Startseite A novel hybrid Fick’s law algorithm-quasi oppositional–based learning algorithm for solving constrained mechanical design problems
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

A novel hybrid Fick’s law algorithm-quasi oppositional–based learning algorithm for solving constrained mechanical design problems

  • 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 interest includes metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

         , Betül Sultan Yildiz

    Dr. Betül Sultan Yildiz completed her BSc and MSc degrees at Uludağ University, Bursa, Turkey, and received her Ph.D. in Mechanical Engineering from Bursa Technical University, Turkey. Her research interests are optimal design, shape optimization, topology optimization, topography optimization, structural optimization methods, meta-heuristic optimization algorithms, and applications to industrial problems.

     ORCID logo 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.

         und 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.
Veröffentlicht/Copyright: 13. September 2023
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Materials Testing
Aus der Zeitschrift Materials Testing Band 65 Heft 12

Abstract

In this article, a recently developed physics-based Fick’s law optimization algorithm is utilized to solve engineering optimization challenges. The performance of the algorithm is further improved by incorporating quasi-oppositional–based techniques at the programming level. The modified algorithm was applied to optimize the rolling element bearing system, robot gripper, planetary gear system, and hydrostatic thrust bearing, along with shape optimization of the vehicle bracket system. Accordingly, the algorithm realizes promising statistical results compared to the rest of the well-known algorithms. Furthermore, the required number of iterations was comparatively less required to attain the global optimum solution. Moreover, deviations in the results were the least even when other optimizers provided better or more competitive results. This being said that this optimization algorithm can be adopted for a critical and wide range of industrial and real-world challenges optimization.

Keywords: Fick’s law algorithm; mechanical design; bearing systems; vehicle bearing system; design algorithm comparison
Corresponding author: Betül Sultan Yildiz, Department of Mechanical Engineering, Bursa Uludag University, Görükle Bursa, Bursa 16059, Türkiye, E-mail:

About the authors

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 interest includes metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

Betül Sultan Yildiz

Dr. Betül Sultan Yildiz completed her BSc and MSc degrees at Uludağ University, Bursa, Turkey, and received her Ph.D. in Mechanical Engineering from Bursa Technical University, Turkey. Her research interests are optimal design, shape optimization, topology optimization, topography optimization, structural optimization methods, meta-heuristic optimization algorithms, and applications to industrial problems.

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.

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] M. Abdel-Basset, L. Abdel-Fatah, and A. K. Sangaiah, “Metaheuristic algorithms: a comprehensive review,” in Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, Cambridge, Elsevier, 2018, pp. 185–231.10.1016/B978-0-12-813314-9.00010-4Suche in Google Scholar

[2] S. Kumar, G. G. Tejani, N. Pholdee, S. Bureerat, and P. Mehta, “Hybrid heat transfer search and passing vehicle search optimizer for multi-objective structural optimization,” Knowl. Base Syst., vol. 212, p. 106556, 2021, https://doi.org/10.1016/j.knosys.2020.106556.Suche in Google Scholar

[3] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002, https://doi.org/10.1109/4235.996017.Suche in Google Scholar

[4] S. Kumar, B. S. Yildiz, P. Mehta, et al.., “Chaotic marine predators algorithm for global optimization of real-world engineering problems,” Knowl. Base Syst., vol. 261, p. 110192, 2023, https://doi.org/10.1016/j.knosys.2022.110192.Suche in Google Scholar

[5] I. FisterJr, X. S. Yang, I. Fister, J. Brest, and D. Fister, “A brief review of nature-inspired algorithms for optimization,” arXiv preprint arXiv:1307.4186, 2013.Suche in Google Scholar

[6] H. Zang, S. Zhang, and K. Hapeshi, “A review of nature-inspired algorithms,” J. Bionic Eng., vol. 7, no. S4, pp. S232–S237, 2010, https://doi.org/10.1016/S1672-6529(09)60240-7.Suche in Google Scholar

[7] A. R. Yildiz and P. Mehta, “Manta ray foraging optimization algorithm and hybrid Taguchi salp swarm-Nelder–Mead algorithm for the structural design of engineering components,” Mater. Test., vol. 64, no. 5, pp. 706–713, 2022, https://doi.org/10.1515/mt-2022-0012.Suche in Google Scholar

[8] G. G. Tejani, V. J. Savsani, and V. K. Patel, “Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization,” J. Comput. Des. Eng., vol. 3, no. 3, pp. 226–249, 2016, https://doi.org/10.1016/j.jcde.2016.02.003.Suche in Google Scholar

