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A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems

  • Pranav Mehta

    Mr. Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387,001, Gujarat, India. He is currently a Ph.D. research scholar with the Dharmsinh Desai University, Nadiad, Gujarat, India. His major research interest includes metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

         , Betül Sultan Yıldız

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

         , Nantiwat Pholdee

    Nantiwat Pholdee received his BEng degree (Second Class Honors) in Mechanical Engineering in 2008 and his Ph.D. degree in Mechanical Engineering in 2013 from KhonKaen University, KhonKaen, Thailand. His research interests include multidisciplinary design optimization, aircraft design, flight control, evolutionary computation, and finiteelement analysis.

         , Sumit Kumar

    Sumit Kumar received the BEng degree(Hons.) in mechanical engineering from Dr. A.P.J.AbdulKalam Technical University, Lucknow, India, in 2012, and the MEng degree (Hons.) in design engineering from the MalaviyaNationalInstitute of Technology (NIT), Jaipur, India, in 2015. He is currently a Ph.D. research scholar with the College of Sciences and Engineering, Australian Maritime College, University of Tasmania, Launceston, Australia. His major research interests include metaheuristics techniques, multi-objective optimization, evolutionary algorithm, and renewable energy systems.

         , 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     , 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.

         and Sujin Bureerat

    Dr. Sujin Bureerat received his BEng degree in Mechanical Engineering from KhonKaen University, KhonKaen, Thailand, in 1992, and his Ph.D. degree in Engineering from Manchester University, Manchester, UK, in 2001. Currently, he is a Professor in the Department of Mechanical Engineering, Khon Kaen University. His research interests include multidisciplinary design optimization, evolutionary computation, aircraft design, finite-element analysis, agricultural machinery, mechanism synthesis, and mechanical vibration.
Published/Copyright: February 3, 2023
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Materials Testing
From the journal Materials Testing Volume 65 Issue 2

Abstract

Optimization of engineering discipline problems are quite a challenging task as they carry design parameters and various constraints. Metaheuristic algorithms can able to handle those complex problems and realize the global optimum solution for engineering problems. In this article, a novel generalized normal distribution algorithm that is integrated with elite oppositional-based learning (HGNDO-EOBL) is studied and employed to optimize the design of the eight benchmark engineering functions. Moreover, the statistical results obtained from the HGNDO-EOBL are collated with the data obtained from the well-established algorithms such as whale optimizer, salp swarm optimizer, LFD optimizer, manta ray foraging optimization algorithm, hunger games search algorithm, reptile search algorithm, and INFO algorithm. For each of the cases, a comparison of the statistical results suggests that HGNDO-EOBL is superior in terms of realizing the prominent values of the fitness function compared to established algorithms. Accordingly, the HGNDO-EOBL can be adopted for a wide range of engineering optimization problems.

Keywords: engineering design problems; generalized normal distribution optimizer HGNDO-EOBL; Metaheursitcs algorithm
Corresponding author: Ali Riza Yildiz, Department of Mechanical Engineering, Bursa Uludag University, Uludağ University, Görükle Bursa, Bursa, 16059 Bursa, Türkiye, E-mail:

Funding source: Bursa Uludag University Scientific Research Projects Center (BAP)

Award Identifier / Grant number: FGA-2022-1192

About the authors

Pranav Mehta

Mr. Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387,001, Gujarat, India. He is currently a Ph.D. research scholar with the Dharmsinh Desai University, Nadiad, Gujarat, India. His major research interest includes metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

Betül Sultan Yıldız

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

Nantiwat Pholdee

Nantiwat Pholdee received his BEng degree (Second Class Honors) in Mechanical Engineering in 2008 and his Ph.D. degree in Mechanical Engineering in 2013 from KhonKaen University, KhonKaen, Thailand. His research interests include multidisciplinary design optimization, aircraft design, flight control, evolutionary computation, and finiteelement analysis.

