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Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints

    Dinh-Nhat Truong Affiliation
    ; Jui-Sheng Chou Affiliation

Abstract

Real-world optimization problems are ubiquitous across scientific domains, and many engineering challenges can be reimagined as optimization problems with relative ease. Consequently, researchers have focused on developing optimizers to tackle these challenges. The Snake Optimizer (SO) is an effective tool for solving complex optimization problems, drawing inspiration from snake patterns. However, the original SO requires the specification of six specific parameters to operate efficiently. In response to this, enhanced snake optimizers, namely ESO1 and ESO2, were developed in this study. In contrast to the original SO, ESO1 and ESO2 rely on a single set of parameters determined through sensitivity analysis when solving mathematical functions. This streamlined approach simplifies the application of ESOs for users dealing with optimization problems. ESO1 employs a logistic map to initialize populations, while ESO2 further refines ESO1 by integrating a Lévy flight to simulate snake movements during food searches. These enhanced optimizers were compared against the standard SO and 12 other established optimization methods to assess their performance. ESO1 significantly outperforms other algorithms in 15, 16, 13, 15, 21, 16, 24, 16, 19, 18, 13, 15, and 22 out of 24 mathematical functions. Similarly, ESO2 outperforms them in 16, 17, 18, 22, 23, 23, 24, 20, 19, 20, 17, 22, and 23 functions. Moreover, ESO1 and ESO2 were applied to solve complex structural optimization problems, where they outperformed existing methods. Notably, ESO2 generated solutions that were, on average, 1.16%, 0.70%, 2.34%, 3.68%, and 6.71% lighter than those produced by SO, and 0.79%, 0.54%, 1.28%, 1.70%, and 1.60% lighter than those of ESO1 for respective problems. This study pioneers the mathematical evaluation of ESOs and their integration with the finite element method for structural weight design optimization, establishing ESO2 as an effective tool for solving engineering problems.

Keyword : steel structural design, finite element analysis, metaheuristic algorithm, enhanced optimizer, benchmark functions

How to Cite
Truong, D.-N., & Chou, J.-S. (2023). Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints. Journal of Civil Engineering and Management, 29(8), 757–786. https://doi.org/10.3846/jcem.2023.20399
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References

American Institute of Steel Construction. (1989). Manual of steel construction: allowable stress design. Chicago, USA.

American Institute of Steel Construction. (1994). Manual of steel construction load resistance factor design. Chicago, USA.

Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609. https://doi.org/10.1016/j.cma.2020.113609

Alorf, A. (2023). A survey of recently developed metaheuristics and their comparative analysis. Engineering Applications of Artificial Intelligence, 117, 105622. https://doi.org/10.1016/j.engappai.2022.105622

Arafa, M., Sallam, E. A., & Fahmy, M. M. (2014). An enhanced differential evolution optimization algorithm. In 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), Bangkok, Thailand. https://doi.org/10.1109/DICTAP.2014.6821685

Askari, Q., Younas, I. & Saeed, M. (2020). Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Systems, 195, 105709. https://doi.org/10.1016/j.knosys.2020.105709

Aydogdu, I., Carbas, S., & Akin, A. (2017). Effect of Levy Flight on the discrete optimum design of steel skeletal structures using metaheuristics. Steel and Composite Structures, 24(1), 93–112. https://doi.org/10.12989/scs.2017.24.1.093

Beyer, H.-G., & Schwefel, H.-P. (2002). Evolution strategies – A comprehensive introduction. Natural Computing, 1(1), 3–52. https://doi.org/10.1023/A:1015059928466

Cheng, M.-Y., & Prayogo, D. (2014). Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112. https://doi.org/10.1016/j.compstruc.2014.03.007

Cheng, M.-Y., & Cao, M.-T. (2016). Estimating strength of rubberized concrete using evolutionary multivariate adaptive regression splines. Journal of Civil Engineering and Management, 22(5), 711–720. https://doi.org/10.3846/13923730.2014.897989

