Skip to main content
SpringerLink
Log in

Modified Artificial Bee Colony Algorithm for Sizing Optimization of Truss Structures

  • Chapter
  • First Online:
Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

  • 452 Accesses

Abstract

Artificial bee colony algorithm (ABC) is a swarm intelligence method which simulates the behaviour of insects for solving optimization problems. In addition to its various applications, the ABC has been used as an efficient structural optimization tool. In this study, a modified artificial bee colony algorithm (MABC) is developed for the sizing optimization of truss structures in order to increase the efficiency of the standard ABC algorithm. Minimum weight design of truss structures is performed under stress and displacement constraints. Four classical optimization problems of truss structures are presented to verify the robustness of the proposed MABC algorithm. Numerical results reveal that MABC could obtain approximately the same or better designs than the standard ABC algorithm and other metaheuristic optimization methods. Furthermore, the MABC algorithm converged much more quickly to the optimum design than the other methods in almost all cases.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan

    Google Scholar 

  2. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26(1):29–41

    Article  Google Scholar 

  3. Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley

    Google Scholar 

  4. Yang XS, Deb S (2010) Eagle strategy using Levy walk and firefly algorithms for stochastic optimization. In: Cruz C, González JR et al (eds) Nature inspired cooperative strategies for optimization (NICSO2010), studies in computational intelligence, vol 284. Springer, pp 101–111

    Google Scholar 

  5. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  6. Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70

    Article  Google Scholar 

  7. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  8. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169(2016):1–12

    Article  Google Scholar 

  9. Xian-Bing Meng XZ, Gao LL, Liu Yu, Zhang H (2016) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28:673–687

    Article  Google Scholar 

  10. Ehsan J, Mohammad C (2018) Tackling global optimization problems with a novel algorithm-mouth brooding fish algorithm. Appl Soft Comput 62:987–1002

    Article  Google Scholar 

  11. Sankalap A, Satvir S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715–734

    Article  MATH  Google Scholar 

  12. Mohit J, Vijander S, Asha R (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175

    Article  Google Scholar 

  13. Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, Boston (MA), USA

    Book  MATH  Google Scholar 

  14. Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  15. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Design 43(3):303–315

    Article  Google Scholar 

  16. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2012) Mine blast algorithm for optimization of truss structures with discrete variables. Comput Struct 102–103:49–63

    Article  Google Scholar 

  17. Gonçalves MS, Lopez RH, Miguel LFF (2015) Search group algorithm: a new metaheuristic method for the optimization of truss structures. Comput Struct 153:165–184

    Article  Google Scholar 

  18. Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

  19. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inform Sciences 179(13):2232–2248

    Article  MATH  Google Scholar 

  20. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289

    Article  MATH  Google Scholar 

  21. Kaveh A, Khayat Azad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  22. Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27

    Article  Google Scholar 

  23. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm–a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166

    Article  Google Scholar 

  24. Bekdaş G, Nigdeli SM, Yang XS (2015) Sizing optimization of truss structures using flower pollination algorithm. Appl Soft Comput 37:322–331

    Article  Google Scholar 

  25. Kaveh A, Bakhshpoori T (2016) A new metaheuristic for continuous structural optimization: water evaporation optimization. Struct Multidiscip Optimiz 54(1):23–43

    Article  Google Scholar 

  26. Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84

    Article  Google Scholar 

  27. Kaveh A, Zolghadr A (2017) Cyclical parthenogenesis algorithm for guided modal strain energy based structural damage detection. Appl Soft Comput 57:250–264

    Article  Google Scholar 

  28. Lamberti L, Pappalettere C (2011) Metaheuristic design optimization of skeletal structures: a review. Comput Technol Rev 4(1):1–32

    Google Scholar 

  29. Saka MP, Dogan E (2012) Recent developments in metaheuristic algorithms: a review. Comput Technol Rev 5:31–78

    Article  Google Scholar 

  30. Kaveh A (2014) Advances in metaheuristic algorithms for optimal design of structures. Springer International Publishing, Switzerland, pp 9–40

    MATH  Google Scholar 

  31. Kaveh A (2017) Applications of metaheuristic optimization algorithms in civil engineering. Springer International Publishing, Switzerland

    Book  MATH  Google Scholar 

  32. Kaveh A, Ghazaan MI (2018) Meta-heuristic algorithms for optimal design of real-size structures. Springer International Publishing, Switzerland

