Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Sizing optimization of truss structures using the exchange market algorithm

Document Type : Original Article

Authors
1 Associate professor, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2 Civil Engineering Department, Faculty of Technical and Engineering,, University of Mohaghegh Ardabili,Ardabil,Iran
Abstract
Optimization is a process of finding the best solution for a problem. Heuristic and metaheuristic optimization algorithms are commonly used where the search space is complex. In recent decades, the ubiquity of trusses as structural systems has made their optimization an important engineering endeavor. The primary aim of structural optimization is to determine the most suitable combination of design variables, so as to achieve satisfactory performance of the structures subjected to constraints. the three basic features of the structural optimization problem are: The design variables, the objective function, the constraints. This study evaluates the performances of the exchange market algorithm (EMA) in the structural optimization field for the first time. This optimization algorithm is inspired by the procedure of trading the shares on stock market. In the proposed method there are two different modes in EMA. In the first mode, there is no oscillation in the market where as in the second mode, the market has oscillation. For the first mode, the algorithm’s duty is to recruit people toward successful individuals, while in the second case the algorithm seeks optimal points. the member’s section area has assumed to be a decision variable, and the objective function is to minimize their weight. The member stresses and node displacements are the constraints that must maintain within the allowed limits for each condition. The implementation of exchange market algorithm has been done in MATLAB software. to quantitatively assess the performance of the algorithm, three planar trusses (10 bar, 18 bar, and 200 bar) and three space trusses (22 bar, 25 bar and 72 bar) with multiple loading conditions and design constraints have been considered. The results demonstrate that the exchange market algorithm is very effective and efficient for the optimization designs of medium scale truss structural problems.
Keywords

