Home A novel chaotic artificial rabbits algorithm for optimization of constrained engineering problems
Article
Licensed
Unlicensed Requires Authentication

A novel chaotic artificial rabbits algorithm for optimization of constrained engineering problems

  • Erhan Duzgun

    Erhan Duzgun is a mechanical engineer, MSc, and PhD student in the Mechanical Engineering Department in Graduate School of Engineering and Science at TOBB University of Economics and Technology (TOBB ETU), Ankara, Turkey. His research interests are optimization, meta-heuristic optimization algorithms, shape and topology optimization of vehicle components and mechanical design.

         , Erdem Acar

    Dr. Erdem Acar is a professor in the Mechanical Engineering Department at TOBB University of Economics and Technology, Ankara, Turkey. His research interests include design optimization, design of automobile and aircraft structures (in particular, composite structures), finite element analysis, ballistic simulations, and uncertainty analysis. He is an associate fellow of the American Institute of Aeronautics and Astronautics, and he has been serving as a review editor for the Structural and Multidisciplinary Optimization Journal, Springer, since 2017.

         and Ali Riza Yildiz

    Dr. Ali Riza Yildiz 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
Published/Copyright: May 28, 2024
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Materials Testing
From the journal Materials Testing Volume 66 Issue 9

Abstract

This study introduces a novel metaheuristic algorithm of optimization named Chaotic Artificial Rabbits Optimization (CARO) algorithm for resolving engineering design problems. In the newly introduced CARO algorithm, ten different chaotic maps are used with the recently presented Artificial Rabbits Optimization (ARO) algorithm to manage its parameters, eventually leading to an improved exploration and exploitation of the search. The CARO algorithm and familiar metaheuristic competitor algorithms were experimented on renowned five mechanical engineering problems of design, in brief; pressure vessel design, rolling element bearing design, tension/compression spring design, cantilever beam design and gear train design. The results indicate that the CARO is an outstanding algorithm compared with the familiar metaheuristic algorithms, and equipped with the best-optimized parameters with the minimal deviation in each case study. Metaheuristic algorithms are utilized to succeed in an optimal design in engineering problems targeting to achieve lightweight designs. In this present study, the optimum design of a vehicle brake pedal piece was achieved through topology and shape optimization methods. The brake pedal optimization problem in terms of the mass minimization is solved properly by using the CARO algorithm in comparison to familiar metaheuristic algorithms in the literature. Consequently, results indicate that the CARO algorithm can be effectively utilized in the optimal design of engineering problems.

Keywords: metaheuristic optimization; chaotic artificial rabbits optimization algorithm; topology optimization; shape optimization; chaotic maps; mechanical design; brake pedal
Corresponding author: Ali Riza Yildiz, Department of Mechanical Engineering, 37523 Bursa Uludag University , Görükle, Bursa, 16059, Türkiye, E-mail:

About the authors

Erhan Duzgun

Erhan Duzgun is a mechanical engineer, MSc, and PhD student in the Mechanical Engineering Department in Graduate School of Engineering and Science at TOBB University of Economics and Technology (TOBB ETU), Ankara, Turkey. His research interests are optimization, meta-heuristic optimization algorithms, shape and topology optimization of vehicle components and mechanical design.

Erdem Acar

Dr. Erdem Acar is a professor in the Mechanical Engineering Department at TOBB University of Economics and Technology, Ankara, Turkey. His research interests include design optimization, design of automobile and aircraft structures (in particular, composite structures), finite element analysis, ballistic simulations, and uncertainty analysis. He is an associate fellow of the American Institute of Aeronautics and Astronautics, and he has been serving as a review editor for the Structural and Multidisciplinary Optimization Journal, Springer, since 2017.

Ali Riza Yildiz

Dr. Ali Riza Yildiz 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.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: Not applicable.