    Book  MATH  Google Scholar 

  33. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, technical report-TR06, Erciyes University Engineering Faculty Computer Engineering Department

    Google Scholar 

  34. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  35. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  36. Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Frankl Ins 346(4):328–348

    Article  MathSciNet  MATH  Google Scholar 

  37. Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9(2):625–631

    Article  Google Scholar 

  38. Hemamalini S, Simon SP (2010) Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions. Electr Power Compos Syst 38(7):786–803

    Article  Google Scholar 

  39. Omkar SN, Senthilnath J, Khandelwal R, Naik GN, Gopalakrishnan S (2011) Artificial bee colony (ABC) for multi-objective design optimization of composite structures. Appl Soft Comput 11(1):489–499

    Article  Google Scholar 

  40. Rao RV, Patel VK (2011) Optimization of mechanical draft counter flow wet-cooling tower using artificial bee colony algorithm. Energ Convers Manage 52(7):2611–2622

    Article  Google Scholar 

  41. Şahin AŞ, Kılıç B, Kılıç U (2011) Design and economic optimization of shell and tube heat exchangers using artificial bee colony (ABC) algorithm. Energ Convers Manage 52(11):3356–3362

    Article  Google Scholar 

  42. Szeto WY, Wu Y, Ho SC (2011) An artificial bee colony algorithm for the capacitated vehicle routing problem. Eur J Oper Res 215(1):126–135

    Article  Google Scholar 

  43. Ayan K, Kılıç U (2012) Artificial bee colony algorithm solution for optimal reactive power flow. Appl Soft Comput 12(5):1477–1482

    Article  Google Scholar 

  44. Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014

    Article  Google Scholar 

  45. Lin SW, Ying KC (2013) Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm. J Oper Res Soc 64(2):293–311

    Article  Google Scholar 

  46. Kıran MS, İşcan H, Gündüz M (2013) The analysis of discrete artificial bee colony algorithm with neighborhood operator on traveling salesman problem. Neural Comput Appl 23(1):9–21

    Article  Google Scholar 

  47. Bulut O, Tasgetiren MF (2014) An artificial bee colony algorithm for the economic lot scheduling problem. Int J Prod Res 52(4):1150–1170

    Article  Google Scholar 

  48. Tiwar R, Waghole V (2015) Optimization of spherical roller bearing design using artificial bee colony algorithm and grid search method. Int J Comput Meth Eng Sci Mech 16(4):221–233

    Article  Google Scholar 

  49. Luo J, Liu Q, Yang Y, Li X, Chen MR, Cao W (2017) An artificial bee colony algorithm for multi-objective optimisation. Appl Soft Comput 50:235–251

    Article  Google Scholar 

  50. Choong SM, El-Shafie A, Mohtar WW (2017) Optimisation of multiple hydropower reservoir operation using artificial bee colony algorithm. Water Resour Manage 31(4):1397–1411

    Article  Google Scholar 

  51. Pérez CJ, Vega-Rodríguez MA, Reder K, Flörke M (2017) A multi-objective artificial bee colony-based optimization approach to design water quality monitoring networks in river basins. J Clean Prod 166:579–589

    Article  Google Scholar 

  52. Banharnsakun A (2018) Multiple traffic sign detection based on the artificial bee colony method. Evolv Syst 9(3):255–264

    Article  Google Scholar 

  53. Dokeroglu T, Sevinc E, Cosar A (2019) Artificial bee colony optimization for the quadratic assignment problem. Appl Soft Comput 76:595–606

    Article  Google Scholar 

  54. Sharma TK, Abraham A (2020) Artificial bee colony with enhanced food locations for solving mechanical engineering design problems. J Amb Intel Human Comput 11(1):267–290

    Article  Google Scholar 

  55. Fairee S, Khompatraporn C, Sirinaovakul B, Prom-On S (2020) Trim loss optimization in paper production using reinforcement artificial bee colony. IEEE Access 8:130647–130660

    Article  Google Scholar 

  56. Karaboga D, Akay B (2011) A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput 11(3):3021–3031

    Article  Google Scholar 

  57. Li JQ, Xie SX, Pan QK, Wang S (2011) A hybrid artificial bee colony algorithm for flexible job shop scheduling problems. Int J Comput Commun Control 6(2):286–296

    Article  Google Scholar 

  58. Gao W, Liu S (2011) Improved artificial bee colony algorithm for global optimization. Inform Process Lett 111(17):871–882

