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The Development of Hybrid Metaheuristics in Structural Engineering

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Hybrid Metaheuristics in Structural Engineering

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 480))

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Abstract

In engineering designs, the variables in the problems are needed to define by checking several constraints. In that case, the problem is a nonlinear one that needs several iterations when the best suitable solution is wanted. To find the best solution, several algorithms may be employed to iteratively search for the optimum solution. These algorithms are inspired by happening or processes to provide different formulations. As the current trend, multiple algorithms are combined to update efficient features instead of using a metaphor. In this chapter, a review is presented for hybrid metaheuristics in structural engineering applications.

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References

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

    Google Scholar 

  2. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  3. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan (1975)

    Google Scholar 

  4. Goldberg, D.E., Samtani, M.P.: Engineering optimization via genetic algorithm. In: Proceedings of Ninth Conference on Electronic Computation. ASCE, New York, NY, pp. 471–482 (1986)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  6. Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., Alizadeh, Y.: Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput. Methods Appl. Mech. Eng. 197(33–40), 3080–3091 (2008)

    Article  MATH  Google Scholar 

  7. Kaveh, A., Talatahari, S.: Hybrid algorithm of harmony search, particle swarm and ant colony for structural design optimization. In: Harmony Search Algorithms for Structural Design Optimization, 159–198 (2009)

    Google Scholar 

  8. Chiou, J.S., Liu, M.T.: Numerical simulation for fuzzy-PID controllers and helping EP reproduction with PSO hybrid algorithm. Simul. Model. Pract. Theory 17(10), 1555–1565 (2009)

    Article  Google Scholar 

  9. Sandesh, S., Shankar, K.: Application of a hybrid of particle swarm and genetic algorithm for structural damage detection. Inverse Probl. Sci. Eng. 18(7), 997–1021 (2010)

    Article  MATH  Google Scholar 

  10. Seyedpoor, S., Gholizadeh, S., Talebian, S.: An efficient structural optimisation algorithm using a hybrid version of particle swarm optimisation with simultaneous perturbation stochastic approximation. Civ. Eng. Environ. Syst. 27(4), 295–313 (2010)

    Article  Google Scholar 

  11. Plevris, V., Papadrakakis, M.: A hybrid particle swarm—gradient algorithm for global structural optimization. Comput.-Aided Civ. Infrastruct. Eng. 26(1), 48–68 (2011)

    Google Scholar 

  12. Rahami, H., Kaveh, A., Aslani, M., Asl, R.N.: A hybrid modified genetic-Nelder Mead simplex algorithm for large-scale truss optimization. Int. J. Optim. Civ. Eng. 1(1), 29–46 (2011)

    Google Scholar 

  13. Hadidi, A., Kaveh, A., Farahmand Azar, B., Talatahari, S., Farahmandpour, C.: An efficient hybrid algorithm based on particle swarm and simulated annealing for optimal design of space trusses. Int. J. Optim. Civil. Eng. 1(3), 377–395 (2011)

    Google Scholar 

  14. Lee, T.Y., Chen, P.C., Juang, D.S.: Sliding mode control on isolated bridges with columns of irregular heights using pole assignment and PSO-SA hybrid algorithm. Int. J. Struct. Stab. Dyn. 12(03), 1250014 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Kaveh, A., Talatahari, S.: A hybrid CSS and PSO algorithm for optimal design of structures. Struct. Eng. Mech. 42(6), 783–797 (2012)

    Article  Google Scholar 

  16. Rachid, E., Rajae, A.: New hybrid algorithm for multi-objective structural optimization. In: Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM). IEEE, pp. 1–5 (2013)

    Google Scholar 

  17. Pholdee, N., Bureerat, S.: Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses. Inf. Sci. 223, 136–152 (2013)

    Article  MathSciNet  Google Scholar 

  18. Shojaee, S., Arjomand, M., Khatibinia, M.: A hybrid algorithm for sizing and layout optimization of truss structures combining discrete PSO and convex approximation. Int. J. Optim. Civ. Eng. 3(1), 57–83 (2013)

    Google Scholar 

  19. Kaveh, A., Nasr, E.A.: Engineering design optimization using a hybrid PSO and HS algorithm (2013)

    Google Scholar 

  20. Gholizadeh, S.: Layout optimization of truss structures by hybridizing cellular automata and particle swarm optimization. Comput. Struct. 125, 86–99 (2013)