[9] B. S. Yıldız, P. Mehta, N. Panagant, S. Mirjalili, and A. R. Yildiz, “A novel chaotic Runge Kutta 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.Suche in Google Scholar

[10] A. R. Yildiz, “Cuckoo search algorithm for the selection of optimal machining parameters in milling operations,” Int. J. Adv. Manuf. Technol., vol. 64, nos. 1–4, pp. 55–61, 2013, https://doi.org/10.1007/s00170-012-4013-7.Suche in Google Scholar

[11] R. W. Salem and M. Haouari, “A simulation-optimisation approach for supply chain network design under supply and demand uncertainties,” Int. J. Prod. Res., vol. 55, no. 7, pp. 1845–1861, 2017, https://doi.org/10.1080/00207543.2016.1174788.Suche in Google Scholar

[12] B. S. Yıldız, “Optimal design of automobile structures using moth-flame optimization algorithm and response surface methodology,” Mater. Test., vol. 62, no. 4, pp. 371–377, 2020, https://doi.org/10.3139/120.111494.Suche in Google Scholar

[13] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, Perth, WA, Australia, IEEE, 1995, pp. 1942–1948.10.1109/ICNN.1995.488968Suche in Google Scholar

[14] 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.Suche in Google Scholar

[15] 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.Suche in Google Scholar

[16] S. Mirjalili, “Genetic algorithm,” in Evolutionary Algorithms and Neural Networks, vol. 780, Cham, Springer International Publishing, 2019, pp. 43–55.10.1007/978-3-319-93025-1_4Suche in Google Scholar

[17] S. Mirjalili, “SCA: a Sine Cosine Algorithm for solving optimization problems,” Knowl. Base Syst., vol. 96, pp. 120–133, 2016, https://doi.org/10.1016/j.knosys.2015.12.022.Suche in Google Scholar

[18] A. M. Khalid, K. M. Hosny, and S. Mirjalili, “COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle,” Neural Comput. Appl., vol. 34, no. 24, pp. 22465–22492, 2022, https://doi.org/10.1007/s00521-022-07639-x.Suche in Google Scholar PubMed PubMed Central

[19] A. E. Ezugwu, J. O. Agushaka, L. Abualigah, S. Mirjalili, and A. H. Gandomi, “Prairie dog optimization algorithm,” Neural Comput. Appl., vol. 34, no. 22, pp. 20017–20065, 2022, https://doi.org/10.1007/s00521-022-07530-9.Suche in Google Scholar

[20] M. A. Akbari, M. Zare, R. Azizipanah-abarghooee, S. Mirjalili, and M. Deriche, “The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems,” Sci. Rep., vol. 12, no. 1, p. 10953, 2022, https://doi.org/10.1038/s41598-022-14338-z.Suche in Google Scholar PubMed PubMed Central

[21] F. A. Hashim, R. R. Mostafa, A. G. Hussien, S. Mirjalili, and K. M. Sallam, “Fick’s Law Algorithm: a physical law-based algorithm for numerical optimization,” Knowl. Base Syst., vol. 260, p. 110146, 2023, https://doi.org/10.1016/j.knosys.2022.110146.Suche in Google Scholar

[22] E.-S. M. El-kenawy, A. A. Abdelhamid, A. Ibrahim, et al.., “Al-biruni Earth radius (BER) metaheuristic search optimization algorithm,” Comput. Syst. Sci. Eng., vol. 45, no. 2, pp. 1917–1934, 2023, https://doi.org/10.32604/csse.2023.032497.Suche in Google Scholar

[23] S. Kumar, D. Datta, and S. K. Singh, “Black hole algorithm and its applications,” in Computational Intelligence Applications in Modeling and Control, vol. 575, A. T. Azar and S. Vaidyanathan, Eds., Cham, Springer International Publishing, 2015, pp. 147–170.10.1007/978-3-319-11017-2_7Suche in Google Scholar

[24] I. Ahmadianfar, A. A. Heidari, S. Noshadian, H. Chen, and A. H. Gandomi, “INFO: an efficient optimization algorithm based on weighted mean of vectors,” Exp. Syst. Appl., vol. 195, p. 116516, 2022, https://doi.org/10.1016/j.eswa.2022.116516.Suche in Google Scholar

[25] J. Zhang, M. Xiao, L. Gao, and Q. Pan, “Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems,” Appl. Math. Model., vol. 63, pp. 464–490, 2018, https://doi.org/10.1016/j.apm.2018.06.036.Suche in Google Scholar

[26] A. Trivedi, K. Sanyal, P. Verma, and D. Srinivasan, “A unified differential evolution algorithm for constrained optimization problems,” in 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, San Sebastián, Spain, 2017, pp. 1231–1238.10.1109/CEC.2017.7969446Suche in Google Scholar

