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The Mosaic of Metaheuristic Algorithms in Structural Optimization

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Abstract

Metaheuristic optimization algorithms (MOAs) represent powerful tools for dealing with multi-modal nonlinear optimization problems. The considerable attention that MOAs have received over the last decade and especially when adopted for dealing with several types of structural optimization problems can be mainly credited to the advances achieved in computer science and computer technology rendering possible, among others, the solution of real-world structural design optimization cases in reasonable computational time. The primal scope of the study is to present a state-of-the-art review of past and current developments achieved so far in structural optimization problems dealt with MOAs, accompanied by a set of tests aiming to examine the efficiency of various MOAs in several benchmark structural optimization problems. For this purpose, 24 population-based state-of-the-art MOAs belonging in four classes, (i) swarm-based; (ii) physics-based; (iii) evolutionary-based; and (iv) human-based, are used for solving 11 single objective benchmark structural optimization test problems of different levels of complexity. The size of the problems employed varies, with the number of unknowns ranging from 3 to 328 and the number of constraint functions ranging from 2 to 264, related to the structural performance of the design with reference to deformation and stress limits.

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References

  1. Dulaimi MF et al (2002) Enhancing integration and innovation in construction. Build Res Inf 30(4):237–247. https://doi.org/10.1080/09613210110115207

    Article  Google Scholar 

  2. Plevris V, Tsiatas G (2018) Computational structural engineering: past achievements and future challenges. Front Built Environ 4(21):1–5. https://doi.org/10.3389/fbuil.2018.00021

    Article  Google Scholar 

  3. Slaughter ES (1998) Models of construction innovation. J Constr Eng Manage 124:226–231. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:3(226)

    Article  Google Scholar 

  4. Sahab MG, Toropov VV, Gandomi AH (2013) A review on traditional and modern structural optimization: problems and techniques. In: Gandomi AH et al (eds) Metaheuristic applications in structures and infrastructures. Elsevier, Oxford, pp 25–47. https://doi.org/10.1016/B978-0-12-398364-0.00002-4

    Chapter  Google Scholar 

  5. Kashani AR et al (2022) Population-based optimization in structural engineering: a review. Artif Intell Rev 55(1):345–452. https://doi.org/10.1007/s10462-021-10036-w

    Article  Google Scholar 

  6. Bekdaş G et al (2019) Optimization in civil engineering and metaheuristic algorithms: a review of state-of-the-art developments. In: Platt GM, Yang X-S, Silva Neto AJ (eds) Computational intelligence, optimization and inverse problems with applications in engineering. Springer, Cham, pp 111–137. https://doi.org/10.1007/978-3-319-96433-1_6

    Chapter  Google Scholar 

  7. Yang X-S, Bekdaş G, Nigdeli SM (2016) Review and applications of metaheuristic algorithms in civil engineering. In: Yang X-S, Bekdaş G, Nigdeli SM (eds) Metaheuristics and optimization in civil engineering. Modeling and optimization in science and technologies. Springer, Berlin. https://doi.org/10.1007/978-3-319-26245-1_1

    Chapter  MATH  Google Scholar 

  8. Lagaros ND (2014) An efficient dynamic load balancing algorithm. Comput Mech 53(1):59–76. https://doi.org/10.1007/s00466-013-0892-1

    Article  MathSciNet  Google Scholar 

  9. International Student Competition in Structural Optimization (2015) (ISCSO 2015). https://www.brightoptimizer.com/problem_iscso2016/. Accessed 25 May 2021

  10. International Student Competition in Structural Optimization (2016) (ISCSO 2016). http://www.brightoptimizer.com/optimization-problem-of-iscso-2016/. Accessed 25 May 2021

  11. International Student Competition in Structural Optimization (2017) (ISCSO 2017). https://www.brightoptimizer.com/problem_iscso2017/. Accessed 25 May 2021

  12. International Student Competition in Structural Optimization (2018) (ISCSO 2018). https://www.brightoptimizer.com/problem_iscso2018/. Accessed 25 May 2021