    Article  Google Scholar 

  21. Asl, R.N., Aslani, M., Panahi, M.S.: Sizing optimization of truss structures using a hybridized genetic algorithm (2013). arXiv preprint arXiv:1306.1454

  22. Amini, F., Ghaderi, P.: Hybridization of harmony search and ant colony optimization for optimal locating of structural dampers. Appl. Soft Comput. 13(5), 2272–2280 (2013)

    Article  Google Scholar 

  23. Khajehzadeh, M., Taha, M.R., Eslami, M.: A new hybrid firefly algorithm for foundation optimization. Natl. Acad. Sci. Lett. 36, 279–288 (2013)

    Article  MathSciNet  Google Scholar 

  24. García-Segura, T., Yepes, V., Martí, J.V., Alcalá, J.: Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm. Lat. Am. J. Solids Struct. 11, 1190–1205 (2014)

    Article  Google Scholar 

  25. Long, W., Zhang, W.Z., Huang, Y.F., Chen, Y.X.: A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization. J. Cent. South Univ. 21(8), 3197–3204 (2014)

    Article  Google Scholar 

  26. Talatahari, S., Hosseini, A., Mirghaderi, S.R., Rezazadeh, F.: Optimum performance-based seismic design using a hybrid optimization algorithm. Math. Probl. Eng. 2014, 1–8 (2014)

    Google Scholar 

  27. Liu, Y., Lu, N.W., Wang, Q.Y.: Reliability assessment of longspan cable-stayed bridges based on hybrid algorithm. J. Highw. Transp. Res. Dev. 31(7), 72–79 (2014)

    Google Scholar 

  28. Khajehzadeh, M., Taha, M.R., Eslami, M.: Multi-objective optimisation of retaining walls using hybrid adaptive gravitational search algorithm. Civ. Eng. Environ. Syst. 31(3), 229–242 (2014)

    Article  Google Scholar 

  29. Gharehbaghi, S., Khatibinia, M.: Optimal seismic design of reinforced concrete structures under time-history earthquake loads using an intelligent hybrid algorithm. Earthq. Eng. Eng. Vib. 14, 97–109 (2015)

    Article  Google Scholar 

  30. Hadidi, A., Rafiee, A.: A new hybrid algorithm for simultaneous size and semi-rigid connection type optimization of steel frames. Int. J. Steel Struct. 15, 89–102 (2015)

    Article  Google Scholar 

  31. Akın, A., Aydoğdu, İ: Optimum design of steel space frames by hybrid teaching-learning based optimization and harmony search algorithms. Int. J. Struct. Constr. Eng. 9(7), 1367–1374 (2015)

    Google Scholar 

  32. Babaei, M., Sanaei, E.: Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization. Front. Struct. Civ. Eng. 10, 472–480 (2016)

    Article  Google Scholar 

  33. Khajehzadeh, M.: A new hybrid algorithm for CO2 emissions optimization of retaining walls. Int. J. Adv. Mech. Civ. Eng 3(1), 7–11 (2016)

    Google Scholar 

  34. Sheikholeslami, R., Khalili, B.G., Sadollah, A., Kim, J.: Optimization of reinforced concrete retaining walls via hybrid firefly algorithm with upper bound strategy. KSCE J. Civ. Eng. 20, 2428–2438 (2016)

    Article  Google Scholar 

  35. Kaveh, A., Shokohi, F.: A hybrid optimization algorithm for the optimal design of laterally-supported castellated beams. Sci. Iran. 23(2), 508–519 (2016)

    Google Scholar 

  36. Maheri, M.R., Askarian, M., Shojaee, S.: Size and topology optimization of trusses using hybrid genetic-particle swarm algorithms. Iran. J. Sci. Technol., Trans. Civ. Eng. 40, 179–193 (2016)

    Article  Google Scholar 

  37. Cheng, M.Y., Prayogo, D., Wu, Y.W., Lukito, M.M.: A hybrid harmony search algorithm for discrete sizing optimization of truss structure. Autom. Constr. 69, 21–33 (2016)

