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Introduction and Overview: Hybrid Metaheuristics in Structural Engineering—Including Machine Learning Applications

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Hybrid Metaheuristics in Structural Engineering

Abstract

For the first chapter of the book entitled “Hybrid Metaheuristics in Structural Engineering—Including Machine Learning Applications”, an introduction and overview chapter is given. This chapter includes the importance of optimization in civil engineering as an introduction. Then, metaheuristics are defined as a general frame. Then, machine learning is mentioned. Finally, an overview of the content of the book was given.

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References

  1. Galilei, G.: Dialogues Concerning Two New Sciences. Northwestern University Press, Evanston, IL (originally published in 1665) (1950)

    Google Scholar 

  2. Haug, E.J., Kirmser, P.G.: Minimum weight design of beams with inequality constraints on stress and deflection. J. Appl. Mech. 34(4), 999–1004 (1967)

    Article  MATH  Google Scholar 

  3. Venkayya, V.B.: Design of optimum structures. Comput. Struct. 1(1–2), 265–309 (1971)

    Article  Google Scholar 

  4. Friel, L.L.: Optimum singly reinforced concrete sections. J. Proc. 71(11), 556–558 (1974, November)

    Google Scholar 

  5. Chou, T.: Optimum reinforced concrete T-beam sections. J. Struct. Div. 103(ASCE 13120) (1977)

    Google Scholar 

  6. Krishnamoorthy, C.S., Munro, J.: Linear program for optimal design of reinforced concrete frames. Proc. IABSE 3(1), 119–141 (1973)

    Google Scholar 

  7. Kirsch, U.: Multilevel optimal design of reinforced concrete structures. Eng. Optim. 6(4), 207–212 (1983)

    Article  Google Scholar 

  8. Lakshmanan, N., Parameswaran, V.S.: Minimum weight design of reinforced concrete sections for flexure. J. Inst. Eng. India. Civ. Eng. Div. 66(2), 92–98 (1985)

    Google Scholar 

  9. Prakash, A., Agarwala, S.K., Singh, K.K.: Optimum design of reinforced concrete sections. Comput. Struct. 30(4), 1009–1011 (1988)

    Article  Google Scholar 

  10. Hoit, M., Soeiro, A., Fagundo, F.: Probabilistic design and optimization of reinforced concrete frames. Eng. Optim. 17(3), 229–235 (1991)

    Article  Google Scholar 

  11. Chakrabarty, B.K.: Models for optimal design of reinforced concrete beams. Comput. Struct. 42(3), 447–451 (1992)

    Article  Google Scholar 

  12. Al-Salloum, Y.A., Husainsiddiqi, G.: Cost-optimum design of reinforced concrete (RC) beams. Struct. J. 91(6), 647–655 (1994)

    Google Scholar 

  13. Chung, T.T., Sun, T.C.: Weight optimization for flexural reinforced concrete beams with static nonlinear response. Struct. Optim. 8(2–3), 174–180 (1994)

    Article  Google Scholar 

  14. Adamu, A., Karihaloo, B.L., Rozvany, G.I.N.: Minimum cost design of reinforced concrete beams using continuum-type optimality criteria. Struct. Optim. 7(1–2), 91–102 (1994)

    Article  Google Scholar 

  15. Koziel, S., Yang, X.S. (eds.): Computational Optimization, Methods and Algorithms, vol. 356. Springer-Verlag, Heidelberg, Berlin (2011). ISBN: 978-3-642-20858-4

    Google Scholar 

  16. Onwubolu, G.C., Babu, B.V.: New Optimization Techniques in Engineering, vol. 141. Springer-Verlag, Heidelberg, Berlin (2004). ISBN: 978-3-540-39930-8

    Google Scholar 

  17. Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H. (eds.): Metaheuristic Algorithms in Modeling and Optimization, Metaheuristic Applications in Structures and Infrastructures. Elsevier, pp. 1–24 (2013). ISBN: 9780123983640