Sumit Kumar

Sumit Kumar received the BEng degree(Hons.) in mechanical engineering from Dr. A.P.J.AbdulKalam Technical University, Lucknow, India, in 2012, and the MEng degree (Hons.) in design engineering from the MalaviyaNationalInstitute of Technology (NIT), Jaipur, India, in 2015. He is currently a Ph.D. research scholar with the College of Sciences and Engineering, Australian Maritime College, University of Tasmania, Launceston, Australia. His major research interests include metaheuristics techniques, multi-objective optimization, evolutionary algorithm, and renewable energy systems.

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.

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.

Sujin Bureerat

Dr. Sujin Bureerat received his BEng degree in Mechanical Engineering from KhonKaen University, KhonKaen, Thailand, in 1992, and his Ph.D. degree in Engineering from Manchester University, Manchester, UK, in 2001. Currently, he is a Professor in the Department of Mechanical Engineering, Khon Kaen University. His research interests include multidisciplinary design optimization, evolutionary computation, aircraft design, finite-element analysis, agricultural machinery, mechanism synthesis, and mechanical vibration.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work was supported by Bursa Uludag University Scientific Research Projects Center (BAP) FGA-2022-1192.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] A. Kaveh, H. Akbari, and S. M. Hosseini, “Plasma generationoptimization: a new physically-based metaheuristic algorithmfor solving constrained optimization problems,” Eng. Comput., vol. 38, no. 4, pp. 1554–1606, 2021, https://doi.org/10.1108/EC-05-2020-0235.Search in Google Scholar

[2] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, “Artificial gorilla troops optimizer: a new nature-inspiredmetaheuristic algorithm for global optimization problems,” Int. J. Intell. Syst., vol. 36, no. 10, pp. 5887–5958, 2021, https://doi.org/10.1002/int.22535.Search in Google Scholar

[3] N. Panagant, N. Pholdee, S. Bureerat, K. Kaen, A. R. Yildiz, and S. M. Sait. “Seagull optimization algorithm for solving real-world design optimization problems,” Mater. Test., vol. 62, no. 6, pp. 640–644, 2020, https://doi.org/10.3139/120.111529.Search in Google Scholar

[4] K. Zervoudakis and S. Tsafarakis, “A mayfly optimization algorithm,” Comput. Ind. Eng., vol. 145, no. 106559, 2020, https://doi.org/10.1016/j.cie.2020.106559.Search in Google Scholar

[5] S. Talatahari and M. Azizi, “Chaos game optimization: a novelmetaheuristic algorithm,” Artif. Intell. Rev., vol. 54, no. 2, pp. 917–1004, 2021, https://doi.org/10.1007/s10462-020-09867-w.Search in Google Scholar

[6] F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, and W. Al-Atabany, “Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems,” Appl. Intell., vol. 51, no. 3, pp. 1531–1551, 2021, https://doi.org/10.1007/s10489-020-01893-z.Search in Google Scholar

[7] O. N. Oyelade, A. E.-S. Ezugwu, T. I. A. Mohamed, and L. Abualigah, “Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm,” IEEE Access, vol. 10, pp. 16150–16177, 2022, https://doi.org/10.1109/ACCESS.2022.3147821.Search in Google Scholar

[8] L. Abualigah, M. A. Elaziz, P. Sumari, Z. W. Geem, and A. H. Gandomi, “Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer,” Expert Syst. Appl., vol. 191, 2022, Art. no. 116158, https://doi.org/10.1016/j.eswa.2021.116158.Search in Google Scholar

[9] J. O. Agushaka, A. E. Ezugwu, and L. Abualigah, “Dwarf mongoose optimization algorithm,” Comput. Methods Appl. Mech. Eng., vol. 391, 2022, Art. no. 114570, https://doi.org/10.1016/j.cma.2022.114570.Search in Google Scholar

[10] Y. Zhong, L. Jian, and W. Zijun, “An integrated optimization algorithm of GA and ACA-based approaches for modeling virtual enterprise partner selection,” SIGMIS Database, vol. 40, no. 2, pp. 37–56, 2009, https://doi.org/10.1145/1531817.1531824.Search in Google Scholar

