Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter April 7, 2022

Hunger games search algorithm for global optimization of engineering design problems

Pranav Mehta, Betul Sultan Yildiz, Sadiq M. Sait and Ali Riza Yildiz
From the journal Materials Testing

Abstract

The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-à-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristics (MHs), such as salp swarm optimizer, equilibrium optimizer, Harris Hawks optimizer (HHO), chaotic HHO, slime mould optimizer, marine predator optimizer, artificial bee colony optimizer, ant lion optimizer, and it was found that the HGS algorithm is able to pursue the best optimized solution subjecting to critical constraints. Moreover, the HGS algorithm can realize the least weight of the SA subjected to maximum stress values. Hence, the adopted algorithm can be found robust in terms of obtaining the best global optimum solution.


Corresponding author: Betul Sultan Yildiz, Department of Electric and Energy, Bursa Uludag Universitesi, Bursa, Turkey, E-mail:

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

  2. Research funding: None declared.

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

References

[1] R. Ravi and U. Surendra, “Battery management systems (BMS) for EV: electric vehicles and the future of energy-efficient transportation,” in Advances in Mechatronics and Mechanical Engineering, U. Subramaniam, S. S. Williamson, and M. Krishna, Eds., IGI Global, 2021, pp. 1–35.10.4018/978-1-7998-7626-7.ch001Search in Google Scholar

[2] M. Muthu, J. Gopal, D.-H. Kim, and I. Sivanesan, “Reviewing the impact of vehicular pollution on road-side plants – future perspectives,” Sustainability, vol. 13, no. 9, p. 5114, 2021, https://doi.org/10.3390/su13095114.Search in Google Scholar

[3] A. Issakhov and P. Omarova, “Modeling and analysis of the effects of barrier height on automobiles emission dispersion,” J. Clean. Prod., vol. 296, p. 126450, 2021, https://doi.org/10.1016/j.jclepro.2021.126450.Search in Google Scholar

[4] J. R. Martins and A. Ning, Engineering Design Optimization, Cambridge, UK, Cambridge University Press, 2021.10.1017/9781108980647Search in Google Scholar

[5] O. M. Pires, R. de Santiago, and J. Marchi, “Two stage quantum optimization for the school timetabling problem,” in 2021 IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, 2021, pp. 2347–2353.10.1109/CEC45853.2021.9504701Search in Google Scholar

[6] N. Kumar, N. Singh, and D. P. Vidyarthi, “Artificial lizard search optimization (ALSO): a novel nature-inspired meta-heuristic algorithm,” Soft Comput., vol. 25, no. 8, pp. 6179–6201, 2021, https://doi.org/10.1007/s00500-021-05606-7.Search in Google Scholar

[7] X.-S. Yang, “Metaheuristic optimization: nature-inspired algorithms and applications,” Artif. Intell., Evol. Comput. Metaheurist., vol. 427, pp. 405–420, 2013, https://doi.org/10.1007/978-3-642-29694-9_16.Search in Google Scholar

[8] L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. A. Al-qaness, and A. H. Gandomi, “Aquila Optimizer: a novel meta-heuristic optimization algorithm,” Comput. Ind. Eng., vol. 157, p. 107250, 2021, https://doi.org/10.1016/j.cie.2021.107250.Search in Google Scholar

[9] 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

[10] T. R. Farshi, “Battle royale optimization algorithm,” Neural Comput. Appl., vol. 33, no. 4, pp. 1139–1157, 2021, https://doi.org/10.1007/s00521-020-05004-4.Search in Google Scholar

[11] F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany, “Honey Badger Algorithm: new metaheuristic algorithm for solving optimization problems,” Math. Comput. Simul., vol. 192, pp. 84–110, 2022, https://doi.org/10.1016/j.matcom.2021.08.013.Search in Google Scholar

[12] A. Kaveh, “Thermal exchange metaheuristic optimization algorithm,” in Advances in Metaheuristic Algorithms for Optimal Design of Structures, Cham, Springer International Publishing, 2021, pp. 733–782.10.1007/978-3-030-59392-6_23Search in Google Scholar