Chou, J.-S., & Ngo, N.-T. (2017). Modified firefly algorithm for multidimensional optimization in structural design problems. Structural and Multidisciplinary Optimization, 55(6), 2013–2028. https://doi.org/10.1007/s00158-016-1624-x

Chou, J.-S., & Nguyen, N.-M. (2020). FBI inspired meta-optimization. Applied Soft Computing, 93, 106339. https://doi.org/10.1016/j.asoc.2020.106339

Chou, J.-S., & Truong, D.-N.. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, 125535. https://doi.org/10.1016/j.amc.2020.125535

da Silva, E. K., Barbosa, H. J. C., & Lemonge, A. C. C. (2011). An adaptive constraint handling technique for differential evolution with dynamic use of variants in engineering optimization. Optimization and Engineering, 12(1), 31–54. https://doi.org/10.1007/s11081-010-9114-2

Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering Software, 146, 102804. https://doi.org/10.1016/j.advengsoft.2020.102804

Degertekin, S. O., Lamberti, L., & Ugur, I. B. (2018). Sizing, layout and topology design optimization of truss structures using the Jaya algorithm. Applied Soft Computing, 70, 903–928. https://doi.org/10.1016/j.asoc.2017.10.001

Degertekin, S. O., Lamberti, L., & Ugur, I. B. (2019). Discrete sizing/layout/topology optimization of truss structures with an advanced Jaya algorithm. Applied Soft Computing, 79, 363–390. https://doi.org/10.1016/j.asoc.2019.03.058

Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18. https://doi.org/10.1016/j.swevo.2011.02.002

Do, D. T. T., Lee, S., & Lee, J. (2016). A modified differential evolution algorithm for tensegrity structures. Composite Structures, 158, 11–19. https://doi.org/10.1016/j.compstruct.2016.08.039

Doğan, B., & Ölmez, T. (2015). A new metaheuristic for numerical function optimization: Vortex Search algorithm. Information Sciences, 293, 125–145. https://doi.org/10.1016/j.ins.2014.08.053

Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39. https://doi.org/10.1109/MCI.2006.329691

Erbatur, F., Hasançebi, O., Tütüncü, İ., & Kılıç, H. (2000). Optimal design of planar and space structures with genetic algorithms. Computers & Structures, 75(2), 209–224. https://doi.org/10.1016/S0045-7949(99)00084-X

Erdal, F., Doğan, E., & Saka, M. P. (2011). Optimum design of cellular beams using harmony search and particle swarm optimizers. Journal of Constructional Steel Research, 67(2), 237–247. https://doi.org/10.1016/j.jcsr.2010.07.014

Erol, O. K., & Eksin, I. (2006). A new optimization method: Big Bang–Big Crunch. Advances in Engineering Software, 37(2), 106–111. https://doi.org/10.1016/j.advengsoft.2005.04.005

Es-Haghi, M. S., Salehi, A., & Strauss, A. (2022). Enhanced teacher-learning based algorithm in real size structural optimization. Journal of Civil Engineering and Management, 28(4), 292–304. https://doi.org/10.3846/jcem.2022.16387

Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., & Alizadeh, Y. (2008). Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 197(33), 3080–3091. https://doi.org/10.1016/j.cma.2008.02.006

Ficarella, E., Lamberti, L., & Degertekin, S. O. (2021). Comparison of three novel hybrid metaheuristic algorithms for structural optimization problems. Computers & Structures, 244, 106395. https://doi.org/10.1016/j.compstruc.2020.106395

Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2013). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35. https://doi.org/10.1007/s00366-011-0241-y

Gao, Q., Xu, H., & Li, A. (2022). The analysis of commodity demand predication in supply chain network based on particle swarm optimization algorithm. Journal of Computational and Applied Mathematics, 400, 113760. https://doi.org/10.1016/j.cam.2021.113760