[11] P. Mehta, B. S. Yildiz, S. M. Sait, and A. R. Yildiz, “Gradient-based optimizer for economic optimization of engineering problems,” Mater. Test., vol. 64, no. 5, pp. 690–696, 2022, https://doi.org/10.1515/mt-2022-0055.Search in Google Scholar

[12] N. Öztürk, A. R. Yildiz, N. Kaya, and F. Öztürk, “Neuro-genetic design optimization framework to support the integrated robust design optimization process in CE,” Concurr. Eng. Res. Appl., vol. 14, no. 1, pp. 5–16, 2006, https://doi.org/10.1177/1063293X06063314.Search in Google Scholar

[13] A. R. Yildiz and F. Ozturk, “Hybrid enhanced genetic algorithm to select optimal machiningparameters in turning operation,” Proc. Instn.Mech. Engrs, Part B, J. Eng. Manufact., vol. 220, no. 12, pp. 2041–2053, 2006, https://doi.org/10.1243/09544054JEM570.Search in Google Scholar

[14] T. Güler, E. Demirci, A. R. Yildiz, and U. Yavuz, “Lightweight design of an automobile hinge component using glass fiber polyamide composites,” Mater. Test., vol. 60, no. 3, pp. 306–310, 2018, https://doi.org/10.3139/120.111152.Search in Google Scholar

[15] A. R. Yildiz and M. U. Erdaş, “A new hybrid taguchi-salp swarm optimization algorithm for the robust design of real-world engineering problems,” Mater. Test., vol. 63, no. 2, pp. 157–162, 2021, https://doi.org/10.1515/mt-2020-0022.Search in Google Scholar

[16] B. S. Yildiz, N. Pholdee, S. Bureerat, M. U. Erdaş, A. R. Yildiz, and S. M. Sait, “Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry,” Mater. Test., vol. 63, no. 4, pp. 356–359, 2021, https://doi.org/10.1515/mt-2020-0053.Search in Google Scholar

[17] B. S. Yildiz, N. Pholdee, S. Bureerat, A. R. Yildiz, and S. M. Sait, “Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm,” Exp. Syst., vol. 38, no. 3, 2021, Art. no. e12666, https://doi.org/10.1111/exsy.12666.Search in Google Scholar

[18] A. R. Yildiz, N. Kaya, N. Öztürk, and F. Öztürk, “Hybrid approach for genetic algorithm and Taguchi’s method based design optimization in the automotive industry,” Int. J. Prod. Res., vol. 44, no. 22, pp. 4897–4914, 2006, https://doi.org/10.1080/00207540600619932.Search in Google Scholar

[19] D. Gürses, S. Bureerat, S. M. Sait, and A. R. Yildiz, “Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications,” Mater. Test., vol. 63, no. 5, pp. 448–452, 2021, https://doi.org/10.1515/mt-2020-0076.Search in Google Scholar

[20] A. R. Yildiz and F. Öztürk, “Hybrid Taguchi-Harmony search approach for shape optimization,” Recent Adv. Harmony Search Algorithm Stud Comput. Intell., vol. 270, pp. 89–93, 2010, https://doi.org/10.1007/978-3-642-04317-8_8.Search in Google Scholar

[21] E. Demirci and A. R. Yildiz, “An experimental and numerical investigation of the effects of geometry and spot welds on the crashworthiness of vehicle thin-walled structures,” Mater. Test., vol. 60, no. 6, pp. 553–561, 2018, https://doi.org/10.3139/120.111187.Search in Google Scholar

[22] C. M. Aye, N. Pholdee, A. R. Yildiz, S. Bureerat, and S. M. Sait, “Multi-surrogate-assisted metaheuristics for crashworthiness optimisation,” Int. J. Veh. Des., vol. 80, nos. 2–4, pp. 223–240, 2021, https://doi.org/10.1504/IJVD.2019.109866.Search in Google Scholar

[23] B. S. Yildiz, A. R. Yildiz, E. I. Albak, H. Abderazek, S. M. Sait, and S. Bureerat, “Butterfly optimization algorithm for optimum shape design of automobile suspension components,” Mater. Test., vol. 62, no. 4, pp. 365–370, 2020, https://doi.org/10.3139/120.111492.Search in Google Scholar