  13. International Student Competition in Structural Optimization (2019) (ISCSO 2019). https://www.brightoptimizer.com/problem-iscso2019/. Accessed 25 May 2021

  14. Kaveh A (2021) Advances in metaheuristic algorithms for optimal design of structures, 3rd edn. Springer, Cham

    Book  MATH  Google Scholar 

  15. Brockett RW (1991) Dynamical systems that sort lists, diagonalize matrices, and solve linear programming problems. Linear Algebra Appl 146:79–91. https://doi.org/10.1016/0024-3795(91)90021-N

    Article  MathSciNet  MATH  Google Scholar 

  16. Lyamin AV, Sloan SW (2002) Lower bound limit analysis using non-linear programming. Int J Numer Meth Eng 55(5):573–611. https://doi.org/10.1002/nme.511

    Article  MATH  Google Scholar 

  17. Yokota T, Gen M, Li Y-X (1996) Genetic algorithm for non-linear mixed integer programming problems and its applications. Comput Ind Eng 30(4):905–917. https://doi.org/10.1016/0360-8352(96)00041-1

    Article  Google Scholar 

  18. Dadebo SA, McAuley KB (1995) Dynamic optimization of constrained chemical engineering problems using dynamic programming. Comput Chem Eng 19(5):513–525. https://doi.org/10.1016/0098-1354(94)00086-4

    Article  Google Scholar 

  19. Wang F-S, Chen L-H (2013) Heuristic Optimization. In: Dubitzky W et al (eds) Encyclopedia of systems biology. Springer, New York, NY, pp 885–885. https://doi.org/10.1007/978-1-4419-9863-7_411

    Chapter  Google Scholar 

  20. Sörensen K, Glover FW (2013) Metaheuristics. In: Gass SI, Fu MC (eds) Encyclopedia of operations research and management science. Springer, Boston, MA, pp 960–970. https://doi.org/10.1007/978-1-4419-1153-7_1167

    Chapter  Google Scholar 

  21. Glover F, Samorani M (2019) Intensification, diversification and learning in metaheuristic optimization. J Heuristics 25(4):517–520. https://doi.org/10.1007/s10732-019-09409-w

    Article  Google Scholar 

  22. Meraihi Y et al (2021) Grasshopper optimization algorithm: theory, variants, and applications. IEEE Access 9:50001–50024. https://doi.org/10.1109/ACCESS.2021.3067597

    Article  Google Scholar 

  23. Yang X, Suash D (2009) Cuckoo Search via Lévy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC)

  24. Yang X-S, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343. https://doi.org/10.1504/IJMMNO.2010.03543

    Article  MATH  Google Scholar 

  25. Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35. https://doi.org/10.1007/s00366-011-0241-y

    Article  Google Scholar 

  26. Yang X-S, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624. https://doi.org/10.1016/j.cor.2011.09.026

    Article  MathSciNet  MATH  Google Scholar 

  27. Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112. https://doi.org/10.1016/j.compstruc.2014.03.007

    Article  Google Scholar 

  28. Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: González JR et al (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin. https://doi.org/10.1007/978-3-642-12538-6_6

    Chapter  Google Scholar 

  29. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483. https://doi.org/10.1108/02644401211235834

    Article  Google Scholar 

  30. Shadravan S, Naji HR, Bardsiri VK (2019) The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34. https://doi.org/10.1016/j.engappai.2019.01.001

    Article  Google Scholar 

  31. Heidari AA et al (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  32. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001

    Article  Google Scholar 

  33. Eskandar H et al (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166. https://doi.org/10.1016/j.compstruc.2012.07.010

    Article  Google Scholar 

  34. Farshi B, Alinia-ziazi A (2010) Sizing optimization of truss structures by method of centers and force formulation. Int J Solids Struct 47(18):2508–2524. https://doi.org/10.1016/j.ijsolstr.2010.05.009

    Article  MATH  Google Scholar 

  35. Kociecki M, Adeli H (2013) Two-phase genetic algorithm for size optimization of free-form steel space-frame roof structures. J Constr Steel Res 90:283–296. https://doi.org/10.1016/j.jcsr.2013.07.027

