Advertisement

Enhanced Firefly Algorithm for Optimum Steel Construction Design

  • S. CarbasEmail author
Chapter
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

The philosophy of structural engineering is the consideration of safety, economy and aesthetics in fundamentally meeting the requirement for sheltering. So, the design of the structures as to be safe and at the same time economical is the main aim of the designers. Thus, not only the construction is to be designed as safely carrying the calculated design loads limited to the structural provisions, but also the minimum level of material is to be used. So then, the steel structural engineers perform minimum weighted designs in order to select the optimum one among the feasible designs within the provision limits. Hence, a variety of structural optimization methods have been developed to solve these complex engineering problems. The traditional gradient-based mathematical methods are not sufficient to solve those tedious design problems. To this end, metaheuristics have been effectively utilized as an instrument of achieving the optimal structural designs. In this chapter, an enhanced version of firefly algorithm, which is based on the social behaviours of fireflies while communicating with each other, to prevent it from confinement in a local optima is presented in order to obtain the optimum design of various types of steel constructions under code provisions that such problem is categorized as discrete nonlinear programming problem.

Keywords

Steel construction Optimum design Structural optimization Enhanced firefly algorithm Metaheuristics 

References

  1. 1.
    Yang XS (2010) Engineering optimization; an introduction with metaheuristic applications. Wiley, LondonCrossRefGoogle Scholar
  2. 2.
    Rozvany GIN (1993) Optimization of large structural sytems. NATO ASI Series, Series E: applied sciences. Springer, BerlinGoogle Scholar
  3. 3.
    Luke S (2010) Essentials of metaheuristics, 2nd ed. Lulu, http://cs.gmu.edu/~sean/book/metaheuristics/
  4. 4.
    Kochenberger GA, Glover F (2003) Handbook of meta-heuristics. Kluwer Academic Publishers, DordrechtzbMATHGoogle Scholar
  5. 5.
    Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(30):268–308CrossRefGoogle Scholar
  6. 6.
    De Castro LN, Von Zuben FJ (2005) Recent developments in biologically inspired computing. Idea Group Publishing, HersheyCrossRefGoogle Scholar
  7. 7.
    Dreo J, Petrowski A, Siarry P, Taillard E (2006) Meta-heuristics for hard optimization. Springer, BerlinzbMATHGoogle Scholar
  8. 8.
    Gonzales TF (2007) Handbook of approximation algorithms and metaheuristics. Chapman & Hall, CRC Press, LondonGoogle Scholar
  9. 9.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MIGoogle Scholar
  10. 10.
    Goldberg DE (1983) Computer-aided pipeline operation using genetic algorithms and rule learning, Ph.D. thesis,. University of Michigan, Ann Arbor, MIGoogle Scholar
  11. 11.
    Kumar C, Prakash S, Kumar Gupta T, Prasad Sahu D (2014) Variant of genetic algorithm and its applications. Int J Art Neural Net 4(4):8–12Google Scholar
  12. 12.
    Singh B (2014) A survey of the variants of genetic algorithm. Int J Sci Eng Res 5(6):1261–1264Google Scholar
  13. 13.
    Elsayed SM, Sarker RA, Essam DL (2010) A comparative study of different variants of genetic algorithms for constrained optimization. In: Deb K et al (eds) Simulated evolution and learning. SEAL 2010. Lecture notes in computer science, vol 6457. Springer, Berlin, HeidelbergGoogle Scholar
  14. 14.
    Bineet M, Rakesh Kumar P (2009) Genetic algorithm and its variants: theory and applications. BTech Thesis, National Institute of Technology, RourkelaGoogle Scholar
  15. 15.
    Affenzeller M (2003) New variants of genetic algorithms applied to problems of combinatorial optimization. In: Trappl R (ed) Cybernetics and systems, vol 1. Austrian Society for Cybernetic StudiesGoogle Scholar
  16. 16.
    Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimisation: a novel method for constrained mechanical design optimisation problems. Comput Aided Des 43:303–315CrossRefGoogle Scholar
  17. 17.
    Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimisation over continuous spaces. J Glob Optim 1:341–359zbMATHCrossRefGoogle Scholar
  18. 18.
    Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Erol Osman K, Eksin Ibrahim (2006) A new optimization method: Big Bang-Big Crunch. Adv Eng Soft 37(2):106–111CrossRefGoogle Scholar
  20. 20.
    Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di MilanoGoogle Scholar
  21. 21.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the ieee international conference on neural networks, Perth, Australia, pp 1942–1948Google Scholar
  22. 22.
    Venkata R (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34Google Scholar
  23. 23.
    Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRefGoogle Scholar
  24. 24.
    Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimisation: artificial bee colony algorithm. J Glob Optim 39(3):459–471zbMATHCrossRefGoogle Scholar
  25. 25.
    Fred G (1989) Tabu search-part I. ORSA J Comput 1(3):190–206zbMATHCrossRefGoogle Scholar
  26. 26.
    Kaveh A, Talatahari S (2010) A novel heuristic optimisation method: charged system search. Acta Mech 213(3–4):267–289zbMATHCrossRefGoogle Scholar
  27. 27.
    Reynolds RG (1994) An introduction to cultural algorithms evolutionary programming. In: Proceeding of 3rd annual conference, World Scientific, River Edge, NJ, USA, pp 131–139Google Scholar
  28. 28.
    Saka MP, Carbas S, Aydogdu I, Akin A, Geem ZW (2015) Comparative study on recent metaheuristic algorithms in design optimization of cold-formed steel structures. In: Lagaros N, Papadrakakis M (eds) Engineering and Applied sciences optimization. Computational methods in applied sciences, vol 38. Springer, ChamGoogle Scholar
  29. 29.
    Saka MP, Carbas S, Aydogdu I, Akin A (2016) Use of swarm intelligence in structural steel design optimization. In: Yang XS, Bekdaş G, Nigdeli S (eds) Metaheuristics and optimization in civil engineering. Modeling and optimization in science and technologies, vol 7. Springer, ChamGoogle Scholar
  30. 30.
    Kaveh A (2017) Advances in metaheuristic algorithms for optimal design of structures, 2nd edn. Springer, ChamzbMATHCrossRefGoogle Scholar
  31. 31.
    Ewens MJ (2011) What changes has mathematics made to the Darwinian theory? In: Chalub FACC, Rodrigues JF (eds) The mathematics of Darwin’s legacy, mathematics and biosciences in interaction. Springer, BaselGoogle Scholar
  32. 32.
    Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, BristolGoogle Scholar
  33. 33.
    Yang XS (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput 29(2):175–184CrossRefGoogle Scholar
  34. 34.
    Jati GK, Suyanto (2011) Evolutionary discrete firefly algorithm for travelling salesman problem. In: Bouchachia A (eds) Adaptive and intelligent systems. ICAIS 2011. Lecture notes in computer science, vol 6943. Springer, Berlin, HeidelbergCrossRefGoogle Scholar
  35. 35.
    Dey N (2017) Advancements in applied metaheuristic computing. IGI GlobalGoogle Scholar
  36. 36.
    Jagatheesan K, Anand B, Samanta S, Dey N, Ashour AS, Balas VE (2019) Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm. IEEE/CAA J Autom Sinica 6(2):589–594CrossRefGoogle Scholar
  37. 37.
    Kumar R, Talukdar FA, Dey N, Balas VE (2018) Quality factor optimization of spiral inductor using firefly algorithm and its application in amplifier. Int J Adv Intel Paradigms 11(3–4):299–314CrossRefGoogle Scholar
  38. 38.
    Carbas S (2016) Design optimization of steel frames using an enhanced firefly algorithm. Eng Optim 48(12):2007–2025CrossRefGoogle Scholar
  39. 39.
    Yu WW (1973) Cold-formed steel structures; design, analysis, construction. McGraw-Hill Book Company, USAGoogle Scholar
  40. 40.
    AISC-LRFD (2001) Load and resistance factor design (LRFD), vol 1, Structural members specifications codes, 3rd edn. American Institute of Steel ConstructionGoogle Scholar
  41. 41.
    AISI (2002) Cold-formed steel design manual, American Iron and Steel InstituteGoogle Scholar
  42. 42.
    AISI S100-07 (2007) North American specification for the design of cold-formed steel structural members. American Iron and Steel InstituteGoogle Scholar
  43. 43.
    AISI D100-08 (2008) Excerpts-gross section property tables, cold-formed steel design manual, Part I: Dimensions and properties. American Iron and Steel InstituteGoogle Scholar
  44. 44.
    Ad Hoc Committee on Serviceability (1986) Structural serviceability: a critical appraisal and research needs. J Struct Eng ASCE 112(12):2646–2664CrossRefGoogle Scholar
  45. 45.
