Home Optimal design of structural engineering components using artificial neural network-assisted crayfish algorithm
Article
Licensed
Unlicensed Requires Authentication

Optimal design of structural engineering components using artificial neural network-assisted crayfish algorithm

  • Sadiq M. Sait

    Dr. Sadiq M. Sait received his Bachelor’s degree in Electronics Engineering from Bangalore University, India, in 1981, and his Master’s and Ph.D. degrees in Electrical Engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a Professor of Computer Engineering and Director of the Center for Communications and IT Research, KFUPM, Dhahran, Saudi Arabia.

         , Pranav Mehta

    Mr. Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387001, Gujarat, India. He is currently a Ph.D. research scholar with the Dharmsinh Desai University, Nadiad, Gujarat, India. His major research interests are metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

         , Ali Rıza Yıldız

    Dr. Ali Rıza Yıldız is a Professor in the Department of Mechanical Engineering, Bursa Uludağ University, Bursa, Turkey. His research interests are the finite element analysis of structural components, lightweight design, vehicle design, vehicle crashworthiness, shape and topology optimization of vehicle components, meta-heuristic optimization techniques, and additive manufacturing.

     EMAIL logo     and Betül Sultan Yıldız

    Dr. Betül Sultan Yıldız is an Associate professor in the Department of Mechanical Engineering at Bursa Uludağ University, Bursa, Turkey. Her research interests are mechanical design, structural optimization methods, and meta-heuristic optimization algorithms.
Published/Copyright: May 27, 2024
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Materials Testing
From the journal Materials Testing Volume 66 Issue 9

Abstract

Optimization techniques play a pivotal role in enhancing the performance of engineering components across various real-world applications. Traditional optimization methods are often augmented with exploitation-boosting techniques due to their inherent limitations. Recently, nature-inspired algorithms, known as metaheuristics (MHs), have emerged as efficient tools for solving complex optimization problems. However, these algorithms face challenges such as imbalance between exploration and exploitation phases, slow convergence, and local optima. Modifications incorporating oppositional techniques, hybridization, chaotic maps, and levy flights have been introduced to address these issues. This article explores the application of the recently developed crayfish optimization algorithm (COA), assisted by artificial neural networks (ANN), for engineering design optimization. The COA, inspired by crayfish foraging and migration behaviors, incorporates temperature-dependent strategies to balance exploration and exploitation phases. Additionally, ANN augmentation enhances the algorithm’s performance and accuracy. The COA method optimizes various engineering components, including cantilever beams, hydrostatic thrust bearings, three-bar trusses, diaphragm springs, and vehicle suspension systems. Results demonstrate the effectiveness of the COA in achieving superior optimization solutions compared to other algorithms, emphasizing its potential for diverse engineering applications.

Keywords: crayfish algorithm; optimization; mechanical design problems; automobile component; artificial neural network
Corresponding author: Ali Rıza Yıldız, Department of Mechanical Engineering, Bursa Uludag University, Bursa, Türkiye, E-mail:

About the authors

Sadiq M. Sait

Dr. Sadiq M. Sait received his Bachelor’s degree in Electronics Engineering from Bangalore University, India, in 1981, and his Master’s and Ph.D. degrees in Electrical Engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a Professor of Computer Engineering and Director of the Center for Communications and IT Research, KFUPM, Dhahran, Saudi Arabia.

Pranav Mehta

Mr. Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387001, Gujarat, India. He is currently a Ph.D. research scholar with the Dharmsinh Desai University, Nadiad, Gujarat, India. His major research interests are metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

Ali Rıza Yıldız

Dr. Ali Rıza Yıldız is a Professor in the Department of Mechanical Engineering, Bursa Uludağ University, Bursa, Turkey. His research interests are the finite element analysis of structural components, lightweight design, vehicle design, vehicle crashworthiness, shape and topology optimization of vehicle components, meta-heuristic optimization techniques, and additive manufacturing.

Betül Sultan Yıldız

Dr. Betül Sultan Yıldız is an Associate professor in the Department of Mechanical Engineering at Bursa Uludağ University, Bursa, Turkey. Her research interests are mechanical design, structural optimization methods, and meta-heuristic optimization algorithms.

