Skip to main content
Log in

Optimal design of truss structures with frequency constraints: a comparative study of DE, IDE, LSHADE, and CMAES algorithms

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

A Correction to this article was published on 14 July 2022

This article has been updated

Abstract

The present study examines the performance of three powerful methods including the original differential evolution (DE), the improved differential evolution (IDE), and the winner of the CEC-2014 competition, LSHADE, in addition to the covariance matrix adaptation evolution strategy (CMAES) for size optimization of truss structures under natural frequency constraints. Despite the abundant researches on novel meta-heuristic algorithms in the literature, the application of CMAES, one of the most powerful and reliable optimization algorithms, on the optimal solution of the truss structures has received scant attention. For consistent comparison between these algorithms, four stopping criteria are defined and for each of these criteria, all algorithms are executed 30 times. Statistical analysis of the results for each algorithm is performed, and the mean, standard deviation, minimum, and maximum for 30 executions of the algorithms are calculated. For the small population size, results show that the CMAES algorithm not only has the best performance and the least standard deviation values among other given algorithms in all cases but also finds the best ever optimal solutions for the design of the benchmark truss structures which have not been reported in other studies. However, by increasing the number of decision variables and the population size, the CMAES algorithm needs more function evaluations to converge to the global optimal solution with higher accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Change history

References

  1. Kiusalaas J, Shaw RCJ (1978) An algorithm for optimal structural design with frequency constraints. Int J Numer Methods Eng 13(2):283–295

    MATH  Google Scholar 

  2. Levy R, Chai K (1979) Implementation of natural frequency analysis and optimality criterion design. Comput Struct 10(1):277–282

    MATH  Google Scholar 

  3. Khot N (1985) Optimization of structures with multiple frequency constraints. Comput Struct 20(5):869–876

    MATH  Google Scholar 

  4. Sadek EA (1986) Dynamic optimization of framed structures with variable layout. Int J Numer Methods Eng 23(7):1273–1294

    MATH  Google Scholar 

  5. Grandhi RV, Venkayya VB (1988) Structural optimization with frequency constraints. AIAA J 26(7):858–866

    MATH  Google Scholar 

  6. Sedaghati R, Suleman A, Tabarrok B (2002) Structural optimization with frequency constraints using the finite element force method. AIAA J 40(2):382–388

    Google Scholar 

  7. Sarcheshmehpour M, Estekanchi HE, Moosavian H (2020) Optimum seismic design of steel framed-tube and tube-in-tube tall buildings. Struct Des Tall Spec Build 29(14):e1782

    Google Scholar 

  8. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99

    Google Scholar 

  9. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    MathSciNet  MATH  Google Scholar 

  10. Hansen N (2006) The CMA evolution strategy: a comparing review. Springer, Berlin, pp 75–102

    Google Scholar 

  11. Moosavian N, Roodsari BK (2014) Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm Evol Comput 17:14–24

    Google Scholar 

  12. Moosavian N (2015) Soccer league competition algorithm for solving knapsack problems. Swarm Evol Comput 20:14–22

    Google Scholar 

  13. Moosavian N, Roodsari B (2014) Soccer league competition algorithm, a new method for solving systems of nonlinear equations. Int J Intell Sci 4:7–16

    Google Scholar 

  14. Kennedy J, Eberhart R (1995) Particle swarm optimization, in Proceedings of ICNN’95—international conference on neural networks 4:1942–1948

  15. Wei L, Mei Z, Guangming W, Guang M (2005) Truss optimization on shape and sizing with frequency constraints based on genetic algorithm. Comput Mech 35:361–368

    MATH  Google Scholar 

  16. Wei L, Tang T, Xie X, Shen W (2011) Truss optimization on shape and sizing with frequency constraints based on parallel genetic algorithm. Struct Multidiscip Optim 43:665–682

    Google Scholar 

  17. Kaveh A, Zolghadr A (2012) Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability. Comput Struct 102–103:14–27

    Google Scholar 

  18. Kaveh A, Mahdavi VR (2013) Optimal design of structures with multiple natural frequency constraints using a hybridized BB-BC/quasi-newton algorithm. Period Polytech Civ Eng 57(1):27–38

    Google Scholar 

  19. Kaveh A, Zolghadr A (2014) Democratic PSO for truss layout and size optimization with frequency constraints. Comput Struct 130:10–21

    Google Scholar 

  20. Kaveh A, Zolghadr A (2014) A new PSRO algorithm for frequency constraint truss shape and size optimization. Struct Eng Mech 52(3):445–468

    Google Scholar 

  21. Kaveh A, Javadi SM (2014) Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm. Acta Mech 225(6):1595–1605

    Google Scholar 

  22. Kaveh A, Ghazaan MI (2017) Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints. Acta Mech 1:307–322

    MathSciNet  Google Scholar 

  23. Kaveh A, Zolghadr A (2017) Cyclical parthenogenesis algorithm for layout optimization of truss structures with frequency constraints. Eng Optim 49(8):1317–1334

    Google Scholar 

  24. Kaveh A, Mahjoubi S (2019) Hypotrochoid spiral optimization approach for sizing and layout optimization of truss structures with multiple frequency constraints. Eng Comput 35:1443–1462

    Google Scholar 

  25. Miguel LFF, Miguel LFF (2012) Shape and size optimization of truss structures considering dynamic constraints through modern metaheuristic algorithms. Expert Syst Appl 39(10):9458–9467

    Google Scholar 

  26. Zuo W, Bai J, Li B (2014) A hybrid OC-GA approach for fast and global truss optimization with frequency constraints. Appl Soft Comput 14:528–535