    Article  Google Scholar 

  38. Assimi, H., Jamali, A., Nariman Zadeh, N.: Sizing and topology optimization of spatial truss structures using hybrid algorithm of genetic programing and Nelder–Mead. Modares Mech. Eng. 17(6), 32–40 (2017)

    Google Scholar 

  39. Hoseini Vaez, S.R., Fallah, N.: Damage detection of thin plates using GA-PSO algorithm based on modal data. Arab. J. Sci. Eng. 42, 1251–1263 (2017)

    Article  Google Scholar 

  40. Chutani, S., Singh, J.: Optimal design of RC frames using a modified hybrid PSOGSA algorithm. Arch. Civ. Eng. 63(4), 123–134 (2017)

    Article  Google Scholar 

  41. Jiale, T.: Optimal allocation of structural sensor in civil engineering based on simulated annealing genetic algorithm

    Google Scholar 

  42. Assimi, H., Jamali, A.: A hybrid algorithm coupling genetic programming and Nelder–Mead for topology and size optimization of trusses with static and dynamic constraints. Expert Syst. Appl. 95, 127–141 (2018)

    Article  Google Scholar 

  43. Kaveh, A., Ilchi Ghazaan, M.: A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures. Eng. Optim. 50(2), 235–252 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  44. Jiang, Z., Lin, Q., Shi, K., Pan, W.: A novel PGSA-PSO hybrid algorithm for structural optimization. Eng. Comput. 37(1), 144–160 (2019)

    Article  Google Scholar 

  45. Huang, M., Cheng, S., Zhang, H., Gul, M., Lu, H.: Structural damage identification under temperature variations based on PSO-CS hybrid algorithm. Int. J. Struct. Stab. Dyn. 19(11), 1950139 (2019)

    Article  Google Scholar 

  46. Bekdaş, G., Kayabekir, A.E., Nigdeli, S.M., Toklu, Y.C.: Advanced energy based analyses of trusses employing hybrid metaheuristics. Struct. Des. Tall Spec. Build. 28(9), 1–19, e1609 (2019)

    Google Scholar 

  47. Jafari, M., Salajegheh, E., Salajegheh, J.: An efficient hybrid of elephant herding optimization and cultural algorithm for optimal design of trusses. Eng. Comput. 35, 781–801 (2019)

    Article  Google Scholar 

  48. Jafari, M., Salajegheh, E., Salajegheh, J.: Optimal design of truss structures using a hybrid method based on particle swarm optimizer and cultural algorithm. In: Structures, vol. 32. Elsevier, pp. 391–405 (2021)

    Google Scholar 

  49. Shayegan, D.S., Lork, A., Hashemi, S.A.H.: A new hybrid algorithm for cost optimization of waffle slab. Slovak J. Civ. Eng. 28(3), 40–46 (2020)

    Article  Google Scholar 

  50. Han, X., Yue, L., Dong, Y., Xu, Q., Xie, G., Xu, X.: Efficient hybrid algorithm based on moth search and fireworks algorithm for solving numerical and constrained engineering optimization problems. J. Supercomput. 76, 9404–9429 (2020)

    Article  Google Scholar 

  51. García, J., Yepes, V., Martí, J.V.: A hybrid k-means cuckoo search algorithm applied to the counterfort retaining walls problem. Mathematics 8(4), 555 (2020)

    Article  Google Scholar 

  52. Chen, C., Yu, L.: A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization. Adv. Struct. Eng. 23(3), 468–484 (2020)

    Article  Google Scholar 

  53. Kaveh, A., Talatahari, S., Khodadadi, N.: Hybrid invasive weed optimization-shuffled frog-leaping algorithm for optimal design of truss structures. Iran. J. Sci. Technol., Trans. Civ. Eng. 44, 405–420 (2020)

    Article  Google Scholar 

  54. Talatahari, S., Goodarzimehr, V.: A discrete hybrid teaching-learning-based optimization algorithm for optimization of space trusses. J. Struct. Eng. Geo-Tech. 10(1), 55–72 (2020)

    Google Scholar 

  55. Talatahari, S., Goodarzimehr, V., Taghizadieh, N.: Hybrid teaching-learning-based optimization and harmony search for optimum design of space trusses. J. Optim. Ind. Eng. 13(1), 177–194 (2020)