    Article  Google Scholar 

  36. Hasançebi O et al (2009) Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Comput Struct 87(5):284–302. https://doi.org/10.1016/j.compstruc.2009.01.002

    Article  Google Scholar 

  37. Kaveh A et al (2010) Performance-based seismic design of steel frames using ant colony optimization. J Constr Steel Res 66(4):566–574. https://doi.org/10.1016/j.jcsr.2009.11.006

    Article  Google Scholar 

  38. Moayyeri N, Gharehbaghi S, Plevris V (2019) Cost-based optimum design of reinforced concrete retaining walls considering different methods of bearing capacity computation. Mathematics 7(12):1–21. https://doi.org/10.3844/jcssp.2018.1351.1362

    Article  Google Scholar 

  39. Gholizadeh S, Milany A (2018) An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures. Eng Optim 50(11):1829–1849. https://doi.org/10.1080/0305215X.2017.1417402

    Article  MathSciNet  Google Scholar 

  40. Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: Tan Y, Shi Y, Tan KC (eds) Advances in swarm intelligence. ICSI 2010. Lecture notes in computer science. Springer, Berlin. https://doi.org/10.1007/978-3-642-13495-1_44

    Chapter  Google Scholar 

  41. Bureerat S, Pholdee N (2016) Optimal truss sizing using an adaptive differential evolution algorithm. J Comput Civ Eng 30(2):04015019. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000487

    Article  Google Scholar 

  42. Hasançebi O, Azad SK (2012) An exponential big bang-big crunch algorithm for discrete design optimization of steel frames. Comput Struct 110–111:167–179. https://doi.org/10.1016/j.compstruc.2012.07.014

    Article  Google Scholar 

  43. Lagaros ND et al (2008) Optimum design of steel structures with web openings. Eng Struct 30(9):2528–2537

    Article  Google Scholar 

  44. Papadrakakis M, Lagaros ND, Plevris V (2001) Optimum design of space frames under seismic loading. Int J Struct Stab Dyn 1(1):105–123. https://doi.org/10.1142/S0219455401000093

    Article  Google Scholar 

  45. Papazafeiropoulos G, Plevris V (2018) OpenSeismoMatlab: a new open-source software for strong ground motion data processing. Heliyon 4(9):1–39. https://doi.org/10.1016/j.heliyon.2018.e00784

    Article  Google Scholar 

  46. Fragiadakis M, Lagaros ND, Papadrakakis M (2006) Performance-based multiobjective optimum design of steel structures considering life-cycle cost. Struct Multidiscip Optim 32(1):1–11

    Article  Google Scholar 

  47. Mitropoulou CC, Lagaros ND, Papadrakakis M (2011) Life-cycle cost assessment of optimally designed reinforced concrete buildings under seismic actions. Reliab Eng Syst Saf 96(10):1311–1331. https://doi.org/10.1016/j.ress.2011.04.002

    Article  Google Scholar 

  48. Kociecki M, Adeli H (2014) Two-phase genetic algorithm for topology optimization of free-form steel space-frame roof structures with complex curvatures. Eng Appl Artif Intell 32:218–227. https://doi.org/10.1016/j.engappai.2014.01.010

    Article  Google Scholar 

  49. Kociecki M, Adeli H (2015) Shape optimization of free-form steel space-frame roof structures with complex geometries using evolutionary computing. Eng Appl Artif Intell 38:168–182. https://doi.org/10.1016/j.engappai.2014.10.012

    Article  Google Scholar 

  50. Amir O (2013) A topology optimization procedure for reinforced concrete structures. Comput Struct 114:46–58

    Article  Google Scholar 

  51. Lagaros ND, Papadrakakis M, Bakas N (2006) Automatic minimization of the rigidity eccentricity of 3D reinforced concrete buildings. J Earthq Eng 10(4):533–564

    Article  Google Scholar 

  52. Zakian P, Kaveh A (2020) Topology optimization of shear wall structures under seismic loading. Earthq Eng Eng Vib 19(1):105–116. https://doi.org/10.1007/s11803-020-0550-5

