apresenta uma grande relevância no cenário da engenharia de estruturas. Nesta pesquisa é utilizado um método probabilístico de otimização global baseado em inteligência coletiva ou inteligência de enxame, com aplicações promissoras em diversos campos das ciências aplicadas, o Algoritmo de Colônia de Vagalumes (ACV), para determinação do peso mínimo de uma treliça de benchmark. Foi conduzida uma análise de sensibilidade com os parâmetros do algoritmo como: população (Npop), nímero de iterações (Ngen), parâmetro de aleatoriedade α, fator de atratividade β e parâmetro de absorção de luz (γ). A treliça utilizada nos testes foi uma estrutura de benchmark com 10 barras e essa foi otimizada obtendo um valor de peso mínimo em torno de 2284 kg, tal valor quando comparado a outros trabalhos da literatura mostram a efetividade do método adotado nesse trabalho. O software utilizado para as implementações e simulação das treliças foi o MATLAB.
References
Pintér JD. Global Optimization: Software, Test Problems, and Applications. In: Pardalos PM, Romeijn HE, editors. Handb. Glob. Optim., vol. 62, Boston, MA: Springer US; 2002, p. 515–69. doi:10.1007/978-1-4757-5362-2_15.
Łukasik S, Żak S. Firefly Algorithm for Continuous Constrained Optimization Tasks. In: Nguyen NT, Kowalczyk R, Chen S-M, editors. Comput. Collect. Intell. Semantic Web Soc. Netw. Multiagent Syst., vol. 5796, Berlin, Heidelberg: Springer Berlin Heidelberg; 2009, p. 97–106. doi:10.1007/978-3-642-04441-0_8.
Jaafari A, Razavi Termeh SV, Bui DT. Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability. J Environ Manage 2019;243:358–69. doi:10.1016/j.jenvman.2019.04.117.
Khalifehzadeh S, Fakhrzad MB. A modified firefly algorithm for optimizing a multi stage supply chain network with stochastic demand and fuzzy production capacity. Comput Ind Eng 2019;133:42–56. doi:10.1016/j.cie.2019.04.048.
La Scalia G, Micale R, Giallanza A, Marannano G. Firefly algorithm based upon slicing structure encoding for unequal facility layout problem. Int J Ind Eng Comput 2019:349–60. doi:10.5267/j.ijiec.2019.2.003.
Vishal P, Ramesh Babu A. Firefly Algorithm for Intelligent Context-Aware Sensor Deployment Problem in Wireless Sensor Network. J Circuits Syst Comput 2019;28:1950094. doi:10.1142/S0218126619500944.
Zhang J, Zhang Y, Ma Z. In silico Prediction of Human Secretory Proteins in Plasma Based on Discrete Firefly Optimization and Application to Cancer Biomarkers Identification. Front Genet 2019;10:542. doi:10.3389/fgene.2019.00542.
Zhang X, Jia W, Guan X, Xu G, Chen J, Zhu Y. Optimized deployment of a radar network based on an improved firefly algorithm. Front Inf Technol Electron Eng 2019;20:425–37. doi:10.1631/FITEE.1800749.
Guerra A, Kiousis PD. Design optimization of reinforced concrete structures. Comput Concr 2006;3:313–34. doi:10.12989/CAC.2006.3.5.313.
Singh RM. Design of Barrages with Genetic Algorithm Based Embedded Simulation Optimization Approach. Water Resour Manag 2011;25:409–29. doi:10.1007/s11269-010-9706-9.
Rana S, Islam N, Ahsan R, Ghani SN. Application of evolutionary operation to the minimum cost design of continuous prestressed concrete bridge structure. Eng Struct 2013;46:38–48. doi:10.1016/j.engstruct.2012.07.017.
Mousavi SS, Nikkhah M, Zare S. A comparative study of two meta-heuristic algorithms in optimizing cost of reinforced concrete segmental lining. J Min Environ 2018. doi:10.22044/jme.2018.7159.1566.
Camp CV, Bichon BJ, Stovall SP. Design of Steel Frames Using Ant Colony Optimization. J Struct Eng 2005;131:369–79. doi:10.1061/(ASCE)0733-9445(2005)131:3(369).
Jeong S, Seong HK, Kim CW, Yoo J. Structural design considering the uncertainty of load positions using the phase field design method. Finite Elem Anal Des 2019;161:1–15. doi:10.1016/j.finel.2019.04.002.