[24] S. Arora and S. Singh, “Butterfly optimization algorithm: a novel approach for global optimization,” Soft Comput., vol. 23, no. 3, pp. 715–734, 2019, https://doi.org/10.1007/s00500-018-3102-4.Search in Google Scholar

[25] A. R. Yildiz, H. Abderazek, and S. Mirjalili, “A comparative study of recent non-traditional methods for mechanical design optimization,” Arch. Comput. Methods Eng., vol. 27, no. 4, pp. 1031–1048, 2020, https://doi.org/10.1007/s11831-019-09343-x.Search in Google Scholar

[26] D. Dhawale, V. K. Kamboj, and P. Anand, “An improved chaotic Harris hawks optimizer for solving numerical and engineering optimization problems,” Eng. Comput., vol. 44, no. 22, pp. 4897–4914, 2021, https://doi.org/10.1007/s00366-021-01487-4.Search in Google Scholar

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

[28] S. Gupta, H. Abderazek, B. S. Yildiz, A. R. Yildiz, S. Mirjalili, S. M. Sait, “Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems,” Expert Syst. Appl.,vol. 183,p. 115351, 2021, https://doi.org/10.1016/j.eswa.2021.115351.Search in Google Scholar

[29] V. Ho-Huu, T. Nguyen-Thoi, T. Vo-Duy, and T. Nguyen-Trang, “An adaptive elitist differential evolution for optimization of truss structures with discrete design variables,” Comput. Struct., vol. 165, pp. 59–75, 2016, https://doi.org/10.1016/j.compstruc.2015.11.014.Search in Google Scholar

[30] 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

[31] S. Li, H. Chen, M. Wang, A. A. Heidari, and S. Mirjalili, “Slime mould algorithm: a new method for stochastic optimization,” Future Generat. Comput. Syst., vol. 111, pp. 300–323, 2020, https://doi.org/10.1016/j.future.2020.03.055.Search in Google Scholar

[32] A. Sadollah, A. Bahreininejad, H. Eskandar, and M. Hamdi, “Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems,” Appl. Soft Comput., vol. 13, no. 5, pp. 2592–2612, 2013, https://doi.org/10.1016/j.asoc.2012.11.026.Search in Google Scholar

[33] A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, “Marine predators algorithm: a nature-inspired metaheuristic,” Expert Syst. Appl., vol. 152, 2020, Art. no. 113377, https://doi.org/10.1016/j.eswa.2020.113377.Search in Google Scholar

[34] S. O. Degertekin, L. Lamberti, and I. B. Ugur, “Discrete sizing/layout/topology optimization of truss structures with an advanced Jaya algorithm,” Appl. Soft Comput., vol. 79, pp. 363–390, 2019, https://doi.org/10.1016/j.asoc.2019.03.058.Search in Google Scholar

[35] X. Yao, “A new simulated annealing algorithm,” Int. J. Comput. Math., vol. 56, nos. 3–4, pp. 161–168, 1995, https://doi.org/10.1080/00207169508804397.Search in Google Scholar

[36] M.-Y. Cheng, D. Prayogo, Y.-W. Wu, and M. M. Lukito, “A Hybrid Harmony Search algorithm for discrete sizing optimization of truss structure,” Autom. ConStruct., vol. 69, pp. 21–33, 2016, https://doi.org/10.1016/j.autcon.2016.05.023.Search in Google Scholar

[37] Y. Zhou, R. Wang, and Q. Luo, “Elite opposition-based flower pollination algorithm,” Neurocomputing, vol. 188, pp. 294–310, 2016, https://doi.org/10.1016/j.neucom.2015.01.110.Search in Google Scholar

[38] B. S. Yildiz, N. Pholdee, S. Bureerat, A. R. Yildiz, and S. M. Sait, “Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems,” Eng. Comput., vol. 38, no. 5, pp. 4207–4219, 2021, https://doi.org/10.1007/s00366-021-01368-w.Search in Google Scholar