References

[1] W. Wong and C. I. Ming, “A review on metaheuristic algorithms: recent trends, benchmarking and applications,” in 2019 7th International Conf. on Smart Computing & Communications (ICSCC), 2019.10.1109/ICSCC.2019.8843624Search in Google Scholar

[2] A. H. Halim, I. Ismail, and S. Das, “Performance assessment of the metaheuristic optimization algorithms: an exhaustive review,” Artif. Intell. Rev., 2020, https://doi.org/10.1007/s10462-020-09906-6.Search in Google Scholar

[3] M. Abdel-Basset, L. Abdel-Fatah, and A. K. Sangaiah, in Chapter 10 – Metaheuristic Algorithms: A Comprehensive Review, A. K. Sangaiah, M. Sheng and Z. Zhang, Academic Press, Netherlands, 2018. Available at: https://www.sciencedirect.com/science/article/pii/B9780128133149000104.10.1016/B978-0-12-813314-9.00010-4Search in Google Scholar

[4] J. Dréo, A. Pétrowski, P. Siarry, and E. Taillard, Metaheuristics for Hard Optimization: Methods and Case Studies, Berlin, Springer Science & Business Media, 2006.Search in Google Scholar

[5] L. Abualigah, et al.., “Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results,” Neural Comput. Appl., pp. 1–30, 2022, https://doi.org/10.1007/s00521-021-06747-4.Search in Google Scholar

[6] N. Lynn and P. N. Suganthan, “Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation,” Swarm Evol. Comput., vol. 24, pp. 11–24, 2015, https://doi.org/10.1016/j.swevo.2015.05.002.Search in Google Scholar

[7] A. H. Gandomi, X. S. Yang, S. Talatahari, and A. H. Alavi, “Metaheuristic algorithms in modeling and optimization,” Metaheuristic Appl. Struct. Infrastruct., vol. 1, pp. 1–24, 2013, https://doi.org/10.1016/b978-0-12-398364-0.00001-2.Search in Google Scholar

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

[9] J. H. Holland, Adaptation in Natural and Artificial Systems, The MIT Press, 1992.10.7551/mitpress/1090.001.0001Search in Google Scholar

[10] R. Storn and K. Price, “Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces,” J. Global Optim., vol. 11, no. 4, pp. 341–359, 1997, https://doi.org/10.1023/a:1008202821328.10.1023/A:1008202821328Search in Google Scholar

[11] H.-G. Beyer and H.-P. Schwefel, “Evolution strategies – a comprehensive introduction,” Nat. Comput., vol. 1, no. 1, pp. 3–52, 2002, https://doi.org/10.1023/a:1015059928466.10.1023/A:1015059928466Search in Google Scholar

[12] S. H. Jung, “Queen-bee evolution for genetic algorithms,” Electron. Lett., vol. 39, no. 6, pp. 575–576, 2003, https://doi.org/10.1049/el:20030383.10.1049/el:20030383Search in Google Scholar

[13] B. Nouri-Moghaddam, M. Ghazanfari, and M. Fathian, “A novel multi-objective forest optimization algorithm for wrapper feature selection,” Expert Syst. Appl., vol. 175, p. 114737, 2021, https://doi.org/10.1016/j.eswa.2021.114737.Search in Google Scholar

[14] S. Mirjalili, “SCA: a sine cosine algorithm for solving optimization problems,” Knowl.-Based Syst., vol. 96, pp. 120–133, 2016, https://doi.org/10.1016/j.knosys.2015.12.022.Search in Google Scholar

[15] G.-G. Wang, S. Deb, and Z. Cui, “Monarch butterfly optimization,” Neural Comput. Appl., vol. 31, no. 7, pp. 1995–2014, 2015, https://doi.org/10.1007/s00521-015-1923-y.Search in Google Scholar

[16] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS’95, IEEE, 1995, pp. 39–43.10.1109/MHS.1995.494215Search in Google Scholar

[17] D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, 2009, https://doi.org/10.1016/j.amc.2009.03.090.Search in Google Scholar

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

[19] E. H. de Vasconcelos Segundo, V. C. Mariani, and L. dos S. Coelho, “Design of heat exchangers using Falcon optimization algorithm,” Appl. Therm. Eng., vol. 156, pp. 119–144, 2019a, https://doi.org/10.1016/j.applthermaleng.2019.04.038.Search in Google Scholar

[20] V. Hayyolalam and A. A. Pourhaji Kazem, “Black Widow Optimization Algorithm: a novel meta-heuristic approach for solving engineering optimization problems,” Eng. Appl. Artif. Intell., vol. 87, p. 103249, 2020, https://doi.org/10.1016/j.engappai.2019.103249.Search in Google Scholar