References

[1] K.-L. Du and M. N. S. Swamy, Search and Optimization by Metaheuristics, Cham, Springer International Publishing, 2016.Search in Google Scholar

[2] B. Chopard and M. Tomassini, “An introduction to metaheuristics for optimization,” in Natural Computing Series, Cham, Springer International Publishing, 2018.10.1007/978-3-319-93073-2Search in Google Scholar

[3] P. Singh and S. K. Choudhary, “Introduction: optimization and metaheuristics algorithms,” in Metaheuristic and Evolutionary Computation: Algorithms and Applications, vol. 916, H. Malik, A. Iqbal, P. Joshi, S. Agrawal, and F. I. Bakhsh, Eds., in Studies in Computational Intelligence, vol. 916, Singapore: Springer Singapore, 2021, pp. 3–33.10.1007/978-981-15-7571-6_1Search in Google Scholar

[4] S. Kumar, et al.., “Chaotic marine predators algorithm for global optimization of real-world engineering problems,” Knowledge-Based Syst., vol. 261, 2023, Art. no. 110192, https://doi.org/10.1016/j.knosys.2022.110192.Search in Google Scholar

[5] D. Gürses, P. Mehta, S. M. Sait, S. Kumar, and A. R. Yildiz, “A multi-strategy boosted prairie dog optimization algorithm for global optimization of heat exchangers,” Mater. Test., vol. 65, no. 9, pp. 1396–1404, 2023, https://doi.org/10.1515/mt-2023-0082.Search in Google Scholar

[6] B. S. Yıldız, P. Mehta, N. Panagant, S. Mirjalili, and A. R. Yildiz, “A novel chaotic RungeKutta optimization algorithm for solving constrained engineering problems,” J. Comput. Design Eng., vol. 9, no. 6, pp. 2452–2465, 2022, https://doi.org/10.1093/jcde/qwac113.Search in Google Scholar

[7] B. S. Yıldız, et al.., “A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems,” Knowledge-Based Syst., vol. 271, 2023, Art. no. 110554, https://doi.org/10.1016/j.knosys.2023.110554.Search in Google Scholar

[8] S. Khatir, S. Tiachacht, C. Le Thanh, E. Ghandourah, S. Mirjalili, and M. Abdel Wahab, “An improved artificial neural network using arithmetic optimization algorithm for damage assessment in FGM composite plates,” Compos. Struct., vol. 273, 2021, Art. no. 114287, https://doi.org/10.1016/j.compstruct.2021.114287.Search in Google Scholar

[9] M. Irfan Shirazi, S. Khatir, B. Benaissa, S. Mirjalili, and M. Abdel Wahab, “Damage assessment in laminated composite plates using modal Strain Energy and YUKI-ANN algorithm,” Compos. Struct., vol. 303, 2023, Art. no. 116272, https://doi.org/10.1016/j.compstruct.2022.116272.Search in Google Scholar

[10] R. Janarthanan, R. U. Maheshwari, P. K. Shukla, S. Mirjalili, and M. Kumar, “Intelligent detection of the PV faults based on artificial neural network and type 2 fuzzy systems,” Energies, vol. 14, no. 20, p. 6584, 2021, https://doi.org/10.3390/en14206584.Search in Google Scholar

[11] S. Barua and A. Merabet, “Lévy Arithmetic Algorithm: an enhanced metaheuristic algorithm and its application to engineering optimization,” Expert Syst. Appl., vol. 241, 2024, Art. no. 122335, https://doi.org/10.1016/j.eswa.2023.122335.Search in Google Scholar

[12] Z. Li, X. Gao, X. Huang, J. Gao, X. Yang, and M.-J. Li, “Tactical unit algorithm: a novel metaheuristic algorithm for optimal loading distribution of chillers in energy optimization,” Appl. Thermal Eng., vol. 238, 2024, Art. no. 122037, https://doi.org/10.1016/j.applthermaleng.2023.122037.Search in Google Scholar

[13] W. Zhao, Z. Zhang, and L. Wang, “Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications,” Eng. Appl. Artif. Intell., vol. 87, 2020, Art. no. 103300, https://doi.org/10.1016/j.engappai.2019.103300.Search in Google Scholar

[14] A. Taheri, et al.., “Partial reinforcement optimizer: an evolutionary optimization algorithm,” Expert Syst. Appl., vol. 238, 2024, Art. no. 122070, https://doi.org/10.1016/j.eswa.2023.122070.Search in Google Scholar