    Article  MathSciNet  MATH  Google Scholar 

  59. Gao WF, Liu SY (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697

    Article  MATH  Google Scholar 

  60. Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inform Sci 192:120–142

    Article  Google Scholar 

  61. Zhang R, Song S, Wu C (2013) A hybrid artificial bee colony algorithm for the job shop scheduling problem. Int J Prod Econ 141(1):167–178

    Article  Google Scholar 

  62. Yildiz AR (2013) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13(5):2906–2912

    Article  Google Scholar 

  63. Chun-Feng W, Kui L, Pei-Ping S (2014) Hybrid artificial bee colony algorithm and particle swarm search for global optimization. Math Probl Eng 832949

    Google Scholar 

  64. Zhang C, Zheng J, Zhou Y (2015) Two modified artificial bee colony algorithms inspired by grenade explosion method. Neurocomputing 151:1198–1207

    Article  Google Scholar 

  65. Mao M, Duan Q (2016) Modified artificial bee colony algorithm with self-adaptive extended memory. Cybernet Syst 47(7):585–601

    Article  Google Scholar 

  66. Ma L, Zhu Y, Zhang D, Niu B (2016) A hybrid approach to artificial bee colony algorithm. Neural Comput Appl 27(2):387–409

    Article  Google Scholar 

  67. Gao WF, Huang LL, Wang J, Liu SY, Qin CD (2016) Enhanced artificial bee colony algorithm through differential evolution. Appl Soft Comput 48:137–150

    Article  Google Scholar 

  68. Guesmi T, Alshammari BM (2017) An improved artificial bee colony algorithm for robust design of power system stabilizers. Eng Comput 34(7):2131–2153

    Article  Google Scholar 

  69. Liang Y, Wan Z, Fang D (2017) An improved artificial bee colony algorithm for solving constrained optimization problems. Int J Mach Learn Cyber 8(3):739–754

    Article  Google Scholar 

  70. Ghambari S, Rahati A (2018) An improved artificial bee colony algorithm and its application to reliability optimization problems. Appl Soft Comput 62:736–767

    Article  Google Scholar 

  71. Mann PS, Singh S (2019) Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks. Artif Intell Rev 51(3):329–354

    Article  Google Scholar 

  72. Lei D, Yuan Y, Cai J (2020) An improved artificial bee colony for multi-objective distributed unrelated parallel machine scheduling. Int J Prod Res:1–13

    Google Scholar 

  73. Sonmez M (2011) Artificial bee colony algorithm for optimization of truss structures. Appl Soft Comput 11(2):2406–2418

    Article  Google Scholar 

  74. Degertekin SO (2012) Optimum design of geometrically non-linear steel frames using artificial bee colony algorithm. Steel Compos Struct 12(6):505–522

    Article  Google Scholar 

  75. Ozturk HT, Durmus A, Durmus A (2012) Optimum design of a reinforced concrete beam using artificial bee colony algorithm. Comput Conc 10(3):295–306

    Article  Google Scholar 

  76. Fiouz AR, Obeydi M, Forouzani H, Keshavarz A (2012) Discrete optimization of trusses using an artificial bee colony (ABC) algorithm and the fly-back mechanism. Struct Eng Mech 44(4):501–519

    Article  Google Scholar 

  77. Sun H, Luş H, Betti R (2013) Identification of structural models using a modified artificial bee colony algorithm. Comput Struct 116:59–74

    Article  Google Scholar 

  78. Kang F, Li J, Ma Z (2013) An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis. Eng Optimiz 45(2):207–223

    Article  MathSciNet  Google Scholar 

  79. Ozturk HT, Durmus A (2013) Optimum cost design of RC columns using artificial bee colony algorithm. Struct Eng Mech 45(5):643–654

    Article  Google Scholar 

  80. Jahjouh MM, Arafa MH, Alqedra MA (2013) Artificial bee colony (ABC) algorithm in the design optimization of RC continuous beams. Struct Multidiscip Optimiz 6:963–979

    Article  Google Scholar 

  81. Sonmez M, Aydin E, Karabork T (2013) Using an artificial bee colony algorithm for the optimal placement of viscous dampers in planar building frames. Struct Multidiscip Optimiz 48(2):395–409

    Article  Google Scholar 

  82. Park JY, Han SY (2013) Application of artificial bee colony algorithm to topology optimization for dynamic stiffness problems. Comput Math Appl 66(10):1879–1891