    Google Scholar 

  56. Omidinasab, F., Goodarzimehr, V.: A hybrid particle swarm optimization and genetic algorithm for truss structures with discrete variables. J. Appl. Comput. Mech. 6(3), 593–604 (2020)

    Google Scholar 

  57. Gebremedhen, H.S., Woldemichael, D.E., Hashim, F.M.: A firefly algorithm based hybrid method for structural topology optimization. Adv. Model. Simul. Eng. Sci. 7, 1–20 (2020)

    Article  Google Scholar 

  58. Kayabekir, A.E., Toklu, Y.C., Bekdaş, G., Nigdeli, S.M., Yücel, M., Geem, Z.W.: A novel hybrid harmony search approach for analysis of plane stress systems via total potential optimization. Appl. Sci. 10(7), 2301 (2020)

    Article  Google Scholar 

  59. Toklu, Y.C., Bekdaş, G., Kayabekir, A.E., Nigdeli, S.M., Yücel, M.: Total potential optimization using hybrid metaheuristics: a tunnel problem solved via plane stress members. In: Nigdeli, et al. (eds.) Advances in Structural Engineering—Optimization Emerging Trends in Structural Optimization. Springer, pp. 181–198 (2020)

    Google Scholar 

  60. Yücel, M., Kayabekir, A.E., Bekdaş, G., Nigdeli, S.M., Kim, S., Geem, Z.W.: Adaptive-hybrid harmony search algorithm for multi-constrained optimum eco-design of reinforced concrete retaining walls. Sustainability 13(4), 1639 (2021)

    Article  Google Scholar 

  61. Kaveh, A., Rahmani, P., Eslamlou, A.D.: An efficient hybrid approach based on Harris Hawks optimization and imperialist competitive algorithm for structural optimization. Eng. Comput. 1–29 (2021)

    Google Scholar 

  62. Kundu, T., Garg, H.: A hybrid ITLHHO algorithm for numerical and engineering optimization problems. Int. J. Intell. Syst. 37(7), 3900–3980 (2022)

    Article  Google Scholar 

  63. Barkhordari, M.S., Feng, D.C., Tehranizadeh, M.: Efficiency of hybrid algorithms for estimating the shear strength of deep reinforced concrete beams. Period. Polytech. Civ. Eng. 66(2), 398–410 (2022)

    Google Scholar 

  64. Al Thobiani, F., Khatir, S., Benaissa, B., Ghandourah, E., Mirjalili, S., Wahab, M.A.: A hybrid PSO and grey wolf optimization algorithm for static and dynamic crack identification. Theoret. Appl. Fract. Mech. 118, 103213 (2022)

    Article  Google Scholar 

  65. Firouzi, B., Abbasi, A., Sendur, P.: Improvement of the computational efficiency of metaheuristic algorithms for the crack detection of cantilever beams using hybrid methods. Eng. Optim. 54(7), 1236–1257 (2022)

    Article  Google Scholar 

  66. Kayabekir, A.E., Nigdeli, S.M., Bekdaş, G.: A hybrid metaheuristic method for optimization of active tuned mass dampers. Comput.-Aided Civ. Infrastruct. Eng. 37(8), 1027–1043 (2022)

    Article  Google Scholar 

  67. Örnek, B.N., Aydemir, S.B., Düzenli, T., Özak, B.: A novel version of slime mould algorithm for global optimization and real world engineering problems: Enhanced slime mould algorithm. Math. Comput. Simul. 198, 253–288 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  68. Chauhan, S., Vashishtha, G., Kumar, A.: A symbiosis of arithmetic optimizer with slime mould algorithm for improving global optimization and conventional design problem. J. Supercomput. 78(5), 6234–6274 (2022)

    Article  Google Scholar 

  69. Cao, H., Sun, W., Chen, Y., Kong, F., Feng, L.: Sizing and shape optimization of truss employing a hybrid constraint-handling technique and manta ray foraging optimization. Expert Syst. Appl. 213, 118999 (2023)

    Article  Google Scholar 

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Kayabekir, A.E., Nigdeli, S.M., Bekdaş, G. (2023). The Development of Hybrid Metaheuristics in Structural Engineering. 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_2

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