[15] E.-S. M. El-kenawy, N. Khodadadi, S. Mirjalili, A. A. Abdelhamid, M. M. Eid, and A. Ibrahim, “Greylag goose optimization: nature-inspired optimization algorithm,” Expert Syst. Appl., vol. 238, 2024, Art. no. 122147, https://doi.org/10.1016/j.eswa.2023.122147.Search in Google Scholar

[16] H. Jia, H. Rao, C. Wen, and S. Mirjalili, “Crayfish optimization algorithm,” Artif. Intell. Rev., vol. 56, no. S2, pp. 1919–1979, 2023, https://doi.org/10.1007/s10462-023-10567-4.Search in Google Scholar

[17] S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper optimisation algorithm: theory and application,” Adv. Eng. Softw., vol. 105, pp. 30–47, 2017, https://doi.org/10.1016/j.advengsoft.2017.01.004.Search in Google Scholar

[18] S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization,” Neural Comput. Appl., vol. 27, no. 2, pp. 495–513, 2016, https://doi.org/10.1007/s00521-015-1870-7.Search in Google Scholar

[19] S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, “Salp Swarm algorithm: a bio-inspired optimizer for engineering design problems,” Adv. Eng. Softw., vol. 114, pp. 163–191, 2017, https://doi.org/10.1016/j.advengsoft.2017.07.002.Search in Google Scholar

[20] S. Mirjalili, “Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm,” Knowledge-Based Syst., vol. 89, pp. 228–249, 2015, https://doi.org/10.1016/j.knosys.2015.07.006.Search in Google Scholar

[21] S. Mirjalili, “The ant lion optimizer,” Adv. Eng. Softw., vol. 83, pp. 80–98, 2015, https://doi.org/10.1016/j.advengsoft.2015.01.010.Search in Google Scholar

[22] A. R. Yıldız, B. S. Yıldız, S. M. Sait, S. Bureerat, and N. Pholdee, “A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems,” Mater. Test., vol. 61, no. 8, pp. 735–743, 2019, https://doi.org/10.3139/120.111378.Search in Google Scholar

[23] R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems,” Comput. Aided Design, vol. 43, no. 3, pp. 303–315, 2011, https://doi.org/10.1016/j.cad.2010.12.015.Search in Google Scholar

[24] P. Savsani and V. Savsani, “Passing vehicle search (PVS): a novel metaheuristic algorithm,” Appl. Math. Modell., vol. 40, nos. 5–6, pp. 3951–3978, 2016, https://doi.org/10.1016/j.apm.2015.10.040.Search in Google Scholar

[25] A. W. Mohamed, “A novel differential evolution algorithm for solving constrained engineering optimization problems,” J. Intell. Manuf., vol. 29, no. 3, pp. 659–692, 2018, https://doi.org/10.1007/s10845-017-1294-6.Search in Google Scholar

[26] W. Zhao, et al.., “Electric eel foraging optimization: a new bio-inspired optimizer for engineering applications,” Expert Syst. Appl., vol. 238, 2024, Art. no. 122200, https://doi.org/10.1016/j.eswa.2023.122200.Search in Google Scholar

[27] S. He, E. Prempain, and Q. H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems,” Eng. Optimizat., vol. 36, no. 5, pp. 585–605, 2004, https://doi.org/10.1080/03052150410001704854.Search in Google Scholar

[28] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: algorithm and applications,” Future Gen. Comput. Syst., vol. 97, pp. 849–872, 2019, https://doi.org/10.1016/j.future.2019.02.028.Search in Google Scholar

[29] A. H. Gandomi, X.-S. Yang, and A. H. Alavi, “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems,” Eng. Comput., vol. 29, no. 1, pp. 17–35, 2013, https://doi.org/10.1007/s00366-011-0241-y.Search in Google Scholar

[30] A. R. Yildiz, “Optimal structural design of vehicle components using topology design and optimization,” Mater. Test., vol. 50, no. 4, pp. 224–228, 2008, https://doi.org/10.3139/120.100880.Search in Google Scholar

[31] B.S. Yildiz, S. Kumar, N. Pholdee, S. Bureerat, and A.R. Yildiz, “A new chaotic Levy flight distribution optimization algorithm for solving constrained engineering problems,” Expert Systems, vol. 39, no. 8, 2022. https://doi.org/10.1111/exsy.12992.Search in Google Scholar