[27] M. Hellwig and H.-G. Beyer, “A matrix adaptation evolution strategy for constrained real-parameter optimization,” in 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, 2018, pp. 1–8.10.1109/CEC.2018.8477950Suche in Google Scholar

[28] T. O. Ting, X.-S. Yang, S. Cheng, and K. Huang, “Hybrid metaheuristic algorithms: past, present, and future,” in Recent Advances in Swarm Intelligence and Evolutionary Computation, vol. 585, X.-S. Yang, Ed., Cham, Springer International Publishing, 2015, pp. 71–83.10.1007/978-3-319-13826-8_4Suche in Google Scholar

[29] B. Alatas, “Chaotic harmony search algorithms,” Appl. Math. Comput., vol. 216, no. 9, pp. 2687–2699, 2010, https://doi.org/10.1016/j.amc.2010.03.114.Suche in Google Scholar

[30] S. Mirjalili and S. Z. M. Hashim, “A new hybrid PSOGSA algorithm for function optimization,” in 2010 International Conference on Computer and Information Application, Tianjin, China, 2010, pp. 374–377.10.1109/ICCIA.2010.6141614Suche in Google Scholar

[31] W. Kaidi, M. Khishe, and M. Mohammadi, “Dynamic levy flight chimp optimization,” Knowl. Base Syst., vol. 235, p. 107625, 2022, https://doi.org/10.1016/j.knosys.2021.107625.Suche in Google Scholar

[32] X. Liang, Z. Cai, M. Wang, X. Zhao, H. Chen, and C. Li, “Chaotic oppositional sine–cosine method for solving global optimization problems,” Eng. Comput., vol. 38, no. 2, pp. 1223–1239, 2022, https://doi.org/10.1007/s00366-020-01083-y.Suche in Google Scholar

[33] A. Singh, “Laplacian whale optimization algorithm,” Int. J. Syst. Assur. Eng. Manage., vol. 10, no. 4, pp. 713–730, 2019, https://doi.org/10.1007/s13198-019-00801-0.Suche in Google Scholar

[34] J. Pierezan, L. dos Santos Coelho, V. Cocco Mariani, E. Hochsteiner de Vasconcelos Segundo, and D. Prayogo, “Chaotic coyote algorithm applied to truss optimization problems,” Comput. Struct., vol. 242, p. 106353, 2021, https://doi.org/10.1016/j.compstruc.2020.106353.Suche in Google Scholar

[35] X. D. Li, J. S. Wang, W. K. Hao, M. Zhang, and M. Wang, “Chaotic arithmetic optimization algorithm,” Appl. Intell., pp. 1–40, 2022, https://doi.org/10.1007/s10489-021-03037-3.Suche in Google Scholar

[36] Z. Meng, Q. Qian, M. Xu, A. R. Yildiz, and S. Mirjalili, “PINN-FORM: a new physics-informed neural network for reliability analysis with partial differential equation,” Comput. Methods Appl. Mech. Eng., vol. 414, p. 116172, 2023, https://doi.org/10.1016/j.cma.2023.116172.Suche in Google Scholar

[37] 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, p. 106951, 2023, https://doi.org/10.1016/j.engappai.2023.106951.Suche in Google Scholar

[38] Z. Meng, Q. Qian, M. Xu, B. Yu, A. R. Yildiz, and S. Mirjalili, “Application of state ‑ of ‑ the ‑ art multiobjective metaheuristic algorithms in reliability ‑ based design optimization: a comparative study,” Struct. Multidiscip. Optim., vol. 66, p. 191, 2023, https://doi.org/10.1007/s00158-023-03639-0.Suche in Google Scholar

[39] D. Gürses, P. Mehta, S. M. Sait, and A. R. Yildiz, “African vultures optimization algorithm for optimization of shell and tube heat exchangers,” Mater. Test., vol. 64, no. 8, pp. 1234–1241, 2022, https://doi.org/10.1515/mt-2022-0050.Suche in Google Scholar

[40] D. Gürses, P. Mehta, V. Patel, S. M. Sait, and A. R. Yildiz, “Artificial gorilla troops algorithm for the optimization of a fine plate heat exchanger,” Mater. Test., vol. 64, no. 9, pp. 1325–1331, 2022, https://doi.org/10.1515/mt-2022-0049.Suche in Google Scholar

[41] B. S. Yildiz, “Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach,” Int. J. Veh. Des., vol. 83, no. 1, pp. 38–53, 2020, https://doi.org/10.1504/IJVD.2020.114779.Suche in Google Scholar