    Article  MathSciNet  MATH  Google Scholar 

  83. Yahya M, Saka MP (2014) Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights. Automat Constr 38:14–29

    Article  Google Scholar 

  84. Park JY, Han SY (2015) Topology optimization for nonlinear structural problems based on artificial bee colony algorithm. Int J Precis Eng Man 16(1):91–97

    Article  Google Scholar 

  85. Aydin E, Sonmez M, Karabork T (2015) Optimal placement of elastic steel diagonal braces using artificial bee colony algorithm. Steel Compos Struct 19(2):349–368

    Article  Google Scholar 

  86. Xu HJ, Ding ZH, Lu ZR, Liu JK (2015) Structural damage detection based on chaotic artificial bee colony algorithm. Struct Eng Mech 55(6):1223–1239

    Article  Google Scholar 

  87. Aydoğdu İ, Akın A, Saka MP (2016) Design optimization of real-world steel space frames using artificial bee colony algorithm with Levy flight distribution. Adv Eng Softw 92:1–14

    Article  Google Scholar 

  88. Xu HJ, Ding ZH, Lu ZR, Liu JK (2016) Structural damage detection using a modified artificial bee colony algorithm. CMES-Comp Model Eng 111:335–355

    Google Scholar 

  89. Ozturk HT, Turkeli E, Durmus A (2016) Optimum design of RC shallow tunnels in earthquake zones using artificial bee colony and genetic algorithms. Comput Conc 17(4):435–453

    Article  Google Scholar 

  90. Tapao A, Cheerarot R (2017) Optimal parameters and performance of artificial bee colony algorithm for minimum cost design of reinforced concrete frames. Eng Struct 151:802–820

    Article  Google Scholar 

  91. Liu H, He X, Jiao Y (2018) Damage identification algorithm of hinged joints for simply supported slab bridges based on modified hinge plate method and artificial bee colony algorithms. Algorithms 11(12):198

    Article  MathSciNet  MATH  Google Scholar 

  92. Mikaeil R, Beigmohammadi M, Bakhtavar E, Haghshenas SS (2019) Assessment of risks of tunneling project in Iran using artificial bee colony algorithm. SN Appl Sci 1(12):1711

    Article  Google Scholar 

  93. Degertekin SO (2012) Improved harmony search algorithms for sizing optimization of truss structures. Comput Struct 92:229–241

    Article  Google Scholar 

  94. Degertekin SO, Hayalioglu MS (2013) Sizing truss structures using teaching-learning-based optimization. Comput Struct 119:177–188

    Article  Google Scholar 

  95. Camp CV, Farshchin M (2014) Design of space trusses using modified teaching–learning based optimization. Eng Struct 62:87–97

    Article  Google Scholar 

  96. Kaveh A, Bakhshpoori T, Afshari E (2014) An efficient hybrid particle swarm and swallow swarm optimization algorithm. Comput Struct 143:40–59

    Article  Google Scholar 

  97. Degertekin SO, Lamberti L, Hayalioglu MS (2017) Heat transfer search algorithm for sizing optimization of truss structures. Lat Am J Solids Stru 14(3):373–397

    Article  Google Scholar 

  98. Kaveh A, Dadras A, Montazeran AH (2018) Chaotic enhanced colliding bodies algorithms for size optimization of truss structures. Acta Mech 229(7):2883–2907

    Article  MathSciNet  MATH  Google Scholar 

  99. Lamberti L, Pappalettere C (2009) An improved harmony-search algorithm for truss structure optimization. In: Topping BHV, Neves LFC, Barros RC (eds) Proceedings of the twelfth international conference civil, structural and environmental engineering computing. Civil-Comp Press, Stirlingshire (UK)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Dicle University, 21280, Diyarbakir, Turkey

    Sadik Ozgur Degertekin & Mehmet Sedat Hayalioglu

  2. Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, 70125, Bari, Italy

    Luciano Lamberti

Authors
  1. Sadik Ozgur Degertekin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luciano Lamberti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehmet Sedat Hayalioglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sadik Ozgur Degertekin .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Serdar Carbas

  2. Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Abdurrahim Toktas

  3. Department of Computer Engineering, Tarsus University, Tarsus, Turkey

    Deniz Ustun

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Degertekin, S.O., Lamberti, L., Hayalioglu, M.S. (2021). Modified Artificial Bee Colony Algorithm for Sizing Optimization of Truss Structures. In: Carbas, S., Toktas, A., Ustun, D. (eds) Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-33-6773-9_4

Download citation

Publish with us

Policies and ethics

Search

Navigation