    Google Scholar 

  18. Bekdaş, G., Nigdeli, S.M., Yücel, M., Kayabekir, A.E.: Yapay Zeka Optimizasyon Algoritmaları ve Mühendislik Uygulamaları. Seçkin, Ankara, Turkey (2021)

    Google Scholar 

  19. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Google Scholar 

  20. Glover, F.: Tabu Search—Part I. ORSA J. Comput. 1(3), 190–206 (1989)

    Google Scholar 

  21. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Google Scholar 

  22. Balas, E., Vazacopoulos, A.: Guided local search with shifting bottleneck for job shop scheduling. Manag. Sci. 44(2), 262–275 (1998)

    Google Scholar 

  23. Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science. Springer US, Boston, MA, pp. 320–353 (2003)

    Google Scholar 

  24. Holland, J.H.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 1st edn. MIT Press, Cambridge, Mass. (1992)

    Google Scholar 

  25. Storn, R.; Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Google Scholar 

  26. de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)

    Article  Google Scholar 

  27. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Google Scholar 

  28. Civicioglu, P.: Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219(15), 8121–8144 (2013)

    Google Scholar 

  29. Salimi, H.: Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75, 1–18 (2015)

    Google Scholar 

  30. Wu, G.: Across neighborhood search for numerical optimization. Information Sciences. Spec. Issue Discov. Sci. 329, 597–618 (2016)

    Google Scholar 

  31. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95—International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

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

    Google Scholar 

  33. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  34. Krishnanand, K.N., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 84–91 (2005)

    Google Scholar 

  35. Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)

    Google Scholar 

  36. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006)

    Google Scholar 

  37. Dai, C., Zhu, Y., Chen, W.: Seeker optimization algorithm. In: Wang, Y., Cheung, Y.-M., Liu, H. (eds.) Computational Intelligence and Security. Lecture Notes in Computer Science, vol. 4456. Springer, Berlin, Heidelberg, pp. 167–176 (2007)

    Google Scholar 

  38. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congr. Evol. Comput. 2007, 4661–4667 (2007)

    Google Scholar 

  39. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Article  Google Scholar 

  40. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) Stochastic Algorithms: Foundations and Applications. Lecture Notes in Computer Science, vol. 5792. Springer, Berlin, Heidelberg, pp. 169–178 (2009)

    Google Scholar 

  41. Hosseini, H.S.: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int. J. Bio-Inspired Comput. 1(1/2), 71 (2009)

    Article  Google Scholar 

  42. Zhao, R.Q., Tang, W.S.: Monkey algorithm for global numerical optimization. J. Uncertain Syst. 2(3), 164–175 (2008)

    Google Scholar 

  43. Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature Biologically Inspired Computing (NaBIC), pp. 210–214 (2009)

    Google Scholar 

  44. He, S., Wu, Q.H., Saunders, J.R.: Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans. Evol. Comput. 13(5), 973–990 (2009)

    Article  Google Scholar 

  45. Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math. Appl. 60(7), 2087–2098 (2010)

    Google Scholar 

  46. Lam, A.Y.S., Li, V.O.K.: Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans. Evol. Comput. 14(3), 381–399 (2010)

    Article  Google Scholar 

  47. Yang, X.-S. A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A.; Cruz, C., Terrazas, G. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol. 284. Springer, Berlin, Heidelberg, pp. 65–74 (2010)

    Google Scholar 

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

    Google Scholar 

  49. Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. 11(8), 5508–5518 (2011)

    Google Scholar 

  50. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput.-Aided Des. 43(3), 303–315 (2011)

    Google Scholar 

  51. Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)

    Google Scholar 

  52. Duman, E., Uysal, M., Alkaya, A.F.: Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf. Sci. 217, 65–77 (2012)

    Google Scholar 

  53. Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. (Including Special Section on New Trends in Ambient Intelligence and Bio-inspired Systems) 222: 175–184 (2013)