[13] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, “African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems,” Comput. Ind. Eng., vol. 158, p. 107408, 2021, https://doi.org/10.1016/j.cie.2021.107408.Search in Google Scholar

[14] H. Jia, X. Peng, and C. Lang, “Remora optimization algorithm,” Expert Syst. Appl., vol. 185, p. 115665, 2021, https://doi.org/10.1016/j.eswa.2021.115665.Search in Google Scholar

[15] F. MiarNaeimi, G. Azizyan, and M. Raschk, “Horse herd optimization algorithm: a nature-inspired algorithm for high-dimensional optimization problems,” Knowl.-Based Syst., vol. 213, p. 106711, 2021, https://doi.org/10.1016/j.knosys.2020.106711.Search in Google Scholar

[16] K. Zhu, S. Ying, N. Zhang, and D. Zhu, “Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network,” J. Syst. Softw., vol. 180, p. 111026, 2021, https://doi.org/10.1016/j.jss.2021.111026.Search in Google Scholar

[17] S. Deb, D. S. Abdelminaam, M. Said, and E. H. Houssein, “Recent methodology-based gradient-based optimizer for economic load dispatch problem,” IEEE Access, vol. 9, pp. 44322–44338, 2021, https://doi.org/10.1109/ACCESS.2021.3066329.Search in Google Scholar

[18] A. Mohammadzadeh, M. Masdari, F. S. Gharehchopogh, and A. Jafarian, “Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing,” Evol. Intell., vol. 14, no. 4, pp. 1997–2025, 2021, https://doi.org/10.1007/s12065-020-00479-5.Search in Google Scholar

[19] 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.-Based Syst., vol. 212, p. 106556, 2021, https://doi.org/10.1016/j.knosys.2020.106556.Search in Google Scholar

[20] S. K. Barman, M. Mishra, D. K. Maiti, and D. Maity, “Vibration-based damage detection of structures employing Bayesian data fusion coupled with TLBO optimization algorithm,” Struct. Multidiscipl. Optim., vol. 64, no. 4, pp. 2243–2266, 2021, https://doi.org/10.1007/s00158-021-02980-6.Search in Google Scholar

[21] F. Goodarzian, S. F. Wamba, K. Mathiyazhagan, and A. Taghipour, “A new bi-objective green medicine supply chain network design under fuzzy environment: hybrid metaheuristic algorithms,” Comput. Ind. Eng., vol. 160, pp. 107535, 2021, https://doi.org/10.1016/j.cie.2021.107535.Search in Google Scholar

[22] C. Iwendi, P. K. R. Maddikunta, T. R. Gadekallu, K. Lakshmanna, A. K. Bashir, and Md. J. Piran, “A metaheuristic optimization approach for energy efficiency in the IoT networks,” Softw.: Pract. Exp., vol. 51, no. 12, pp. 2558–2571, 2021, https://doi.org/10.1002/spe.2797.Search in Google Scholar

[23] B. S. Yıldız, “The spotted hyena optimization algorithm for weight-reduction of automobile brake components,” Mater. Test., vol. 62, no. 4, pp. 383–388, 2020, https://doi.org/10.3139/120.111495.Search in Google Scholar

[24] B. S. Yıldız, A. R. Yıldız, N. Pholdee, S. Bureerat, S. M. Sait, and V. Patel, “The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components,” Mater. Test., vol. 62, no. 3, pp. 261–264, 2020, https://doi.org/10.3139/120.111479.Search in Google Scholar

[25] B. S. Yıldız, 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

[26] K.-H. Hwang, K.-W. Lee, and G.-J. Park, “Robust optimization of an automobile rearview mirror for vibration reduction,” Struct. Multidiscipl. Optim., vol. 21, no. 4, pp. 300–308, 2001, https://doi.org/10.1007/s001580100107.Search in Google Scholar

[27] G. M. Williams, “Optimization of eyesafe avalanche photodiode lidar for automobile safety and autonomous navigation systems,” Opt. Eng., vol. 56, no. 3, 2017, Art no. 031224, https://doi.org/10.1117/1.OE.56.3.031224.Search in Google Scholar