    Article  Google Scholar 

  53. Kaveh A, Kalatjari V (2003) Topology optimization of trusses using genetic algorithm, force method and graph theory. Int J Numer Meth Eng 58(5):771–791. https://doi.org/10.1002/nme.800

    Article  MATH  Google Scholar 

  54. Tian X et al (2019) Topology optimization design for offshore platform jacket structure. Appl Ocean Res 84:38–50. https://doi.org/10.1016/j.apor.2019.01.003

    Article  Google Scholar 

  55. de Souza RR et al (2016) A procedure for the size, shape and topology optimization of transmission line tower structures. Eng Struct 111:162–184

    Article  Google Scholar 

  56. Jiang B, Zhang J, Ohsaki M (2021) Shape optimization of free-form shell structures combining static and dynamic behaviors. Structures 29:1791–1807. https://doi.org/10.1016/j.istruc.2020.12.045

    Article  Google Scholar 

  57. Papadrakakis M, Tsompanakis Y, Lagaros ND (1999) Structural shape optimization using evolution strategies. Eng Optim 31(4):515–540

    Article  MATH  Google Scholar 

  58. Lagaros ND, Fragiadakis M, Papadrakakis M (2004) Optimum design of shell structures with stiffening beams. AIAA J 42(1):175–184

    Article  Google Scholar 

  59. Belevičius R et al (2017) Optimization of rigidly supported guyed masts. Adv Civ Eng. https://doi.org/10.1155/2017/4561376

    Article  Google Scholar 

  60. Mam K et al (2020) Shape optimization of braced frames for tall timber buildings: influence of semi-rigid connections on design and optimization process. Eng Struct 216:110692. https://doi.org/10.1016/j.engstruct.2020.110692

    Article  Google Scholar 

  61. Pastore T et al (2019) Topology optimization of stress-constrained structural elements using risk-factor approach. Comput Struct 224:106104. https://doi.org/10.1016/j.compstruc.2019.106104

    Article  Google Scholar 

  62. Frangedaki E, Sardone L, Lagaros ND (2021) Design optimization of tree-shaped structural systems and sustainable architecture using bamboo and earthen materials. J Archit Eng 27(4):04021033. https://doi.org/10.1061/(ASCE)AE.1943-5568.0000492

    Article  Google Scholar 

  63. Plevris V, Papadrakakis M (2011) A hybrid particle swarm—gradient algorithm for global structural optimization. Comput-Aided Civ Infrastruct Eng 26(1):48–68. https://doi.org/10.1111/j.1467-8667.2010.00664.x

    Article  Google Scholar 

  64. Plevris V (2009) Innovative computational techniques for the optimum structural design considering uncertainties. National Technical University of Athens, Athens, p 312

    Google Scholar 

  65. Kennedy J, Eberhart R (1995) Particle swarm optimization. In IEEE International Conference on Neural Networks, Piscataway, NJ, pp 1942–1948

  66. Aydilek İB (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput 66:232–249. https://doi.org/10.1016/j.asoc.2018.02.025

    Article  Google Scholar 

  67. Yang X-S (2008) Nature-inspired metaheuristic algorithms. Luniver Press, ISBN: 1905986106

  68. Gholizadeh S, Salajegheh E, Torkzadeh P (2008) Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network. J Sound Vib 312(1):316–331. https://doi.org/10.1016/j.jsv.2007.10.050

    Article  Google Scholar 

  69. Nguyen T-H, Vu A-T (2021) Speeding up composite differential evolution for structural optimization using neural networks. J Inf Telecommun. https://doi.org/10.1080/24751839.2021.1946740

    Article  Google Scholar 

  70. Papadrakakis M, Lagaros ND, Tsompanakis Y (1998) Structural optimization using evolution strategies and neural networks. Comput Methods Appl Mech Eng 156(1–4):309–333

    Article  MATH  Google Scholar 

  71. Papadrakakis M, Lagaros ND (2002) Reliability-based structural optimization using neural networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 191(32):3491–3507

