Optimal Design of Steel Structures Using Innovative Black Widow Algorithm Hybridized with Greedy Sensitivity-Based Particle Swarm Optimization Technique

Document Type : Regular Article

Authors

1 Professor, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

2 M.Sc. Student, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

Abstract

This paper presents a Greedy Sensitivity-based analysis implemented on the Particle Swarm Optimization search engine (GSPSO). The effectiveness of the method focuses mainly on providing an intelligent population to enter meta-heuristic algorithms. As a meta-heuristic method in the second stage, the recently introduced Black Widow Optimization (BWO) algorithm was selected and improved by the authors. It is based on three operators: cannibalism, crossover, and mutation, whose main stage is Cannibalism. The advantage of this stage is that those designs that do not match the solutions close to the global optimal are eliminated, and the more effective solutions remain. To examine the proposed approach, five optimization examples, including three two-dimensional benchmark frames and two three-dimensional structures, have been used. The results show that the greedy sensitivity-based PSO technique can improve computational efficiency in solving discrete variable structural optimization problems. The hybridized BWO (BGP) with this technique was able to obtain very good results in terms of convergence speed and performance accuracy. Overall, compared to the performance of BWO, between 50 and 75% improvement in the total number of analyzes was achieved. In addition, a slight improvement in the weight of the evaluated structures was also reported. Compared to other hybrid algorithms, very competitive and promising results were obtained.