[39] A. R. Yildiz, N. Kaya, F. Öztürk, and O. Alankus, “Optimal design of vehicle components using topology design and optimisation,” Int. J. Veh. Des., vol. 34, no. 4, pp. 387–398, 2004, https://doi.org/10.1504/IJVD.2004.004064.Search in Google Scholar

[40] Y. Zhang, Z. Jin, and S. Mirjalili, “Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models,” Energy Convers. Manage., vol. 224, 2020, Art. no. 113301, https://doi.org/10.1016/j.enconman.2020.113301.Search in Google Scholar

[41] M. Yıldız, N. Panagant, N. Pholdee, S. Bureerat, S. Sait, and A. R. Yildiz, “Hybrid Taguchi-Lévy flight distribution optimization algorithm for solving real-world design optimization problems,” Mater. Test., vol. 63, no. 6, pp. 547–551, 2021, https://doi.org/10.1515/mt-2020-0091.10.1515/mt-2020-0091Search in Google Scholar
Published Online: 2023-02-03
Published in Print: 2023-02-23

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Effect of cyclic loading on mechanical properties and microstructure of die cast magnesium alloy AZ91D
  3. Selective laser melting of Ti6Al4V alloy: effects of process parameters at constant energy density on mechanical properties, residual stress, microstructure and relative density
  4. A comparison study on experimental characterization of unidirectional fiber reinforced composites using strain-gauges and virtual extensometers
  5. A modified Johnson–Cook constitutive model for titanium alloy TA31 and its implementation into FE
  6. Anisotropy of epoxy acrylate with magnetic field-induced Ni-MWNTs
  7. A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems
  8. Improvement of metallurgical properties of A356 aluminium alloy by AlCrFeSrTiBSi master alloy
  9. Precipitation of carbides in a nickel-based cast heat-resistant alloy during thermal exposure: evolution of microstructure, hardness and corrosion properties
  10. Mechanical property improvement of a AA6082 alloy by the TV-CAP process as a novel SPD method
  11. Effect of high temperatures on dry sliding friction and wear behaviour of CuCrZr copper alloy
  12. Joint performance of medium carbon steel-austenitic stainless steel double-sided TIG welds
  13. Enhancing the wear performance of Ti-6Al-4V against Al2O3 and WC-6Co via TiBn layer produced by boriding
  14. Cutting parameters optimization of hybrid fiber composite during drilling
  15. Dry sliding wear behavior of energy density dependent PA 12/Cu composites produced by selective laser sintering
https://doi.org/10.1515/mt-2022-0259
Keywords for this article
engineering design problems; generalized normal distribution optimizer HGNDO-EOBL; Metaheursitcs algorithm

Articles in the same Issue

  1. Frontmatter
  2. Effect of cyclic loading on mechanical properties and microstructure of die cast magnesium alloy AZ91D
  3. Selective laser melting of Ti6Al4V alloy: effects of process parameters at constant energy density on mechanical properties, residual stress, microstructure and relative density
  4. A comparison study on experimental characterization of unidirectional fiber reinforced composites using strain-gauges and virtual extensometers
  5. A modified Johnson–Cook constitutive model for titanium alloy TA31 and its implementation into FE
  6. Anisotropy of epoxy acrylate with magnetic field-induced Ni-MWNTs
  7. A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems
  8. Improvement of metallurgical properties of A356 aluminium alloy by AlCrFeSrTiBSi master alloy
  9. Precipitation of carbides in a nickel-based cast heat-resistant alloy during thermal exposure: evolution of microstructure, hardness and corrosion properties
  10. Mechanical property improvement of a AA6082 alloy by the TV-CAP process as a novel SPD method
  11. Effect of high temperatures on dry sliding friction and wear behaviour of CuCrZr copper alloy
  12. Joint performance of medium carbon steel-austenitic stainless steel double-sided TIG welds
  13. Enhancing the wear performance of Ti-6Al-4V against Al2O3 and WC-6Co via TiBn layer produced by boriding
  14. Cutting parameters optimization of hybrid fiber composite during drilling
  15. Dry sliding wear behavior of energy density dependent PA 12/Cu composites produced by selective laser sintering
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