Salar M, Ghasemi MR, Dizangian B. Practical optimization of deployable and scissor-like structures using a fast GA method. Front Struct Civ Eng 2019;13:557–68. doi:10.1007/s11709-018-0497-z.
M. Salar, M. R. Ghasemi, and B. Dizangian, Practical optimization of deployable and scissor-like structures using a fast GA method, Frontiers of Structural
and Civil Engineering, vol. 13, pp. 557568, July 2018.
Di Cesare N, Domaszewski M. A new hybrid topology optimization method based on I-PR-PSO and ESO. Application to continuum structural mechanics. Comput Struct 2019;212:311–26. doi:10.1016/j.compstruc.2018.11.006.
Xu G, Liu B, Song J, Xiao S, Wu A. Multiobjective sorting-based learning particle swarm optimization for continuous optimization. Nat Comput 2019;18:313–31. doi:10.1007/s11047-016-9548-3.
Gholami K, Dehnavi E. A modified particle swarm optimization algorithm for scheduling renewable generation in a micro-grid under load uncertainty. Appl Soft Comput 2019;78:496–514. doi:10.1016/j.asoc.2019.02.042.
Yang X-S. Nature-Inspired Metaheuristic Algorithms. Luniver Press; 2008.
Wang H, Wang W, Zhou X, Sun H, Zhao J, Yu X, et al. Firefly algorithm with neighborhood attraction. Inf Sci 2017;382–383:374–87. doi:10.1016/j.ins.2016.12.024.
Carvalho CC. Desenvolvimento de um algoritmo de otimização evolutivo auto-adaptativo para a resolução de problemas de otimização com variáveis mistas. Dissertação. Universidade Federal de Goiás, 2018.
Gandomi AH, Yang X-S, Alavi AH. Mixed variable structural optimization using Firefly Algorithm. Comput Struct 2011;89:2325–36. doi:10.1016/j.compstruc.2011.08.002.
Yang X-S. Firefly Algorithm, Stochastic Test Functions and Design Optimisation. ArXiv10031409 Math 2010.
Yu S, Zhu S, Ma Y, Mao D. A variable step size firefly algorithm for numerical optimization. Appl Math Comput 2015;263:214–20. doi:10.1016/j.amc.2015.04.065.
Selvakumar B, Karuppiah M. Firefly algorithm based feature selection for network intrusion detection. Comput Secur 2019;81:148–55. doi:10.1016/j.cose.2018.11.005.
Talatahari S, Gandomi AH, Yun GJ. Optimum design of tower structures using Firefly Algorithm: OPTIMUM DESIGN OF TOWER STRUCTURES USING FIREFLY ALGORITHM. Struct Des Tall Spec Build 2014;23:350–61. doi:10.1002/tal.1043.
Sheikholeslami R, Khalili BG, Sadollah A, Kim J. Optimization of reinforced concrete retaining walls via hybrid firefly algorithm with upper bound strategy. KSCE J Civ Eng 2016;20:2428–38. doi:10.1007/s12205-015-1163-9.
Zhou G-D, Xie M-X, Yi T-H, Li H-N. Optimal wireless sensor network configuration for structural monitoring using automatic-learning firefly algorithm. Adv Struct Eng 2019;22:907–18. doi:10.1177/1369433218797074.
Farshi B, Alinia-ziazi A. Sizing optimization of truss structures by method of centers and force formulation. Int J Solids Struct 2010;47:2508–24. doi:10.1016/j.ijsolstr.2010.05.009.
Li LJ, Huang ZB, Liu F. A heuristic particle swarm optimization method for truss structures with discrete variables. Comput Struct 2009;87:435–43. doi:10.1016/j.compstruc.2009.01.004.
Aminifar F, Aminifar F, Nazarpour D. Optimal design of truss structures via an augmented genetic algorithm 2013:13. doi:doi:10.3906/muh-1203-13.
Camp CV, Farshchin M. Design of space trusses using modified teaching–learning based optimization. Eng Struct 2014;62–63:87–97. doi:10.1016/j.engstruct.2014.01.020.
Li LJ, Huang ZB, Liu F, Wu QH. A heuristic particle swarm optimizer for optimization of pin connected structures. Comput Struct 2007;85:340–9. doi:10.1016/j.compstruc.2006.11.020.
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