[21] D. Połap and M. Woz´niak, “Polar bear optimization algorithm: meta-heuristic with fast population movement and dynamic birth and death mechanism,” Symmetry vol. 9, no. 10, p. 203, 2017, https://doi.org/10.3390/sym9100203.Search in Google Scholar

[22] E. H. de Vasconcelos Segundo, V. C. Mariani, and L. dos S. Coelho, “Metaheuristic inspired on owls behavior applied to heat exchangers design,” Therm. Sci. Eng. Prog., vol. 14, p. 100431, 2019b, https://doi.org/10.1016/j.tsep.201o.Search in Google Scholar

[23] C. Klein, V. Mariani, and L. Coelho, “Cheetah based optimization algorithm: a novel swarm intelligence paradigm,” in European Symposium on Artificial Neural Networks, 2018.Search in Google Scholar

[24] J. Pierezan and L. Dos Santos Coelho, “Coyote optimization algorithm: a new metaheuristic for global optimization problems,” in IEEE Congress on Evolutionary Computation (CEC), IEEE, 2018, pp. 1–8, https://doi.org/10.1109/CEC.2018.8477769.Search in Google Scholar

[25] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, “GSA: a gravitational search algorithm,” Inf. Sci., vol. 179, no. 13, pp. 2232–2248, 2009, https://doi.org/10.1016/j.ins.2009.03.004.Search in Google Scholar

[26] B. Alatas, “ACROA: artificial chemical reaction optimization algorithm for global optimization,” Expert Syst. Appl., vol. 38, no. 10, pp. 13170–13180, 2011, https://doi.org/10.1016/j.eswa.2011.04.126.Search in Google Scholar

[27] W. Zhao, L. Wang, and Z. Zhang, “Atom search optimization and its application to solve a hydrogeologic parameter estimation problem,” Knowl. Base Syst., vol. 163, pp. 283–304, 2019a, https://doi.org/10.1016/j.knosys.2018.08.030.Search in Google Scholar

[28] D. Mohammadi, T. Seppänen, R. Moghdani, T. Van Woensel, and S. Mirjalili, “Quantum Henry gas solubility optimization algorithm for global optimization,” Eng. Comput., vol. 38, no. S3, pp. 2329–2348, 2021, https://doi.org/10.1007/s00366-021-01347-1.Search in Google Scholar

[29] B. Javidy, A. Hatamlou, and S. Mirjalili, “Ions motion algorithm for solving optimization problems,” Appl. Soft Comput., vol. 32, pp. 72–79, 2015, https://doi.org/10.1016/j.asoc.2015.03.035.Search in Google Scholar

[30] A. Kaveh and B. Liu, “Water Evaporation Optimization: a novel physically inspired optimization algorithm,” Comput. Struct., vol. 167, pp. 69–85, 2016, https://doi.org/10.1016/j.compstruc.2016.01.008.Search in Google Scholar

[31] H. Eskandar, A. Sadollah, A. Bahreininejad, and M. Hamdi, “Water cycle algorithm – a novel metaheuristic optimization method for solving constrained engineering optimization problems,” Comput. Struct., vols. 110–111, pp. 151–166, 2012, https://doi.org/10.1016/j.compstruc.2012.07.010.Search in Google Scholar

[32] D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67–82, 1997, https://doi.org/10.1109/4235.585893.Search in Google Scholar

[33] A. Faramarzi, M. Heidarinejad, B. Stephens, and S. Mirjalili, “Equilibrium optimizer: a novel optimization algorithm,” Knowl. Base Syst., vol. 191, p. 105190, 2020, https://doi.org/10.1016/j.knosys.2019.105190.Search in Google Scholar

[34] L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, and W. Zhao, “Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems,” Eng. Appl. Artif. Intell., vol. 114, p. 105082, 2022, https://doi.org/10.1016/j.engappai.2022.105082.Search in Google Scholar