[28] J.-H. Meng, X.-D. Wang, and W.-H. Chen, “Performance investigation and design optimization of a thermoelectric generator applied in automobile exhaust waste heat recovery,” Energy Convers. Manage., vol. 120, pp. 71–80, 2016, https://doi.org/10.1016/j.enconman.2016.04.080.Search in Google Scholar

[29] B. A. Hassan, “CSCF: a chaotic sine cosine firefly algorithm for practical application problems,” Neural Comput. Appl., vol. 33, no. 12, pp. 7011–7030, 2021, https://doi.org/10.1007/s00521-020-05474-6.Search in Google Scholar

[30] L. Abualigah and A. Diabat, “A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments,” Cluster Comput., vol. 24, no. 1, pp. 205–223, 2021, https://doi.org/10.1007/s10586-020-03075-5.Search in Google Scholar

[31] Y. Yang, H. Chen, A. A. Heidari, and A. H. Gandomi, “Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts,” Expert Syst. Appl., vol. 177, p. 114864, 2021, https://doi.org/10.1016/j.eswa.2021.114864.Search in Google Scholar

[32] C. J. Burnett, C. Li, E. Webber, et al.., “Hunger-driven motivational state competition,” Neuron, vol. 92, no. 1, pp. 187–201, 2016, https://doi.org/10.1016/j.neuron.2016.08.032.Search in Google Scholar PubMed PubMed Central

[33] L. Real, “Animal choice behavior and the evolution of cognitive architecture,” Science, vol. 253, no. 5023, pp. 980–986, 1991, https://doi.org/10.1126/science.1887231.Search in Google Scholar PubMed

[34] M. I. Friedman and E. M. Stricker, “The physiological psychology of hunger: a physiological perspective,” Psychol. Rev., vol. 83, no. 6, pp. 409–431, 1976, https://doi.org/10.1037/0033-295X.83.6.409.Search in Google Scholar

[35] 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

[36] L. Giraud-Moreau and P. Lafon, “A comparison of evolutionary algorithms for mechanical design components,” Eng. Optim., vol. 34, no. 3, pp. 307–322, 2002, https://doi.org/10.1080/03052150211750.Search in Google Scholar

[37] 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

[38] 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

[39] 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,” Expert Syst., vol. 38, no. 3, 2021, Art no. e12666, https://doi.org/10.1111/exsy.12666.Search in Google Scholar

[40] 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 structure,” Mater. Test., vol. 60, no. 6, pp. 553–561, 2018, https://doi.org/10.3139/120.111187.Search in Google Scholar

[41] B. S. Yildiz, V. Patel, N. Pholdee, S. M. Sait, S. Bureerat, and A. R. Yildiz, “Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design,” Mater. Test., vol. 63, no. 4, pp. 336–340, 2021, https://doi.org/10.1515/mt-2020-0049.Search in Google Scholar

[42] H. Özkaya, M. Yıldız, A. R. Yildiz, S. Bureerat, B. S. Yıldız, and S. M. Sait, “The equilibrium optimization algorithm and the response surface-based metamodel for optimal structural design of vehicle components,” Mater. Test., vol. 62, no. 5, pp. 492–496, 2020, https://doi.org/10.3139/120.111509.Search in Google Scholar

[43] E. Demirci and A. R. Yıldız, “A new hybrid approach for reliability-based design optimization of structural components,” Mater. Test., vol. 61, pp. 111–119, 2019, https://doi.org/10.3139/120.111291.Search in Google Scholar

[44] 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, pp. 157–162, 2021, https://doi.org/10.1515/mt-2020-0022.Search in Google Scholar

[45] B. S. Yıldız, N. Pholdee, S. Bureerat, M. U. Erdaş, A. R. Yıldız, 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

[46] E. Demirci and A. R. Yildiz, “An investigation of the crash performance of magnesium, aluminum and advanced high strength steels and different cross-sections for vehicle thin-walled energy absorber,” Mater. Test., vol. 60, nos. 7–8, pp. 661–668, 2018, https://doi.org/10.3139/120.111201.Search in Google Scholar

Published Online: 2022-04-07
Published in Print: 2022-04-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Scroll Up Arrow