    Google Scholar 

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

    Google Scholar 

  55. Li, X., Zhang, J., Yin, M.: Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput. Appl. 24(7), 1867–1877 (2014)

    Google Scholar 

  56. Chandra, V.: Smell detection agent based optimization algorithm. J. Inst. Eng. India Ser. B 97(3), 431–436 (2014)

    Google Scholar 

  57. Ma, L., Hu, K., Zhu, Y., Chen, H., He, M.: A novel plant root foraging algorithm for image segmentation problems. Math. Probl. Eng. 2014, 1–16 (2014)

    Article  Google Scholar 

  58. Meng, X., Liu, Y., Gao, X., Zhang, H.: A new bio-inspired algorithm: chicken swarm optimization. In: Tan, Y., Shi, Y., Coello, C.A. (eds.) Advances in Swarm Intelligence. Lecture Notes in Computer Science, vol. 8794. Springer International Publishing, Cham, pp. 86–94 (2014)

    Google Scholar 

  59. Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) Unconventional Computation and Natural Computation. Lecture Notes in Computer Science, vol. 7445. Springer, Berlin, Heidelberg. pp. 240–249 (2012)

    Google Scholar 

  60. Rahmani, R., Yusof, R.: A new simple, fast and efficient algorithm for global optimization over continuous search-space problems: radial movement optimization. Appl. Math. Comput. 248, 287–300 (2014)

    Google Scholar 

  61. Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014)

    Google Scholar 

  62. Deb, S., Fong, S., Tian, Z.: Elephant search algorithm for optimization problems. In: 2015 Tenth International Conference on Digital Information Management (ICDIM), pp. 249–255 (2015)

    Google Scholar 

  63. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Google Scholar 

  64. Chen, C.-C., Tsai, Y.-C., Liu, I.-I., Lai, C.-C., Yeh, Y.-T., Kuo, S.-Y., Chou, Y.-H.: A novel metaheuristic: jaguar algorithm with learning behavior. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1595–1600 (2015)

    Google Scholar 

  65. Cuevas, E., González, A., Zaldívar, D., Cisneros, M.P.: An optimisation algorithm based on the behaviour of locust swarms. Int. J. Bio-Inspired Comput. 7(6), 402 (2015)

    Article  Google Scholar 

  66. Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)

    Google Scholar 

  67. Doğan, B., Ölmez, T.: A new metaheuristic for numerical function optimization: vortex search algorithm. Inf. Sci. 293, 125–145 (2015)

    Google Scholar 

  68. Zheng, Y.-J.: Water wave optimization: a new nature-inspired metaheuristic. Comput. Oper. Res. 55, 1–11 (2015)

    Google Scholar 

  69. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Google Scholar 

  70. Odili, J.B., Kahar, M.N.M., Anwar, S.: African buffalo optimization: a swarm-intelligence technique. In: Procedia Computer Science. 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IEEE IRIS2015), vol. 76, pp. 443–448 (2015)

    Google Scholar 

  71. Shareef, H., Ibrahim, A.A., Mutlag, A.H.: Lightning search algorithm. Appl. Soft Comput. 36, 315–333 (2015)

    Google Scholar 

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

    Google Scholar 

  73. Abedinpourshotorban, H., Mariyam Shamsuddin, S., Beheshti, Z., Jawawi, D.N.A.: Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol. Comput. 26, 8–22 (2016)

    Google Scholar 

  74. Sun, G., Zhao, R., Lan, Y.: Joint operations algorithm for large-scale global optimization. Appl. Soft Comput. 38, 1025–1039 (2016)

    Google Scholar 

  75. Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3(1), 24–36 (2016)

    Google Scholar 

  76. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Google Scholar 

  77. Li, M.D., Zhao, H., Weng, X.W., Han, T.: A novel nature-inspired algorithm for optimization: virus colony search. Adv. Eng. Softw. 92, 65–88 (2016)

    Google Scholar 

  78. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Google Scholar 

  79. Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., Tavakkoli-Moghaddam, R.: Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput. 24(19), 14637–14665 (2020)