Keywords

Main Subjects

References

[1]     Sinha GR. Modern Optimization Methods for Science, Engineering and Technology. IOP Publishing; 2019. https://doi.org/10.1088/978-0-7503-2404-5.
[2]     Ahmad S, Mehfuz S, Mebarek-Oudina F, Beg J. RSM analysis based cloud access security broker: a systematic literature review. Cluster Comput 2022;25:3733–63. https://doi.org/10.1007/s10586-022-03598-z.
[3]     Farhan M, Omar Z, Mebarek-Oudina F, Raza J, Shah Z, Choudhari R V, et al. Implementation of the One-Step One-Hybrid Block Method on the Nonlinear Equation of a Circular Sector Oscillator. Comput Math Model 2020;31:116–32. https://doi.org/10.1007/s10598-020-09480-0.
[4]     Nyo MT, Mebarek-Oudina F, Hlaing SS, Khan NA. Otsu’s thresholding technique for MRI image brain tumor segmentation. Multimed Tools Appl 2022;81:43837–49. https://doi.org/10.1007/s11042-022-13215-1.
[5]     Van TH, Tangaramvong S, Limkatanyu S, Xuan HN. Two-phase ESO and comprehensive learning PSO method for structural optimization with discrete steel sections. Adv Eng Softw 2022;167:103102. https://doi.org/10.1016/j.advengsoft.2022.103102.
[6]     Ghasemi MR, Ghasri M, Salarnia A. Soccer league optimization-based championship algorithm (SLOCA): A fast novel meta-heuristic technique for optimization problems. Adv Comput Des 2022;7:297–319. https://doi.org/10.12989/acd.2022.7.4.297.
[7]     Slowik A. Particle Swarm Optimization. Ind. Electron. Handb. - Five Vol. Set, vol. 4, 2011, p. 1942–8. https://doi.org/10.1007/978-3-319-46173-1_2.
[8]     Abdollahzadeh B, Gharehchopogh FS, Khodadadi N, Mirjalili S. Mountain Gazelle Optimizer: A new Nature-inspired Metaheuristic Algorithm for Global Optimization Problems. Adv Eng Softw 2022;174:103282. https://doi.org/10.1016/j.advengsoft.2022.103282.
[9]     Naruei I, Keynia F. Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems. Eng Comput 2022;38:3025–56.
[10]   Trojovský P, Dehghani M. Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications. Sensors 2022;22:855. https://doi.org/10.3390/s22030855.
[11]   Dhiman G, Kumar V. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Syst 2019;165:169–96. https://doi.org/10.1016/j.knosys.2018.11.024.
[12]   Pierezan J, Dos Santos Coelho L. Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems. 2018 IEEE Congr. Evol. Comput. CEC 2018 - Proc., 2018, p. 1–8. https://doi.org/10.1109/CEC.2018.8477769.
[13]   Jain M, Singh V, Rani A. A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm Evol Comput 2019;44:148–75. https://doi.org/10.1016/j.swevo.2018.02.013.
[14]   Yuan Y, Ren J, Wang S, Wang Z, Mu X, Zhao W. Alpine skiing optimization: A new bio-inspired optimization algorithm. Adv Eng Softw 2022;170:103158. https://doi.org/10.1016/j.advengsoft.2022.103158.
[15]   Abdollahzadeh B, Gharehchopogh FS, Mirjalili S. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 2021;158:107408. https://doi.org/10.1016/j.cie.2021.107408.
[16]   Iba K. Reactive Power Optimization by Genetic Algorithm. IEEE Trans Power Syst 1994;9:685–92. https://doi.org/10.1109/59.317674.
[17]   Kaveh A, Talatahari S. An improved ant colony optimization for the design of planar steel frames. Eng Struct 2010;32:864–73. https://doi.org/10.1016/j.engstruct.2009.12.012.
[18]   Lieu QX, Do DTT, Lee J. An adaptive hybrid evolutionary firefly algorithm for shape and size optimization of truss structures with frequency constraints. Comput Struct 2018;195:99–112. https://doi.org/10.1016/j.compstruc.2017.06.016.
[19]   Kaveh A, Ilchi Ghazaan M. Enhanced colliding bodies optimization for design problems with continuous and discrete variables. Adv Eng Softw 2014;77:66–75. https://doi.org/10.1016/j.advengsoft.2014.08.003.
[20]   Maheri MR, Talezadeh M. An Enhanced Imperialist Competitive Algorithm for optimum design of skeletal structures. Swarm Evol Comput 2018;40:24–36. https://doi.org/10.1016/j.swevo.2017.12.001.
[21]   Talatahari S, Gandomi AH, Yang XS, Deb S. Optimum design of frame structures using the Eagle Strategy with Differential Evolution. Eng Struct 2015;91:16–25. https://doi.org/10.1016/j.engstruct.2015.02.026.
[22]   Mashayekhi MR, Shirpour A, Sadeghi R. Finding Optimum Parameters of Passive Tuned Mass Damper by PSO, WOA, and Hybrid PSO-WOA (HPW) Algorithms. J Soft Comput Civ Eng 2023;7:72–92. https://doi.org/10.22115/scce.2023.352340.1489.