[32] H. Abderazek, S. M. Sait, and A. R. Yildiz, “Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics,” Int. J. Vehicle Design, vol. 80, nos. 2/3/4, p. 121, 2019, https://doi.org/10.1504/IJVD.2019.109862.Search in Google Scholar

[33] H. Abderazek, A. R. Yildiz, and S. M. Sait, “Mechanical engineering design optimisation using novel adaptive differential evolution algorithm,” Int. J. Vehicle Design, vol. 80, nos. 2/3/4, pp. 285, 2019, https://doi.org/10.1504/IJVD.2019.109873.Search in Google Scholar

[34] S.C. Chu, T.T. Wang, A.R. Yildiz, and J.S. Pan, “Ship Rescue Optimization: A New Metaheuristic Algorithm for Solving Engineering Problems,” Journal of Internet Technology, vol. 25, no. 1, pp. 71–78, 2024. https://doi.org/10.53106/160792642024012501006.Search in Google Scholar

[35] S. M. Sait, P. Mehta, D. Gürses, and A. R. Yildiz, “Cheetah optimization algorithm for optimum design of heat exchangers,” Mater. Test., vol. 65, no. 8, pp. 1230–1236, 2023, https://doi.org/10.1515/mt-2023-0015.Search in Google Scholar

[36] B. S. Yildiz, et al.., “A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems,” Mater. Test., vol. 65, no. 1, pp. 134–143, 2023, https://doi.org/10.1515/mt-2022-0183.Search in Google Scholar

[37] B. S. Yildiz, P. Mehta, S. M. Sait, N. Panagant, S. Kumar, and A. R. Yildiz, “A new hybrid artificial hummingbird-simulated annealing algorithm to solve constrained mechanical engineering problems,” Mater. Test., vol. 64, no. 7, pp. 1043–1050, 2022, https://doi.org/10.1515/mt-2022-0123.Search in Google Scholar

[38] S. Kumar, et al.., “Chaotic marine predators algorithm for global optimization of real-world engineering problems,” Knowl.-Based Syst., vol. 261, 2023. https://doi.org/10.1016/j.knosys.2022.110192.Search in Google Scholar

[39] A. Karaduman, B. S. Yildiz, and A. R. Yildiz, “Experimental and numerical fatigue-based design optimisation of clutch diaphragm spring in the automotive industry,” IJVD, vol. 80, no. 2–4, pp. 330–345, 2019. https://doi.org/10.1504/IJVD.2019.109875.Search in Google Scholar

[40] B. S. Yildiz, P. Mehta, N. Panagant, S. Mirjalili, and A. R. Yildiz, “A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems,” Journal of Computational Design and Engineering, vol. 9, no. 6, pp. 2452–2465, 2022. https://doi.org/10.1093/jcde/qwac113.Search in Google Scholar

[41] B. S. Yildiz, et al.., “A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems,” Knowl.-Based Syst., vol. 271, 2023. https://doi.org/10.1016/j.knosys.2023.110554.Search in Google Scholar

[42] H. M. Jia, X. L. Zhou, J. R. Zhang, L. Abualigah, A. R. Yildiz, and A. G. Hussien, “Modified crayfish optimization algorithm for solving multiple engineering application problems,” Artif. Intell. Rev., vol. 57, no. 5, 2024. https://doi.org/10.1007/s10462-024-10738-x.Search in Google Scholar

[43] Y. Kanokmedhakul, N. Bureerat, N. Panagant, T. Radpukdee, N. Pholdee, and A. R. Yildiz, “Metaheuristic-assisted complex H-infinity flight control tuning for the Hawkeye unmanned aerial vehicle: A comparative study,” Expert Syst. Appl., vol. 248, 2024. https://doi.org/10.1016/j.eswa.2024.123428.Search in Google Scholar

[44] P. Mehta, A. R. Yildiz, S. M. Sait, and B. S. Yildiz, “Enhancing the structural performance of engineering components using the geometric mean optimizer,” Mater. Test., vol. 66, no. 7, pp. 1063–1073, 2024. https://doi.org/10.1515/mt-2024-0005.Search in Google Scholar

[45] Z. Meng, B. S. Yildiz, G. Li, C. T. Zhong, S. Mirjalili, and A. R. Yildiz, “Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study,” Struct. Multidiscip. Optim., vol. 66, no. 8, 2023. https://doi.org/10.1007/s00158-023-03639-0.Search in Google Scholar