    Google Scholar 

  80. Vinod, C.S.S., Anand, H.S.: Phototropic algorithm for global optimisation problems. Appl. Intell. 51(8), 5965–5977 (2021)

    Google Scholar 

  81. Pierezan, J., Dos Santos Coelho, L.: Coyote optimization algorithm: a new metaheuristic for global optimization problems. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2018)

    Google Scholar 

  82. Jain, M., Maurya, S., Rani, A., Singh, V.: Owl search algorithm: a novel nature-inspired heuristic paradigm for global optimization. In: Thampi, S.M., El-Alfy, E.-S.M., Mitra, S., Trajkovic, L. (eds.). J. Intell. Fuzzy Syst. 34(3), 1573–1582 (2018)

    Google Scholar 

  83. Jain, M., Singh, V., Rani, A.: A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol. Comput. 44, 148–175 (2019)

    Google Scholar 

  84. Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., Tavakkoli-Moghaddam, R.: The social engineering optimizer (SEO). Eng. Appl. Artif. Intell. 72, 267–293 (2018)

    Google Scholar 

  85. Dhiman, G., Kumar, V.: Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl.-Based Syst. 159, 20–50 (2018)

    Google Scholar 

  86. Elsisi, M.: Future search algorithm for optimization. Evol. Intell. 12(1), 21–31 (2019)

    Google Scholar 

  87. Harifi, S., Khalilian, M., Mohammadzadeh, J., Ebrahimnejad, S.: Emperor penguins colony: a new metaheuristic algorithm for optimization. Evol. Intell. 12(2), 211–226 (2019)

    Google Scholar 

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

    Google Scholar 

  89. Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)

    Google Scholar 

  90. Askari, Q., Younas, I., Saeed, M.: Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowl-Based Syst. 195, 105709 (2020)

    Google Scholar 

  91. Askari, Q., Saeed, M., Younas, I.: Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Syst. Appl. 161, 113702 (2020)

    Google Scholar 

  92. Sörensen, K., Sevaux, M., Glover, F.: A history of metaheuristics. In: Handbook of Heuristics, pp. 1–18 (2018)

    Google Scholar 

  93. Abou-Warda, H., Belal, N.A., El-Sonbaty, Y., Darwish, S.: A random forest model for mental disorders diagnostic systems. In: International Conference on Advanced Intelligent Systems and Informatics. Springer, Cham, pp. 670–680 (2016)

    Google Scholar 

  94. Bone, D., Lee, C.C., Chaspari, T., Gibson, J., Narayanan, S.: Signal processing and machine learning for mental health research and clinical applications [perspectives]. IEEE Signal Process. Mag. 34(5), 196–195 (2017)

    Article  Google Scholar 

  95. Srividya, M., Mohanavalli, S., Bhalaji, N.: Behavioral modeling for mental health using machine learning algorithms. J. Med. Syst. 42(5), 1–12 (2018)

    Article  Google Scholar 

  96. Chen, M., Hao, Y., Hwang, K., Wang, L., Wang, L.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5, 8869–8879 (2017)

    Article  Google Scholar 

  97. Sahoo, A.K., Pradhan, C., Das, H.: Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making. In: Nature Inspired Computing for Data Science. Springer, Cham, pp. 201–212 (2020)

    Google Scholar 

  98. Koh, J.E.W., De Michele, S., Sudarshan, V.K., Jahmunah, V., Ciaccio, E.J., Ooi, C.P., Grurajan, R., Grurajan, R., Oh, S.L., Lewis, S.K., Green, P.H., Bhagat, G., Acharya, U.R.: Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach. Comput. Methods Programs Biomed. 203, 106010 (2021)

    Article  Google Scholar 

  99. Sawant, A., Bhandari, M., Yadav, R., Yele, R., Bendale, M.S.: Brain cancer detection from mri: a machine learning approach (tensorflow). Brain 5(04) (2018)