[23]   Vu Hong Son P, Soulisa FV. A Hybrid Ant Lion Optimizer (ALO) Algorithm for Construction Site Layout Optimization. J Soft Comput Civ Eng 2023;7:50–71. https://doi.org/10.22115/scce.2023.365303.1540.
[24]   Tayfur B, Yilmaz H, Daloğlu AT. Hybrid tabu search algorithm for weight optimization of planar steel frames. Eng Optim 2021;53:1369–83. https://doi.org/10.1080/0305215X.2020.1793977.
[25]   Gholizadeh S, Milany A. An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures. Eng Optim 2018;50:1829–49. https://doi.org/10.1080/0305215X.2017.1417402.
[26]   Hosseinaei S, Ghasemi MR, Etedali S, Chan THT. Reliability-based optimal control design for seismic-excited structures: A hybrid IS-MTLBO pseudo-double loop method. Structures 2022;44:1204–18. https://doi.org/10.1016/j.istruc.2022.07.040.
[27]   Rezaeemanesh M, Ghasemi SH, Rezaeemanesh M. Dual target optimization of two dimensional truss using cost efficiency and structural reliability sufficiency. J Soft Comput Civ Eng 2020;4:98–111. https://doi.org/10.22115/SCCE.2020.244833.1252.
[28]   Kamgar R, Samea P, Khatibinia M. Optimizing parameters of tuned mass damper subjected to critical earthquake. Struct Des Tall Spec Build 2018;27:e1460. https://doi.org/10.1002/tal.1460.
[29]   Salimi M, Kamgar R, Heidarzadeh H. An evaluation of the advantages of friction TMD over conventional TMD. Innov Infrastruct Solut 2021;6:1–12. https://doi.org/10.1007/s41062-021-00473-5.
[30]   Khatibinia M, Gholami H, Kamgar R. Optimal design of tuned mass dampers subjected to continuous stationary critical excitation. Int J Dyn Control 2018;6:1094–104. https://doi.org/10.1007/s40435-017-0386-7.
[31]   Kamgar R, Gholami F, Zarif Sanayei HR, Heidarzadeh H. Modified Tuned Liquid Dampers for Seismic Protection of Buildings Considering Soil–Structure Interaction Effects. Iran J Sci Technol - Trans Civ Eng 2020;44:339–54. https://doi.org/10.1007/s40996-019-00302-x.
[32]   Dadkhah M, Kamgar R, Heidarzadeh H, Jakubczyk-Galczyńska A, Jankowski R. Improvement of performance level of steel moment-resisting frames using tuned mass damper system. Appl Sci 2020;10:3403. https://doi.org/10.3390/APP10103403.
[33]   Wadhawan S, Bassi A, Singh R, Patel M. Prediction of Compressive Strength for Fly Ash-Based Concrete: Critical Comparison of Machine Learning Algorithms. J Soft Comput Civ Eng 2023;7:68–110. https://doi.org/10.22115/scce.2023.353183.1493.
[34]   Ghasemi MR, Ghasri M, Salarnia AH. ANFIS–TLBO Hybrid Approach to Predict Compressive Strength of Rectangular FRP Columns. Int J Optim Civ Eng 2022;12:399–410.
[35]   Ghasemi SH, Bahrami H, Akbari M. Classification of Seismic Vulnerability Based on Machine Learning Techniques for RC Frames. J Soft Comput Civ Eng 2020;4:13–21. https://doi.org/10.22115/scce.2020.223322.1186.
[36]   Hosseinaei S, Ghasemi MR, Etedali S, Chan THT. Sensitivity and Reliability Analyses in to Actively Controlled Structures Under Earthquake. Int J Struct Stab Dyn 2022;22:2250124. https://doi.org/10.1142/S0219455422501243.
[37]   Balling RJ. Optimal Steel Frame Design by Simulated Annealing. J Struct Eng 1991;117:1780–95. https://doi.org/10.1061/(asce)0733-9445(1991)117:6(1780).
[38]   May SA, Balling RJ. A filtered simulated annealing strategy for discrete optimization of 3D steel frameworks. Struct Optim 1992;4:142–8. https://doi.org/10.1007/BF01742735.
[39]   Pezeshk S, Camp C V., Chen D. Design of Nonlinear Framed Structures Using Genetic Optimization. J Struct Eng 2000;126:382–8. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:3(382).
[40]   Sarma KC, Adeli H. Fuzzy Discrete Multicriteria Cost Optimization of Steel Structures. J Struct Eng 2000;126:1339–47. https://doi.org/10.1061/(asce)0733-9445(2000)126:11(1339).
[41]   Kaveh A, Farahmand Azar B, Hadidi A, Rezazadeh Sorochi F, Talatahari S. Performance-based seismic design of steel frames using ant colony optimization. J Constr Steel Res 2010;66:566–74. https://doi.org/10.1016/j.jcsr.2009.11.006.
[42]   Alberdi R, Khandelwal K. Comparison of robustness of metaheuristic algorithms for steel frame optimization. Eng Struct 2015;102:40–60. https://doi.org/10.1016/j.engstruct.2015.08.012.