    Google Scholar 

  27. Tejani GG, Savsani VJ, Bureerat S, Patel VK, Savsani P (2019) Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms. Eng Comput 535:499–517

    Google Scholar 

  28. Vu TV (2015) Weight minimization of trusses with natural freqency constraints. In: Conference: WCSMO-11

  29. Ho-Huu V, Vo-Duy T, Luu-Van T, Le-Anh L, Nguyen-Thoi T (2016) Optimal design of truss structures with frequency constraints using improved differential evolution algorithm based on an adaptive mutation scheme. Autom Constr 68:81–94

    Google Scholar 

  30. Pham HA (2016) Truss optimization with frequency constraints using enhanced differential evolution based on adaptive directional mutation and nearest neighbor comparison. Adv Eng Softw 102:142–154

    Google Scholar 

  31. Bureerat S, Pholdee N (2015) Optimal truss sizing using an adaptive differential evolution algorithm. J Comput Civ Eng 30:04015019

    MATH  Google Scholar 

  32. Lieu QX, Do DT, Lee J (2018) An adaptive hybrid evolutionary firefly algorithm for shape and size optimization of truss structures with frequency constraints. Comput Struct 195:99–112

    Google Scholar 

  33. Khatibinia M, Naseralavi SS (2014) Truss optimization on shape and sizing with frequency constraints based on orthogonal multi-gravitational search algorithm. J Sound Vib 333(24):6349–6369

    Google Scholar 

  34. Kumar S, Tejani GG, Mirjalili S (2019) Modified symbiotic organisms search for structural optimization. Eng Comput 35:1269–1296

    Google Scholar 

  35. Kaveh A, Zolghadr A (2014) Comparison of nine meta-heuristic algorithms for optimal design of truss structures with frequency constraints. Adv Eng Softw 76:9–30

    Google Scholar 

  36. Kaveh A, Ilchi Ghazaan M (2015) Layout and size optimization of trusses with natural frequency constraints using improved ray optimization algorithm. Iran J Sci Technol Trans Civ Eng 39(C2+):395–408

    Google Scholar 

  37. Kaveh A, Ilchi Ghazaan M (2015) Hybridized optimization algorithms for design of trusses with multiple natural frequency constraints. Adv Eng Softw 79:137–147

    Google Scholar 

  38. Wang D, Zhang W, Jiang J (2002) Truss shape optimization with multiple displacement constraints. Comput Methods Appl Mech Eng 191(33):3597–3612

    MATH  Google Scholar 

  39. Su GS, Zhang Y, Wu ZX, Yan LB (2012) Optimization design of trusses based on covariance matrix adaptation evolution strategy algorithm, advances in design technology. Appl Mech Mater 215:133–137 (Trans Tech Publications Ltd, 11)

    Google Scholar 

  40. Ghosh S, Das S, Roy S, Islam SM, Suganthan P (2012) A differential covariance matrix adaptation evolutionary algorithm for real parameter optimization. Inf Sci 182(1):199–219 (Nature-Inspired Collective Intelligence in Theory and Practice)

    MathSciNet  Google Scholar 

  41. de Melo VV, Iacca G (2014) A modified covariance matrix adaptation evolution strategy with adaptive penalty function and restart for constrained optimization. Expert Syst Appl 41(16):7077–7094

    Google Scholar 

  42. Yang W, Yue Z, Li L, Yang F, Wang P (2017) Optimization design of unitized panels with stiffeners in different formats using the evolutionary strategy with covariance matrix adaptation. Proc Inst Mech Eng Part G J Aerosp Eng 231(9):1563–1573

    Google Scholar 

  43. Moosavian N, Moosavian H (2017) Testing soccer league competition algorithm in comparison with ten popular meta-heuristic algorithms for sizing optimization of truss structures. Int J Eng 30:926–936

    Google Scholar 

  44. Grandhi R (1993) Structural optimization with frequency constraints—a review. AIAA J 31(12):2296–2303

    MATH  Google Scholar 

  45. Bouzarkouna Z, Ding DY, Auger A (2012) Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models. Comput Geosci 16:75–92

    Google Scholar 

  46. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    MATH  Google Scholar 

  47. Friedman M (1940) A comparison of alternative tests of significance for the problem of \(m\) rankings. Ann Math Stat 11(1):86–92

    MathSciNet  MATH  Google Scholar 

  48. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Google Scholar 

  49. Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC), pp. 1658–1665

  50. Gomes HM (2011) Truss optimization with dynamic constraints using a particle swarm algorithm. Expert Syst Appl 38(1):957–968

    Google Scholar 

  51. Tejani GG, Savsani VJ, Patel VK (2016) Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization. J Comput Des Eng 3(3):226–249

    Google Scholar 

  52. Baykasoğlu A, Baykasoğlu C (2021) Weighted superposition attraction-repulsion (WSAR) algorithm for truss optimization with multiple frequency constraints. Structures 30:253–264

    Google Scholar 

  53. Canfield RA, Venkayya VB, Grandhi RV (1989) Structural optimization with stiffness and frequency constraints. Mech Struct Mach 17(1):95–110

    Google Scholar 

  54. Ho-Huu V, Nguyen-Thoi T, Truong-Khac T, Le-Anh L, Vo-Duy T (2018) An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints. Neural Comput Appl 29:167–185

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H. Moosavian.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moosavian, H., Mesbahi, P., Moosavian, N. et al. Optimal design of truss structures with frequency constraints: a comparative study of DE, IDE, LSHADE, and CMAES algorithms. Engineering with Computers 39, 1499–1517 (2023). https://doi.org/10.1007/s00366-021-01534-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01534-0

Keywords

Navigation