    Google Scholar 

  100. Fujisawa, Y., Inoue, S., Nakamura, Y.: The possibility of deep learning-based, computer-aided skin tumor classifiers. Front. Med. 6, 191 (2019)

    Article  Google Scholar 

  101. Chand, S.: A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method. Mach. Vis. Appl. 31(6), 1–10 (2020)

    Google Scholar 

  102. Shabani, S., Yousefi, P., Naser, G.: Support vector machines in urban water demand forecasting using phase space reconstruction. Procedia Eng. 186, 537–543 (2017)

    Article  Google Scholar 

  103. Lopez Farias, R., Puig, V., Rodriguez Rangel, H., Flores, J.J.: Multi-model prediction for demand forecast in water distribution networks. Energies 11(3), 660 (2018)

    Article  Google Scholar 

  104. Yücel, M., Namli, E.: Yapay zekâ modelleri ile betonarme yapilara ait enerji performans siniflarinin tahmini. Uludağ Univer. J. Fac. Eng. 22(3), 325–346 (2018)

    Article  Google Scholar 

  105. Yang, S., Wan, M.P., Chen, W., Ng, B.F., Dubey, S.: Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Appl. Energy 271, 115147 (2020)

    Article  Google Scholar 

  106. Chen, X.L., Fu, J.P., Yao, J.L., Gan, J.F.: Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model. Eng. Comput. 34(2), 367–383 (2018)

    Article  Google Scholar 

  107. Hoang, N.D., Tran, X.L., Nguyen, H.: Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model. Neural Comput. Appl. 32(11), 7289–7309 (2020)

    Article  Google Scholar 

  108. Yucel, M., Namlı, E.: High performance concrete (HPC) compressive strength prediction with advanced machine learning methods: combinations of machine learning algorithms with bagging, rotation forest, and additive regression. In: Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering. IGI Global, pp. 118–140 (2020)

    Google Scholar 

  109. Yücel, M., Bekdaş, G., Nigdeli, S.M.: Prediction of optimum 3-bar truss model parameters with an ANN model. In: International Conference on Harmony Search Algorithm. Springer, Singapore, pp. 317–324 (2020)

    Google Scholar 

  110. Yücel, M., Nigdeli, S.M., Kayabekir, A.E., Bekdaş, G.: Optimization and artificial neural network models for reinforced concrete members. In: Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications. Springer, Singapore, pp. 181–199 (2021)

    Google Scholar 

  111. Nigdeli, S.M., Yücel, M., Bekdaş, G.: A hybrid artificial intelligence model for design of reinforced concrete columns. Neural Comput. Appl. 35(10), 7867–7875 (2023)

    Article  Google Scholar 

  112. Yucel, M., Bekdaş, G., Nigdeli, S.M., Sevgen, S.: Estimation of optimum tuned mass damper parameters via machine learning. J. Build. Eng. 26, 100847 (2019)

    Article  Google Scholar 

  113. Lara-Valencia, L.A., Farbiarz-Farbiarz, Y., Valencia-González, Y.: Design of a tuned mass damper inerter (TMDI) based on an exhaustive search optimization for structural control of buildings under seismic excitations. Shock Vib. (2020)

    Google Scholar 

  114. Etedali, S., Bijaem, Z.K., Mollayi, N., Babaiyan, V.: Artificial intelligence-based prediction models for optimal design of tuned mass dampers in damped structures subjected to different excitations. Int. J. Struct. Stab. Dyn. 2150120 (2021)

    Google Scholar 

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Bekdaş, G., Nigdeli, S.M. (2023). Introduction and Overview: Hybrid Metaheuristics in Structural Engineering—Including Machine Learning Applications. In: Bekdaş, G., Nigdeli, S.M. (eds) Hybrid Metaheuristics in Structural Engineering. Studies in Systems, Decision and Control, vol 480. Springer, Cham. https://doi.org/10.1007/978-3-031-34728-3_1

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