[46] S. Anosri, et al.., “A comparative study of state-of-the-art metaheuristics for solving many-objective optimization problems of fixed wing unmanned aerial vehicle conceptual design,” Arch. Comput. Methods Eng., vol. 30, no. 6, pp. 3657–3671, 2023. https://doi.org/10.1007/s11831-023-09914-z.Search in Google Scholar

[47] P. Champasak, N. Panagant, N. Pholdee, S. Bureerat, P. Rajendran, and A. R. Yildiz, “Grid-based many-objective optimiser for aircraft conceptual design with multiple aircraft configurations,” Eng. Appl. Artif. Intell., vol. 126, 2023. https://doi.org/10.1016/j.engappai.2023.106951.Search in Google Scholar
Published Online: 2024-05-27
Published in Print: 2024-09-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Modal analysis for the non-destructive testing of brazed components
  3. Numerical analysis of cathodic protection of a Q355ND frame in a shallow water subsea Christmas tree
  4. Friction stir lap welding of AZ31B magnesium alloy to AISI 304 stainless steel
  5. Microstructure and mechanical properties of double pulse TIG welded super austenitic stainless steel butt joints
  6. Numerical and experimental investigation of impact performances of cast and stretched polymethyl methacrylate panels
  7. Mechanical properties of Sr inoculated A356 alloy by Taguchi-based gray relational analysis
  8. Influence of high-reactivity energetic materials on microstructure and performance on iron-based cladding layer under low laser power
  9. Experimental investigations and material modeling of an elastomer jaw coupling
  10. Optimal design of structural engineering components using artificial neural network-assisted crayfish algorithm
  11. A novel chaotic artificial rabbits algorithm for optimization of constrained engineering problems
  12. Numerical and experimental investigation of the effect of heat input on weld bead geometry and stresses in laser welding
  13. Influence of betel nut fiber hybridization on properties of novel aloevera fiber-reinforced vinyl ester composites
  14. Electro discharge machining performance of cryogenic heat treated copper electrode
  15. Influence of IP-TIG welding parameters on weld bead geometry, tensile properties, and microstructure of Ti6Al4V alloy joints
  16. Experimental and numerical investigation of crash performances of additively manufactured novel multi-cell crash box made with CF15PET, PLA, and ABS
  17. Microstructural characteristics and mechanical properties of 3D printed Kevlar fibre reinforced Onyx composite
  18. Microstructural, mechanical and nondestructive characterization of X60 grade steel pipes welded by different processes
https://doi.org/10.1515/mt-2024-0075
Keywords for this article
crayfish algorithm; optimization; mechanical design problems; automobile component; artificial neural network

Articles in the same Issue

  1. Frontmatter
  2. Modal analysis for the non-destructive testing of brazed components
  3. Numerical analysis of cathodic protection of a Q355ND frame in a shallow water subsea Christmas tree
  4. Friction stir lap welding of AZ31B magnesium alloy to AISI 304 stainless steel
  5. Microstructure and mechanical properties of double pulse TIG welded super austenitic stainless steel butt joints
  6. Numerical and experimental investigation of impact performances of cast and stretched polymethyl methacrylate panels
  7. Mechanical properties of Sr inoculated A356 alloy by Taguchi-based gray relational analysis
  8. Influence of high-reactivity energetic materials on microstructure and performance on iron-based cladding layer under low laser power
  9. Experimental investigations and material modeling of an elastomer jaw coupling
  10. Optimal design of structural engineering components using artificial neural network-assisted crayfish algorithm
  11. A novel chaotic artificial rabbits algorithm for optimization of constrained engineering problems
  12. Numerical and experimental investigation of the effect of heat input on weld bead geometry and stresses in laser welding
  13. Influence of betel nut fiber hybridization on properties of novel aloevera fiber-reinforced vinyl ester composites
  14. Electro discharge machining performance of cryogenic heat treated copper electrode
  15. Influence of IP-TIG welding parameters on weld bead geometry, tensile properties, and microstructure of Ti6Al4V alloy joints
  16. Experimental and numerical investigation of crash performances of additively manufactured novel multi-cell crash box made with CF15PET, PLA, and ABS
  17. Microstructural characteristics and mechanical properties of 3D printed Kevlar fibre reinforced Onyx composite
  18. Microstructural, mechanical and nondestructive characterization of X60 grade steel pipes welded by different processes
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 15.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/mt-2024-0075/html
Scroll to top button