    Article  MATH  Google Scholar 

  72. Lagaros ND, Charmpis DC, Papadrakakis M (2005) An adaptive neural network strategy for improving the computational performance of evolutionary structural optimization. Comput Methods Appl Mech Eng 194(30–33):3374–3393

    Article  MATH  Google Scholar 

  73. Lagaros ND, Papadrakakis M (2012) Applied soft computing for optimum design of structures. Struct Multidiscip Optim 45(6):787–799. https://doi.org/10.1007/s00158-011-0741-9

    Article  MATH  Google Scholar 

  74. Lagaros ND, Papadrakakis M (2004) Learning improvement of neural networks used in structural optimization. Adv Eng Softw 35(1):9–25

    Article  Google Scholar 

  75. Liao TW (2010) Two hybrid differential evolution algorithms for engineering design optimization. Appl Soft Comput 10(4):1188–1199. https://doi.org/10.1016/j.asoc.2010.05.007

    Article  Google Scholar 

  76. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359. https://doi.org/10.1023/a:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  77. Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. J Global Optim

  78. Kaveh A, Bakhshpoori T, Afshari E (2014) An efficient hybrid particle swarm and swallow swarm optimization algorithm. Comput Struct 143:40–59. https://doi.org/10.1016/j.compstruc.2014.07.012

    Article  Google Scholar 

  79. Carbas S (2016) Design optimization of steel frames using an enhanced firefly algorithm. Eng Optim 48(12):2007–2025. https://doi.org/10.1080/0305215X.2016.1145217

    Article  Google Scholar 

  80. Talatahari S et al (2015) Optimum design of frame structures using the eagle strategy with differential evolution. Eng Struct 91:16–25. https://doi.org/10.1016/j.engstruct.2015.02.026

    Article  Google Scholar 

  81. Yang X-S, Deb S (2010) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In: González JR et al (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin. https://doi.org/10.1007/978-3-642-12538-6_9

    Chapter  Google Scholar 

  82. Khalilpourazari S, Khalilpourazary S (2019) An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput 23(5):1699–1722. https://doi.org/10.1007/s00500-017-2894-y

    Article  Google Scholar 

  83. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  84. Lagaros ND (2018) The environmental and economic impact of structural optimization. Struct Multidiscip Optim 58(4):1751–1768. https://doi.org/10.1007/s00158-018-1998-z

    Article  Google Scholar 

  85. Mavrokapnidis D, Mitropoulou CC, Lagaros ND (2019) Environmental assessment of cost optimized structural systems in tall buildings. J Build Eng 24:100730. https://doi.org/10.1016/j.jobe.2019.100730

    Article  Google Scholar 

  86. Papadrakakis M et al (1998) Advanced solution methods in structural optimization based on evolution strategies. Eng Comput 15(1):12–34

    Article  MATH  Google Scholar 

  87. Papadrakakis M, Lagaros ND, Fragakis Y (2003) Parallel computational strategies for structural optimization. Int J Numer Meth Eng 58(9):1347–1380

    Article  MATH  Google Scholar 

  88. Lagaros ND (2014) A general purpose real-world structural design optimization computing platform. Struct Multidiscip Optim 49(6):1047–1066. https://doi.org/10.1007/s00158-013-1027-1

    Article  Google Scholar 

  89. Lagaros ND, Karlaftis MG (2016) Life-cycle cost structural design optimization of steel wind towers. Comput Struct 174:122–132. https://doi.org/10.1016/j.compstruc.2015.09.013

    Article  Google Scholar 

  90. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  91. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  92. Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917. https://doi.org/10.1016/j.eswa.2020.113917

    Article  Google Scholar 

  93. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  94. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010

    Article  Google Scholar 

  95. Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195. https://doi.org/10.1162/106365601750190398

    Article  Google Scholar 

  96. Nadimi-Shahraki MH et al (2020) MTDE: an effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems. Appl Soft Comput 97:106761. https://doi.org/10.1016/j.asoc.2020.106761

    Article  Google Scholar 

  97. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073. https://doi.org/10.1007/s00521-015-1920-1