[43]   Alberdi R, Murren P, Khandelwal K. Connection topology optimization of steel moment frames using metaheuristic algorithms. Eng Struct 2015;100:276–92. https://doi.org/10.1016/j.engstruct.2015.06.014.
[44]   Gholizadeh S, Poorhoseini H. Optimum design of steel frame structures by a modified Dolphin echolocation algorithm. Struct Eng Mech 2015;55:535–54. https://doi.org/10.12989/sem.2015.55.3.535.
[45]   Bybordiani M, Kazemzadeh Azad S. Optimum design of steel braced frames considering dynamic soil-structure interaction. Struct Multidiscip Optim 2019;60:1123–37. https://doi.org/10.1007/s00158-019-02260-4.
[46]   Zakian P. Meta-heuristic design optimization of steel moment resisting frames subjected to natural frequency constraints. Adv Eng Softw 2019;135:102686. https://doi.org/10.1016/j.advengsoft.2019.102686.
[47]   Hassanzadeh A, Gholizadeh S. Collapse-performance-aided design optimization of steel concentrically braced frames. Eng Struct 2019;197:109411. https://doi.org/10.1016/j.engstruct.2019.109411.
[48]   Kaveh A, Biabani Hamedani K, Milad Hosseini S, Bakhshpoori T. Optimal design of planar steel frame structures utilizing meta-heuristic optimization algorithms. Structures 2020;25:335–46. https://doi.org/10.1016/j.istruc.2020.03.032.
[49]   Dumonteil P. Simple Equations for Effective Length Factors. AISC Eng J 1992;29:111–5.
[50]   Hayyolalam V, Pourhaji Kazem AA. Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 2020;87:103249. https://doi.org/10.1016/j.engappai.2019.103249.
[51]   Tessema B, Yen GG. An adaptive penalty formulation for constrained evolutionary optimization. IEEE Trans Syst Man, Cybern Part ASystems Humans 2009;39:565–78. https://doi.org/10.1109/TSMCA.2009.2013333.
[52]   Khajeh A, Ghasemi MR, Arab HG, Khajeh ) A. Hybrid Particle Swarm Optimization, Grid Search Method and Univariate Method To Optimally Design Steel Frame Structures. Int J Optim Civ Eng 2017;7:171–89.
[53]   Geethaikrishnan C, Mujumdar PM, Sudhakar K, Adimurthy V. a robust and efficient hybrid algorithm for global optimization. 2009 IEEE Int. Adv. Comput. Conf. IACC 2009, 2009, p. 486–91. https://doi.org/10.1109/IADCC.2009.4809059.
[54]   Gupta N, Khosravy M, Mahela OP, Patel N. Plant Biology-Inspired Genetic Algorithm: Superior Efficiency to Firefly Optimizer. Appl. Firefly Algorithm its Var. Case Stud. New Dev., Springer; 2020, p. 193–219. https://doi.org/10.1007/978-981-15-0306-1_9.
[55]   Erol OK, Eksin I. A new optimization method. Adv Eng Softw 2006;37:106–11.
[56]   Kaveh A, Talatahari S. Hybrid algorithm of harmony search, particle swarm and ant colony for structural design optimization. Stud Comput Intell 2009;239:159–98. https://doi.org/10.1007/978-3-642-03450-3_5.
[57]   Kaveh A, Ghazaan MI. Optimum design of skeletal structures using PSO-based algorithms. Period Polytech Civ Eng 2017;61:184–95. https://doi.org/10.3311/PPci.9614.
[58]   Degertekin SO. Optimum Design of Steel Frames via Harmony Search Algorithm. Stud. Comput. Intell., vol. 239, Springer; 2009, p. 51–78. https://doi.org/10.1007/978-3-642-03450-3_2.
[59]   Carbas S. Design optimization of steel frames using an enhanced firefly algorithm. Eng Optim 2016;48:2007–25. https://doi.org/10.1080/0305215X.2016.1145217.
[60]   Gholizadeh S, Razavi N, Shojaei E. Improved black hole and multiverse algorithms for discrete sizing optimization of planar structures. Eng Optim 2019;51:1645–67. https://doi.org/10.1080/0305215X.2018.1540697.
[61]   Kaveh A, Talatahari S, Alami MT. A newhybrid meta-heuristic for optimum design of frame structures. Asian J Civ Eng 2012;13:705–17.
[62]   Aydoğdu İ. Optimum design of 3-d irregular steel frames using ant colony optimization and harmony search algorithms. Middle East Technical University; 2010.
[63]   Javanmardi R, Ahmadi-Nedushan B. Cost Optimization of Steel-Concrete Composite I-Girder Bridges With Skew Angle and Longitudinal Slope, Using The Sm Toolbox and The Parallel Pattern Search Algorithm. vol. 11. 2021.
[64]   Salarnia AH, Ghasemi MR. Practical optimization of pedestrian bridges using grid search sensitivity based PSO. Int J Optim Civ Eng 2021;11:445–59.

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  • Receive Date: 16 November 2022
  • Revise Date: 10 May 2023
  • Accept Date: 19 May 2023

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