[42] B. S. Yildiz, “Marine predators algorithm and multi-verse optimisation algorithm for optimal battery case design of electric vehicles,” Int. J. Veh. Des., vol. 88, no. 1, pp. 1–11, 2022, https://doi.org/10.1504/IJVD.2022.124866.Suche in Google Scholar

[43] B. S. Yildiz, N. Pholdee, N. Panagant, S. Bureerat, and A. R. Yildiz, “A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems,” Eng. Comput., vol. 38, no. 2, pp. 871–883, 2022, https://doi.org/10.1007/s00366-020-01268-5.Suche in Google Scholar

[44] C. M. Aye, A. R. Pholdee, S. Bureerat, and S. M. Sait, “Multi-surrogate-assisted metaheuristics for crashworthiness optimisation,” Int. J. Veh. Des., vol. 80, no. 2–4, pp. 223–240, 2021, https://doi.org/10.1504/IJVD.2019.109866.Suche in Google Scholar

[45] A. Karaduman, B. S. Yildiz, and A. R. Yildiz, “Experimental and numerical fatigue-based design optimisation of clutch diaphragm spring in the automotive industry,” Int. J. Veh. Des., vol. 80, no. 2–4, pp. 330–345, 2020, https://doi.org/10.1504/IJVD.2019.109875.Suche in Google Scholar

[46] 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.Suche in Google Scholar

[47] P. Mehta, B. S. Yildiz, S. M. Sait, and A. R. Yildiz, “Hunger games search algorithm for global optimization of engineering design problems,” Mater. Test., vol. 64, no. 4, pp. 524–532, 2022, https://doi.org/10.1515/mt-2022-0013.Suche in Google Scholar

[48] B. S. Yildiz, S. Kumar, N. Panagant, P. Mehta, 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.Suche in Google Scholar

[49] S. Kumar, B. S. Yildiz, P. Mehta, et al.., “Chaotic marine predators algorithm for global optimization of real-world engineering problems,” Knowl. Based Syst., vol. 261, p. 110192, 2023, https://doi.org/10.1016/j.knosys.2022.110192.Suche in Google Scholar

[50] 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.Suche in Google Scholar

[51] 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.Suche in Google Scholar

[52] 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,” J. Comput. Des. Eng., vol. 9, no. 6, pp. 2452–2465, 2022, https://doi.org/10.1093/jcde/qwac113.Suche in Google Scholar

[53] A. Hammoudi, A. R. Yildiz, and S. M. Sait, “Mechanical engineering design optimisation using novel adaptive differential evolution algorithm,” Int. J. Veh. Des., vol. 80, no. 2–4, pp. 285–329, 2020, https://doi.org/10.1504/IJVD.2019.109873.Suche in Google Scholar

[54] A. Hammoudi, A. R. Yildiz, and S. M. Sait, “Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics,” Int. J. Veh. Des., vol. 80, nos. 2–4, pp. 121–136, 2020, https://doi.org/10.1504/IJVD.2019.109862.Suche in Google Scholar

[55] B. S. Yildiz, S. Kumar, N. Pholdee, S. Bureeat, S. M. Sait, and A. R. Yildiz, “A new chaotic Levy flight distribution optimization algorithm for solving constrained engineering problems,” Expert Syst., vol. 39, no. 8, p. e12992, 2022, https://doi.org/10.1111/exsy.12992.Suche in Google Scholar

[56] B. Aslan and A. R. Yildiz, “Optimum design of automobile components using lattice structures for additive manufacturing,” Mater. Test., vol. 62, no. 6, pp. 633–639, 2020, https://doi.org/10.3139/120.111527.Suche in Google Scholar

[57] H. Abderazek, A. R. Yildiz, and S. M. Sait, “Mechanical engineering design optimization using novel adaptive differential evolution algorithm,” Int. J. Veh. Des., vol. 80, nos. 2/3/4, p. 285, 2019, https://doi.org/10.1504/IJVD.2019.109873.Suche in Google Scholar

[58] Z. Fan, Y. Fang, W. Li, Y. Yuan, Z. Wang, and X. Bian, “LSHADE44 with an improved constraint-handling method for solving constrained single-objective optimization problems,” in 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, 2018, pp. 1–8.10.1109/CEC.2018.8477943Suche in Google Scholar

[59] M. Hellwig and H.-G. Beyer, “A modified matrix adaptation evolution strategy with restarts for constrained real-world problems,” in 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, United Kingdom, 2020, pp. 1–8.10.1109/CEC48606.2020.9185566Suche in Google Scholar

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

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

[62] 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.Suche in Google Scholar