    Article  MathSciNet  Google Scholar 

  98. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  99. Mishra P, Goyal V, Shukla A (2020) An improved grasshopper optimization algorithm for solving numerical optimization problems. In: Mohanty MN, Das S (eds) Advances in intelligent computing and communication. Springer, Singapore

    Google Scholar 

  100. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

  101. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  102. Mirjalili S et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  103. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE Congress on Evolutionary Computation

  104. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76(2):60–68. https://doi.org/10.1177/003754970107600201

    Article  Google Scholar 

  105. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015

    Article  Google Scholar 

  106. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010

    Article  MathSciNet  MATH  Google Scholar 

  107. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018

    Article  Google Scholar 

  108. Kallioras NA, Lagaros ND, Avtzis DN (2018) Pity beetle algorithm—a new metaheuristic inspired by the behavior of bark beetles. Adv Eng Softw 121:147–166. https://doi.org/10.1016/j.advengsoft.2018.04.007

    Article  Google Scholar 

  109. Li S et al (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  110. Abualigah L et al (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https://doi.org/10.1016/j.cma.2020.113609

    Article  MathSciNet  MATH  Google Scholar 

  111. Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications. Springer, Berlin

    Google Scholar 

  112. Yang X-S (2014) Chapter 8—firefly algorithms. In: Yang X-S (ed) Nature-inspired optimization algorithms. Elsevier, Oxford, pp 111–127. https://doi.org/10.1016/B978-0-12-416743-8.00008-7

    Chapter  MATH  Google Scholar 

  113. Georgioudakis M, Plevris V (2020) A comparative study of differential evolution variants in constrained structural optimization. Front Built Environ 6(102):1–14. https://doi.org/10.3389/fbuil.2020.00102

    Article  Google Scholar 

  114. Georgioudakis M, Plevris V (2020) On the performance of differential evolution variants in constrained structural optimization. Procedia Manuf 44:371–378. https://doi.org/10.1016/j.promfg.2020.02.281

    Article  Google Scholar 

  115. Georgioudakis M, Plevris V (2018) A combined modal correlation criterion for structural damage identification with noisy modal data. Adv Civ Eng 2018(3183067):20. https://doi.org/10.1155/2018/3183067

    Article  Google Scholar 

  116. Tuo S, Geem ZW, Yoon JH (2020) A new method for analyzing the performance of the harmony search algorithm. Mathematics 8(9):1421

    Article  Google Scholar 

  117. Ocak A et al (2022) Optimization of tuned liquid damper including different liquids for lateral displacement control of single and multi-story structures. Buildings 12(3):377

    Article  Google Scholar 

  118. Tsipianitis A, Tsompanakis Y (2020) Improved Cuckoo Search algorithmic variants for constrained nonlinear optimization. Adv Eng Softw 149:102865. https://doi.org/10.1016/j.advengsoft.2020.102865

    Article  Google Scholar 

  119. Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):405–411. https://doi.org/10.1115/1.2919393

    Article  Google Scholar 

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Acknowledgements

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1EDK-05603).

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Authors and Affiliations

  1. Institute of Structural Analysis & Antiseismic Research, School of Civil Engineering, National Technical University of Athens, 9, Heroon Polytechniou Str., Zografou Campus, 15780, Athens, Greece

    Nikos D. Lagaros & Nikos Ath. Kallioras

  2. Department of Civil and Architectural Engineering, College of Engineering, Qatar University, P.O. Box: 2713, Doha, Qatar

    Vagelis Plevris

  3. Infersence, 1, Georgiou Mpakou Str., 11524, Athens, Greece

    Nikos Ath. Kallioras

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  1. Nikos D. Lagaros
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  2. Vagelis Plevris
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  3. Nikos Ath. Kallioras
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Correspondence to Nikos D. Lagaros.

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Lagaros, N.D., Plevris, V. & Kallioras, N.A. The Mosaic of Metaheuristic Algorithms in Structural Optimization. Arch Computat Methods Eng 29, 5457–5492 (2022). https://doi.org/10.1007/s11831-022-09773-0

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