    Lukasik S, Zak S (2009) Firefly algorithm for continuous constrained optimization tasks. In: 1st international conference on computational collective intelligence, semantic web, social networks and multiagent systems, Wrodaw, Poland, pp 97–106Google Scholar
  46. 46.
    Fraga H (2008) Firefly luminescence: a historical perspective and recent developments. J Photochem Photobiol Sci 7:146–158CrossRefGoogle Scholar
  47. 47.
    Babu BG, Kannan M (2002) Lightning bugs. Resonance 7(9):49–55CrossRefGoogle Scholar
  48. 48.
    Yang XS, He XS (2019) Nature-inspired algorithms. In: Mathematical foundations of nature-inspired algorithms. Springer briefs in optimization. Springer, ChamGoogle Scholar
  49. 49.
    Yang XS, Hosseini SSS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12:1180–1186CrossRefGoogle Scholar
  50. 50.
    Gandomi AH, Yang XS, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89:2325–2336CrossRefGoogle Scholar
  51. 51.
    Talatahari S, Gandomi AH, Yun GJ (2012) Optimum design of tower structures using firefly algorithm. Struct Des Tall Special 23:350–361CrossRefGoogle Scholar
  52. 52.
    Degertekin SO, Lamberti L (2013) Sizing optimization of truss structures using the firefly algorithm. In Topping BHV, Iványi P (eds) Proceedings of the fourteenth international conference on civil, structural and environmental engineering computing. Civil-Comp Press, Stirlingshire, UKGoogle Scholar
  53. 53.
    Yu S, Yang S, Su S (2013) Self-Adaptive step firefly algorithm. J Appl Math 2013:1–8MathSciNetzbMATHGoogle Scholar
  54. 54.
    Memari A, Ahmad R, Akbari Jokar MR, Abdul Rahim AR (2019) A new modified firefly algorithm for optimizing a supply chain network problem. Appl Sci 9(1):7:1–13CrossRefGoogle Scholar
  55. 55.
    Liu C, Gao F, Jin N (2014) Design and simulation of a modified firefly algorithm. In: Proceedings of seventh international joint conference on computational sciences and optimization. IEEE, Beijing, ChinaGoogle Scholar
  56. 56.
    Gupta M, Gupta D (2016) A new modified firefly algorithm. Int J Eng Sci 4(2):4006–4011Google Scholar
  57. 57.
    Yelghi A, Kose C (2018) A modified firefly algorithm for global minimum optimization. Appl Soft Comput 62:29–44CrossRefGoogle Scholar
  58. 58.
    Tilahun SL, Ong HC (2012) Modified firefly algorithm. J Appl Math 2012:1–12MathSciNetzbMATHCrossRefGoogle Scholar
  59. 59.
    Fister I, Yang XS, Brest J, Fister I Jr (2012) Modified firefly algorithm using quaternion representation. Exp Syst Appl 40:7220–7230CrossRefGoogle Scholar
  60. 60.
    Kazemzadeh-Parsi MJ (2014) A modified firefly algorithm for engineering design optimization problems. Trans Mech Eng 38(M2):403–421Google Scholar
  61. 61.
    Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA (2016) Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains. J Water Resour Plann Manage 142(9):1–15CrossRefGoogle Scholar
  62. 62.
    Karuvelam S, Rajaram M (2014) Modified firefly algorithm for selective harmonic elimination in single phase matrix converter. Int J Appl Eng Res 9(23):22325–22336Google Scholar
  63. 63.
    Verma OP, Aggarwal D, Patodi T (2016) Opposition and dimensional based modified firefly algorithm. Exp Syst Appl 44:168–176CrossRefGoogle Scholar
  64. 64.
    Celik Y, Kutucu H (2018) Solving the tension/compression spring design problem by an improved firefly algorithm. IDDM 1(2255):1–7Google Scholar
  65. 65.
    Xu H, Yu S, Chen J, Zuo X (2018) An improved firefly algorithm for feature selection in classification. Wireless Pers Commun 102(4):2823CrossRefGoogle Scholar
  66. 66.
    Wang GG, Guo L (2014) A new improved firefly algorithm for global numerical optimization. J Comput Theo Nano 11(2):477–485CrossRefGoogle Scholar
  67. 67.
    Xiang Q (2015) An improved firefly algorithm for numerical optimization. Int J Comput Sci Mat 6(2):201CrossRefGoogle Scholar
  68. 68.
    Wahid F, Ghazali R, Shah H (2018) An improved hybrid firefly algorithm for solving optimization problems. In: Ghazali R, Deris M, Nawi N, Abawajy J (eds) Recent advances on soft computing and data mining. SCDM 2018. Advance International System Computing, vol 700. Springer, ChamGoogle Scholar
  69. 69.
    Baykasoglu A, Ozsoydan FB (2014) An improved firefly algorithm for solving dynamic multidimensional knapsack problems. Exp Syst Appl 41(8):3712–3725CrossRefGoogle Scholar
  70. 70.