[35] Y. Wang, L. Huang, J. Zhong, and G. Hu, “LARO: opposition-based learning boosted artificial rabbits-inspired optimization algorithm with Lévy flight,” Symmetry, vol. 14, no. 11, p. 2282, 2022, https://doi.org/10.3390/sym14112282.Search in Google Scholar

[36] A. Mazloumi, A. Poolad, M. S. Mokhtari, M. B. Altman, A. Y. Abdelaziz, and M. Elsisi, “Optimal sizing of a photovoltaic pumping system integrated with water storage tank considering cost/reliability assessment using enhanced artificial rabbits optimization: a case study,” Mathematics, vol. 11, no. 2, p. 463, 2023, https://doi.org/10.3390/math11020463.Search in Google Scholar

[37] Y. Wang, Y. Xiao, Y. Guo, and J. Li, “Dynamic chaotic opposition-based learning-driven hybrid Aquila optimizer and artificial rabbits optimization algorithm: framework and applications,” Processes, vol. 10, no. 12, p. 2703, 2022, https://doi.org/10.3390/pr10122703.Search in Google Scholar

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

[39] A. Kaveh, “Chaos embedded metaheuristic algorithms,” Adv. Metaheuristic Algorithms Opt. Des. Struct., pp. 375–398, 2016, https://doi.org/10.1007/978-3-319-46173-1_12.Search in Google Scholar

[40] A. G. Hussien and M. Amin, “A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection,” Int. J. Mach. Learn. Cybern., vol. 13, 2021, https://doi.org/10.1007/s13042-021-01326-4.Search in Google Scholar

[41] R. C. Hilborn, Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers, Oxford Univ. Press, 2000.10.1093/acprof:oso/9780198507239.001.0001Search in Google Scholar

[42] A. Kaveh, R. Mahdipour Moghanni, and S. M. Javadi, “Chaotic optimization algorithm for performance-based optimization design of composite moment frames,” Eng. Comput., 2021, https://doi.org/10.1007/s00366-020-01244-z.Search in Google Scholar

[43] A. Erramilli, R. P. Singh, and P. Pruthi, “An application of deterministic chaotic maps to model packet traffic,” Queueing Syst., vol. 20, nos. 1–2, pp. 171–206, 1995, https://doi.org/10.1007/bf01158436.Search in Google Scholar

[44] S. Talatahari, A. Kaveh, and R. Sheikholeslami, “An efficient charged system search using chaos for global optimization problems,” Int. J. Optim. Civil Eng., vol. 1, no. 2, pp. 305–325, 2011.Search in Google Scholar

[45] A. GálvezTomida, “Matlab toolbox and GUI for analyzing one-dimensional chaotic maps,” in 2008 International Conference on Computational Sciences and Its Applications, 2008.10.1109/ICCSA.2008.7Search in Google Scholar

[46] R. L. Devaney, An Introduction to Chaotic Dynamical Systems, New York, Addison-Wesley, 1987.10.1063/1.2820117Search in Google Scholar

[47] H. Peitgen, H. Jurgens, and D. Saupe, Chaos and Fractals, New York, Springer-Verlag, 1992.10.1007/978-1-4757-4740-9Search in Google Scholar

[48] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014, https://doi.org/10.1016/j.advengsoft.2013.12.007.Search in Google Scholar

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

[50] B. K. Kannan and S. N. Kramer, “An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design,” J. Mech. Des., vol. 116, no. 2, pp. 405–411, 1994, https://doi.org/10.1115/1.2919393.Search in Google Scholar

[51] B. Rajeswara Rao and R. Tiwari, “Optimum design of rolling element bearings using genetic algorithms,” Mech. Mach. Theory, vol. 42, no. 2, pp. 233–250, 2007, https://doi.org/10.1016/j.mechmachtheory.2006.02.004.Search in Google Scholar

[52] 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

[53] 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 Des., vol. 43, no. 3, pp. 303–315, 2011, https://doi.org/10.1016/j.cad.2010.12.015.Search in Google Scholar
Published Online: 2024-05-28
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-2024-0097
Keywords for this article
metaheuristic optimization; chaotic artificial rabbits optimization algorithm; topology optimization; shape optimization; chaotic maps; mechanical design; brake pedal

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 25.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/mt-2024-0097/html
Scroll to top button