[63] W. Gong, Z. Cai, and D. Liang, “Engineering optimization by means of an improved constrained differential evolution,” Comput. Methods Appl. Mech. Eng., vol. 268, pp. 884–904, 2014, https://doi.org/10.1016/j.cma.2013.10.019.Suche in Google Scholar

[64] 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.Suche in Google Scholar

[65] M. S. Tavazoei and M. Haeri, “Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms,” Appl. Math. Comput., vol. 187, no. 2, pp. 1076–1085, 2007, https://doi.org/10.1016/j.amc.2006.09.087.Suche in Google Scholar

[66] A. B. Krishna, S. Saxena, and V. K. Kamboj, “A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer,” Neural Comput. Appl., vol. 33, no. 12, pp. 7031–7072, 2021, https://doi.org/10.1007/s00521-020-05475-5.Suche in Google Scholar

[67] D. Dhawale, V. K. Kamboj, and P. Anand, “An improved chaotic Harris hawks optimizer for solving numerical and engineering optimization problems,” Eng. Comput., vol. 39, no. 2, pp. 1183–1228, 2021, https://doi.org/10.1007/s00366-021-01487-4.Suche in Google Scholar
Published Online: 2023-09-13
Published in Print: 2023-12-15

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Effects of shear deformation on grain size and mechanical properties of the forged B4Cp/Al composite
  3. A novel method for measurements of surface topography in previously inaccessible areas
  4. Optimum design of a seat bracket using artificial neural networks and dandelion optimization algorithm
  5. Comparison between laser and TIG welding of electron beam melted Ti6Al4V parts
  6. Influence of heat input on temperature and stress field of X80 steel pipeline cirumferential weld using type-B sleeve repairing
  7. Experimental investigation of mechanical properties of PLA, ABS, and PETG 3-d printing materials using fused deposition modeling technique
  8. Preparation, characterization, and antimicrobial activity of novel chitosan blended almond gum–nanosilica bionanocomposite film for food packaging applications
  9. A novel hybrid Fick’s law algorithm-quasi oppositional–based learning algorithm for solving constrained mechanical design problems
  10. Tensile, bending, and impact properties of laminated carbon/aramid/glass hybrid fiber composites
  11. Effect of welding processes on ferrite content, microstructure and mechanical properties of super duplex stainless steel 2507 welds
  12. Wear and residual stress in high-feed milling of AISI H13 tool steel
  13. Optimum design of a composite drone component using slime mold algorithm
  14. Lateral compression behavior of expanded polypropylene foam–filled carbon and glass fiber composite tubes
  15. Effect of red mud on mechanical and thermal properties of agave sisalana/glass fiber–reinforced hybrid composites
  16. Mechanical analysis of composite plates adhesively joined with different single-lap techniques under bending loading
https://doi.org/10.1515/mt-2023-0235
Schlagwörter für diesen Artikel
Fick’s law algorithm; mechanical design; bearing systems; vehicle bearing system; design algorithm comparison

Artikel in diesem Heft

  1. Frontmatter
  2. Effects of shear deformation on grain size and mechanical properties of the forged B4Cp/Al composite
  3. A novel method for measurements of surface topography in previously inaccessible areas
  4. Optimum design of a seat bracket using artificial neural networks and dandelion optimization algorithm
  5. Comparison between laser and TIG welding of electron beam melted Ti6Al4V parts
  6. Influence of heat input on temperature and stress field of X80 steel pipeline cirumferential weld using type-B sleeve repairing
  7. Experimental investigation of mechanical properties of PLA, ABS, and PETG 3-d printing materials using fused deposition modeling technique
  8. Preparation, characterization, and antimicrobial activity of novel chitosan blended almond gum–nanosilica bionanocomposite film for food packaging applications
  9. A novel hybrid Fick’s law algorithm-quasi oppositional–based learning algorithm for solving constrained mechanical design problems
  10. Tensile, bending, and impact properties of laminated carbon/aramid/glass hybrid fiber composites
  11. Effect of welding processes on ferrite content, microstructure and mechanical properties of super duplex stainless steel 2507 welds
  12. Wear and residual stress in high-feed milling of AISI H13 tool steel
  13. Optimum design of a composite drone component using slime mold algorithm
  14. Lateral compression behavior of expanded polypropylene foam–filled carbon and glass fiber composite tubes
  15. Effect of red mud on mechanical and thermal properties of agave sisalana/glass fiber–reinforced hybrid composites
  16. Mechanical analysis of composite plates adhesively joined with different single-lap techniques under bending loading
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 22.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/mt-2023-0235/html
Button zum nach oben scrollen