    Zhang F, Hui J, Guo Y (2018) An improved firefly algorithm for collaborative manufacturing chain optimization problem. Proc Inst Mech Eng, Part B: J Eng Manuf 233(6):1711–1722 (Sage)CrossRefGoogle Scholar
  71. 71.
    Al-Wagih K (2015) Improved firefly algorithm for unconstrained optimization problems. Int J Comput Appl Tech Res 4(1):77–81Google Scholar
  72. 72.
    Kaur K, Salgotra R, Singh U (2017) An improved firefly algorithm for numerical optimization. In: Proceedings of international conference on innovations in information, embedded and communication systems (ICIIECS), Coimbatore, IndiaGoogle Scholar
  73. 73.
    Nguyen TT, Quynh NV, Le Van Dai LV (2018) Improved firefly algorithm: a novel method for optimal operation of thermal generating units. Complexity 2018:1–23Google Scholar
  74. 74.
    Ranganathan S, Kalavathi MS, Rajan CA (2015) Self-adaptive firefly algorithm based multi-objectives for multi-type FACTS placement. IET Gener Transm Distrib 10(11):2576–2584CrossRefGoogle Scholar
  75. 75.
    Fister I, Yang XS, Brest J, Fister Jr I (2013) Memetic self-adaptive firefly algorithm. In: Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (eds) Swarm intelligence and bio-inspired computation: theory and applications. Elsevier IncGoogle Scholar
  76. 76.
    Baykasoglu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36:152–164CrossRefGoogle Scholar
  77. 77.
    Wang W, Wang H, Zhao J, Lv L (2017) A new adaptive firefly algorithm for solving optimization problems. In: Huang DS, Bevilacqua V, Premaratne P, Gupta P (eds) Intelligent computing theories and application. ICIC 2017. Lecture Notes in Computer Science, vol 10361. Springer, ChamCrossRefGoogle Scholar
  78. 78.
    Selvarasu R, Surya Kalavathi M (2014) Self-adaptive firefly algorithm based transmission loss minimization using multi type FACTS devices. In: Proceedings of international conference on circuit, power and computing technologies [ICCPCT], Tamil Nadu, IndiaGoogle Scholar
  79. 79.
    Cheung NJ, Ding XM, Shen HB (2014) Adaptive firefly algorithm: parameter analysis and its application. PLoS ONE 9(11):e112634CrossRefGoogle Scholar
  80. 80.
    Saka MP, Aydogdu I, Akin A (2012) Discrete design optimization of space steel frames using the adaptive firefly algorithm. In: Proceedings of the eleventh international conference on computational structures technology, Dubrovik, CroatiaGoogle Scholar
  81. 81.
    Yang XS (2009) Firefly algorithms for multimodal optimization, stochastic algorithms: foundations and applications, SAGA, Lecture Notes in Computer Science, vol 5792. Springer, BerlinCrossRefGoogle Scholar
  82. 82.
    Dogan E, Saka MP (2012) Optimum design of unbraced steel frames to LRFD–AISC using particle swarm optimization. Adv Eng Soft 46(1):27–34CrossRefGoogle Scholar
  83. 83.
    Carbas S (2017) Optimum structural design of spatial steel frames via biogeography-based optimization. Neural Comput Appl 28:1525–1539CrossRefGoogle Scholar
  84. 84.
    Aydodu I, Akin A (2014) Teaching and learning-based optimization algorithm for optimum design of steel buildings. Comput Civil Build Eng, 2167–2175Google Scholar
  85. 85.
    Akin A, Aydogdu I (2015) Optimum design of steel space frames by hybrid teaching-learning based optimization and harmony search algorithms. Int J Mech Aerosp Indust Mechatron Manuf Eng 9(7):1367–1374Google Scholar
  86. 86.
    Aydodu I, Akin A, Saka MP (2016) Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Adv Eng Software 92:1–14CrossRefGoogle Scholar
  87. 87.
    Aydogdu I, Carbas S, Akin A (2017) Effect of Levy Flight on the discrete optimum design of steel skeletal structures using metaheuristics. Steel Comp Struct 24(1):93–112CrossRefGoogle Scholar
  88. 88.
    Carbas S, Aydogdu I, Tokdemir T, Saka MP (2014) Design optimization of low-rise cold-formed steel frames with thin-walled sections using the artificial bee colony algorithm. In: Topping BHV, Iványi P (eds) Proceedings of the twelfth international conference on computational structures technology. Civil-Comp Press, Stirlingshire, ScotlandGoogle Scholar
  89. 89.
    ASCE 7-05 (2005) Minimum design loads for buildings and other structures. American Society of Civil Engineers, Reston, VA, USAGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Civil EngineeringKaramanoglu Mehmetbey UniversityKaramanTurkey

Personalised recommendations