Subjects


  • Stolpe, M. (2016)."Truss optimization with discrete design variables: a critical review. Structural and Multidisciplinary optimization", 53, pp. 349-374.
  • Salcedo-Sanz,S (2016)."Modern meta-heuristics based on nonlinear physics processes:A review of models and design procedures." Physics Reports, Vol. 655, pp.1-70.
  • Kaveh, A,& Ghazaan, M. I. (2018)."Meta-heuristic algorithms for optimal design of real-size structures." Switzerland: Springer International Publishing.
  • Zargham, S,Ward, T. A, Ramli, R,& Badruddin, I. A. (2016).”Topology optimization: a review for structural designs under vibration problems."Structural and Multidisciplinary Optimization, Vol.53, pp 1157-1177.
  • Wolpert, D. H,& Macready, W. G. (1997).”No free lunch theorems for optimization.”IEEE transactions on evolutionary computation, Vol.1(no.1), pp 67-82.
  • Rajeev,S,& Krishnamoorthy, C.S. (1992).”Discrete optimization of structures using genetic algorithms. Journal of structural engineering,” Vol.118(no.5), pp 1233-1250.
  • Li,L.J,Huang,Z.B.,Liu, F,& Wu, Q. H. (2007).”A heuristic particle swarm optimizer for optimization of pin connected structures.”Computers & structures, Vol. 85(7-8), pp 340-349.
  • Camp,Cv,& Bichon, B.J (2004).”Design of space trusses using ant colony optimization. Journal of structural engineering,” Vol.130(no.5), pp.741-751.
  • Sonmez, M. (2011). Discrete optimum design of truss structures using artificial bee colony algorithm. Structural and multidisciplinary optimization, 43, 85-97..
  • Jawad, F. K., Mahmood, M., Wang, D., Osama, A. A., & Anas, A. J. (2021, February). Heuristic dragonfly algorithm for optimal design of truss structures with discrete variables. In Structures (Vol. 29, pp. 843-862). Elsevier..
  • Degertekin, S. O,Saka,M. P,& Hayalioglu, M. S. (2008).”Optimal load and resistance factor design of geometrically nonlinear steel space frames via tabu search and genetic algorithm.”Engineering Structures, Vol.30(no.1), pp. 197-205.
  • Kaveh, A,Ilchi Ghazaan, M,& Saadatmand, F.(2022).”Colliding bodies optimization with Morlet wavelet mutation and quadratic interpolation for global optimization problems.”Engineering with Computers, pp.1-25.
  • Kooshkbaghi, M, & Kaveh, A. (2020).”Sizing optimization of truss structures with continuous variables by artificial coronary circulation system algorithm.”Iranian Journal of Science and Technology, Transactions of Civil Engineering, vol.44, pp.1-20.
  • Kaveh, A., & Zakian, P. (2014). Optimal seismic design of reinforced concrete shear wall-frame structures. KSCE Journal of Civil Engineering, 18, 2181-2190.
  • Kaveh, A. (2014).”Advances in metaheuristic algorithms for optimal design of structures” Switzerland: Springer International Publishing. (pp. 9-40).
  • Kaveh, A, & Talatahar, S. (2008). “A hybrid particle swarm and ant colony optimization for design of truss structures.”
  • Kaveh, A,& Malakoutirad, S. (2010).”Hybrid genetic algorithm and particle swarm optimization for the force method-based simultaneous analysis and design”
  • Kaveh, A,& Talatahari, S. (2009).”Hybrid algorithm of harmony search, particle swarm and ant colony for structural design optimization. In Harmony search algorithms for structural design optimization” Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 159-198.
  • Kaveh, A,& Talatahari, S. (2012).”A hybrid CSS and PSO algorithm for optimal design of structures.”Structural Engineering and Mechanics, Vol.42(no.6), pp. 783-797.
  • Khatibinia, M,& Yazdani,H.(2018).”Accelerated multi-gravitational search algorithm for size optimization of truss structures.”Swarm and Evolutionary Computation, Vol.38, pp.109-119.
  • Javidi, A,Salajegheh, E, & Salajegheh, J. (2019).”Enhanced crow search algorithm for optimum design of structures.”Applied Soft Computing, Vol.77, pp. 274-289.
  • Alkhraisat, H, Dalbah, L.M,Al-Betar, M. A, Awadallah, M. A., Assaleh, K,& Deriche, M. (2023).”Size Optimization of Truss Structures Using Improved Grey Wolf Optimizer.”IEEE Access, Vol. 11, 13383-13397.
  • Khajeh,A., Kiani,A,Seraji,M,&Dashti,H.(2023).”Optimization of structure using hybird Harris hawks and genetic algorithm.”journal of Structural and Construction Engineering, Vol.10(no.1),pp114-132
  • Ghorbani, N., & Babaei, E. (2014).”Exchange market algorithm. Applied soft computing,” Vol.19, pp. 177-187.
  • Sonmez, M. (2011).”Artificial Bee Colony algorithm for optimization of truss structures.”Applied Soft Computing, Vol.11(no.2), pp. 2406-2418.
  • .Lee, K. S., & Geem, Z. W. (2004).”A new structural optimization method based on the harmony search algorithm.”Computers & structures, Vol.82(9-10), pp. 781-798.
  • Kaveh, A,& Zakian, P. (2018). “Improved GWO algorithm for optimal design of truss structures.”Engineering with Computers, Vol.34, pp. 685-707.
  • Sheu,C.Y,& Schmit Jr,L.A (1972).”Minimum weight design of elastic redundant trusses under multiple static loading conditions.”AIAA journal, 10(no.2), pp.155-162.
  • Degertekin,S.O,(2012).”Improved harmony search algorithms for sizing optimization of truss structures.”Computers & Structures, Vol.92, 229-241.
  • Kaveh A, Bakhshpoori T, Afshari E. An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm. Comput Struct 2014;143:40–59.
  • Kaveh, A., & Bakhshpoori, T. (2016). A new metaheuristic for continuous structural optimization: water evaporation optimization. Structural and Multidisciplinary Optimization, 54, 23-43.
  • Kaveh, A,Akbari, H,& Hosseini, S. M. (2020).”Plasma generation optimization: a new physically-based metaheuristic algorithm for solving constrained optimization problems.”Engineering Computations, Vol.38(no.4), pp.1554-1606.
  • Imai, K,& Schmit Jr, L.A (1981).”Configuration optimization of trusses. Journal of the Structural Division,” 107, pp. 745-756.
  • Khan, M. R, Willmert, K. D,& Thornton, W.A. (1979).”An optimality criterion method for large-scale structures.”AIAA journal, Vol.17(no.7), pp. 753-761.
  • Kaveh, A., & Zolghadr, A. (2017). Cyclical parthenogenesis algorithm: A new meta-heuristic algorithm.
  • Jalili, S,& Husseinzadeh Kashan, A. (2019).”An optics inspired optimization method for optimal design of truss structures.”The Structural Design of Tall and Special Buildings, Vol.28(no.6), e1598.
  • Toğan,v, & Daloğlu, A.T (2008).”An improved genetic algorithm with initial population strategy and self-adaptive member grouping.”Computers & Structures, Vol.86(11-12), pp. 1204-1218.
  • Kaveh, A,Talatahari, S,& Khodadadi, N. (2020).”Hybrid invasive weed optimization-shuffled frog-leaping algorithm for optimal design of truss structures.”Iranian Journal of Science and Technology, Transactions of Civil Engineering, Vol.44, pp. 405-420.
  • Kaveh, A,& Khayatazad,M.(2013).”Ray optimization for size and shape optimization of truss structures.”Computers & Structures, Vol.117, pp.82-94.
  • Kaveh, A,& Zakian, P. (2014).”Enhanced bat algorithm for optimal design of skeletal structures.” pp.179-212.
  • Ozbasaran, H, & Eryilmaz Yildirim, M. (2020).”Truss-sizing optimization attempts with CSA: a detailed evaluation.”Soft Computing, Vol.24, pp.16775-16801.
  • Kooshkbaghi, M., & Kaveh, A. (2020).”Sizing optimization of truss structures with continuous variables by artificial coronary circulation system algorithm.”Iranian Journal of Science and Technology, Transactions of Civil Engineering, Vol.44, pp.1-20.
  • Talatahari S, Kheirollahi M, Farahmandpour C, Gandomi AH. A multi-stage particle swarm for optimum design of truss structures. Neural Comput Appl 2013;23(5): 1297–309
  • Camp CV, Farshchin M. Design of space trusses using modified teaching-learning based optimization. Eng Struct 2014;62–63
  • Camp CV. Design of Space Trusses Using Big Bang-Big Crunch Optimization. J Struct Eng 2007;133(7):999–1008

  • Receive Date 07 August 2023
  • Revise Date 08 January 2024
  • Accept Date 15 February 2024