Hasançebi, O., Çarbaş, S., Doğan, E., Erdal, F., & Saka, M. P. (2009). Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Computers & Structures, 87(5), 284–302. https://doi.org/10.1016/j.compstruc.2009.01.002

Hasançebi, O., Çarbaş, S., Doğan, E., Erdal, F., & Saka, M. P. (2010). Comparison of non-deterministic search techniques in the optimum design of real size steel frames. Computers & Structures, 88(17), 1033–1048. https://doi.org/10.1016/j.compstruc.2010.06.006

Hashim, F. A., & Hussien, A. G. (2022). Snake Optimizer: A novel meta-heuristic optimization algorithm. Knowledge-Based Systems, 242, 108320. https://doi.org/10.1016/j.knosys.2022.108320

Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73. https://doi.org/10.1038/scientificamerican0792-66

Hu, G., Zhong, J., Du, B., & Wei, G. (2022). An enhanced hybrid arithmetic optimization algorithm for engineering applications. Computer Methods in Applied Mechanics and Engineering, 394, 114901. https://doi.org/10.1016/j.cma.2022.114901

Huan, T. T., Kulkarni, A. J., Kanesan, J., Huang, C. J., & Abraham, A. (2017). Ideology algorithm: a socio-inspired optimization methodology. Neural Computing and Applications, 28(1), 845–876. https://doi.org/10.1007/s00521-016-2379-4

Hwang, S.-F., & He, R.-S. (2006). Improving real-parameter genetic algorithm with simulated annealing for engineering problems. Advances in Engineering Software, 37(6), 406–418. https://doi.org/10.1016/j.advengsoft.2005.08.002

Ji, A., Xue, X., Wang, Y., Luo, X., & Zhang, M. (2020). An integrated multi-objectives optimization approach on modelling pavement maintenance strategies for pavement sustainability. Journal of Civil Engineering and Management, 26(8), 717–732. https://doi.org/10.3846/jcem.2020.13751

Joshi, H., & Arora, S. (2017). Enhanced grey wolf optimization algorithm for global optimization. Fundamenta Informaticae, 153, 235–264. https://doi.org/10.3233/FI-2017-1539

Kamoona, A. M., Patra, J. C., & Stojcevski, A. (2018). An enhanced cuckoo search algorithm for solving optimization problems. In 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil. https://doi.org/10.1109/CEC.2018.8477784

Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471. https://doi.org/10.1007/s10898-007-9149-x

Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3), 267–289. https://doi.org/10.1007/s00707-009-0270-4

Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: Ray Optimization. Computers & Structures, 112–113, 283–294. https://doi.org/10.1016/j.compstruc.2012.09.003

Kaveh, A., & Mahdavi, V. R. (2014). Colliding Bodies Optimization method for optimum discrete design of truss structures. Computers & Structures, 139, 43–53. https://doi.org/10.1016/j.compstruc.2014.04.006

Kaveh, A., & Bakhshpoori, T. (2016). Water Evaporation Optimization: A novel physically inspired optimization algorithm. Computers & Structures, 167, 69–85. https://doi.org/10.1016/j.compstruc.2016.01.008

Kaveh, A., & Ilchi Ghazaan, M. (2018a). Meta-heuristic algorithms for optimal design of real-size structures. Springer, Cham. https://doi.org/10.1007/978-3-319-78780-0

Kaveh, A., & Ilchi Ghazaan, M. (2018b). Optimal design of double-layer grids. In Meta-heuristic algorithms for optimal design of real-size structures (pp. 65–83). Springer, Cham. https://doi.org/10.1007/978-3-319-78780-0_5

Kaveh, A., & Ilchi Ghazaan, M. (2018c). Optimal design of steel lattice transmission line towers. In Meta-heuristic algorithms for optimal design of real-size structures (pp. 123–137). Springer, Cham. https://doi.org/10.1007/978-3-319-78780-0_8

Kaveh, A., & Mahjoubi, S. (2018). Optimum design of double-layer barrel vaults by lion pride optimization algorithm and a comparative study. Structures, 13, 213–229. https://doi.org/10.1016/j.istruc.2018.01.002

Kaveh, A., Hamedani, K. B., & Kamalinejad, M. (2021). An enhanced Forensic-Based Investigation algorithm and its application to optimal design of frequency-constrained dome structures. Computers & Structures, 256, 106643. https://doi.org/10.1016/j.compstruc.2021.106643

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 – International Conference on Neural Networks. https://doi.org/10.1109/ICNN.1995.488968

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680. https://doi.org/10.1126/science.220.4598.671

Kumar, M., Kulkarni, A. J., & Satapathy, S. C. (2018). Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology. Future Generation Computer Systems, 81, 252–272. https://doi.org/10.1016/j.future.2017.10.052

Kumar, A., Price, K. V., Mohamed, A. W., Hadi, A. A., & Suganthan, P. N. (2021). Problem definitions and evaluation criteria for the CEC 2022 special session and competition on single objective bound constrained numerical optimization (Technical report). https://github.com/P-N-Suganthan

Lam, A. Y. S., & Li, V. O. K. (2010). Chemical-reaction-inspired metaheuristic for optimization. IEEE Transactions on Evolutionary Computation, 14(3), 381–399. https://doi.org/10.1109/TEVC.2009.2033580

Lee, K. S., Geem, Z. W., Lee, S.-h., & Bae, K.-w. (2005). The harmony search heuristic algorithm for discrete structural optimization. Engineering Optimization, 37(7), 663–684. https://doi.org/10.1080/03052150500211895

Lemonge, A. C. C., & Barbosa, H. J. C. (2004). An adaptive penalty scheme for genetic algorithms in structural optimization. International Journal for Numerical Methods in Engineering, 59(5), 703–736. https://doi.org/10.1002/nme.899

Li, M., Zhao, H., Weng, X., & Han, T. (2016). Cognitive behavior optimization algorithm for solving optimization problems. Applied Soft Computing, 39, 199–222. https://doi.org/10.1016/j.asoc.2015.11.015

Li, Y,. & Han, M. (2020). Improved fruit fly algorithm on structural optimization. Brain Informatics, 7(1), 1. https://doi.org/10.1186/s40708-020-0102-9

Logan, D. L. (2016). A first course in the finite element method. Cengage Learning.

May, R. (1976). Simple mathematical models with very complicated dynamics. Nature, 261, 459–467. https://doi.org/10.1038/261459a0

Makiabadi, M. H., & Maheri, M. R. (2021). An enhanced symbiotic organism search algorithm (ESOS) for the sizing design of pin connected structures. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 45(3), 1371–1396. https://doi.org/10.1007/s40996-020-00471-0

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Mousavirad, S. J., & Ebrahimpour-Komleh, H. (2017). Human mental search: A new population-based metaheuristic optimization algorithm. Applied Intelligence, 47(3), 850–887. https://doi.org/10.1007/s10489-017-0903-6

Omran, M. G. H. (2016). A novel cultural algorithm for real-parameter optimization. International Journal of Computer Mathematics, 93(9), 1541–1563. https://doi.org/10.1080/00207160.2015.1067309

Pholdee, N., & Bureerat, S. (2012). Performance enhancement of multiobjective evolutionary optimisers for truss design using an approximate gradient. Computers & Structures, 106–107, 115–124. https://doi.org/10.1016/j.compstruc.2012.04.015

Podolski, M., & Sroka, B. (2019). Cost optimization of multiunit construction projects using linear programming and metaheuristic-based simulated annealing algorithm. Journal of Civil Engineering and Management, 25(8), 848–857. https://doi.org/10.3846/jcem.2019.11308

Ramli, M. R., Abas, Z. A., Desa, M. I., Abidin, Z. Z., & Alazzam, M. B. (2019). Enhanced convergence of Bat Algorithm based on dimensional and inertia weight factor. Journal of King Saud University – Computer and Information Sciences, 31(4), 452–458. https://doi.org/10.1016/j.jksuci.2018.03.010

Rao, R. V. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19–34. https://doi.org/10.5267/j.ijiec.2015.8.004

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015

Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

Rocca, P., Oliveri, G., & Massa, A. (2011). Differential evolution as applied to electromagnetics. IEEE Antennas and Propagation Magazine, 53(1), 38–49. https://doi.org/10.1109/MAP.2011.5773566

Sabbah, T. (2020). Enhanced genetic algorithm for optimized classification. In 2020 International Conference on Promising Electronic Technologies (ICPET), Jerusalem, Palestine. https://doi.org/10.1109/ICPET51420.2020.00039

Samareh Moosavi, S. H., & Bardsiri, V. K. (2019). Poor and rich optimization algorithm: A new human-based and multi populations algorithm. Engineering Applications of Artificial Intelligence, 86, 165–181. https://doi.org/10.1016/j.engappai.2019.08.025

Shabani, A., Asgarian, B., Salido, M., & Asil Gharebaghi, S. (2020). Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems. Expert Systems with Applications, 161, 113698. https://doi.org/10.1016/j.eswa.2020.113698

Shareef, H., Ibrahim, A. A., & Mutlag, A. H. (2015). Lightning search algorithm. Applied Soft Computing, 36, 315–333. https://doi.org/10.1016/j.asoc.2015.07.028

Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.919004

Storn, R., & Price, K. (1997). Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328

Truong, D.-N., & Chou, J.-S. (2023). Fuzzy adaptive forensic-based investigation algorithm for optimizing frequency-constrained structural dome design. Mathematics and Computers in Simulation, 210, 473–531. https://doi.org/10.1016/j.matcom.2023.03.007

van Laarhoven, P. J. M., & Aarts, E. H. L. (1987). Simulated annealing. Dordrecht. Springer Netherlands. https://doi.org/10.1007/978-94-015-7744-1_2

Wu, L., Wu, J., & Wang, T. (2023). Enhancing grasshopper optimization algorithm (GOA) with levy flight for engineering applications. Scientific Reports, 13(1), 124. https://doi.org/10.1038/s41598-022-27144-4

Wu, S.-J., & Chow, P.-T. (1995). Steady-state genetic algorithms for discrete optimization of trusses. Computers & Structures, 56(6), 979–991. https://doi.org/10.1016/0045-7949(94)00551-D

Xue, X., & Chen, X. (2019). Determination of ultimate bearing capacity of shallow foundations using Lssvm algorithm. Journal of Civil Engineering and Management, 25(5), 451–459. https://doi.org/10.3846/jcem.2019.9875

Yang, X.-S., & Deb, S. (2009). Cuckoo search via Lévy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, India. https://doi.org/10.1109/NABIC.2009.5393690

Yang, X.-S. (2010a). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2(2 ), 78–84. https://doi.org/10.1504/IJBIC.2010.032124

Yang, X.-S. (2010b). Nature-inspired metaheuristic algorithms. Luniver Press.

Yang, X. S., & Hossein Gandomi, A. (2012). Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29(5), 464–483. https://doi.org/10.1108/02644401211235834

Yıldızdan, G., & Baykan, Ö. K. (2021). A novel artificial jellyfish search algorithm improved with detailed local search strategy. In 2021 6th International Conference on Computer Science and Engineering (UBMK), Ankara, Turkey. https://doi.org/10.1109/UBMK52708.2021.9559009

Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283–304. https://doi.org/10.1016/j.knosys.2018.08.030

Zhou, Y., Ling, Y., & Luo, Q. (2018). Lévy flight trajectory-based whale optimization algorithm for engineering optimization. Engineering Computations, 35(7), 2406–2428. https://doi.org/10.1108/EC-07-2017-0264

Zong Woo, G., Joong Hoon, K., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68. https://doi.org/10.1177/003754970107600201