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A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system

Hammoudi Abderazek, Ferhat Hamza, Ali Riza Yildiz, Liang Gao and Sadiq M. Sait
From the journal Materials Testing


Metaheuristic optimization algorithms have gained relevance and have effectively been investigated for solving complex real design problems in diverse fields of science and engineering. In this paper, a recent meta-heuristic approach inspired by human social concepts, namely the queuing search algorithm (QSA), is implemented for the first time to optimize the main parameters of the spur gear, in particular, to minimize the weight of a single-stage spur gear. The effectiveness of the algorithm introduced is examined in two steps. First, the algorithm used is compared with descriptions in previous studies and indicates that the final results obtained by QSA lead to a reduction in gear weight by 7.5 %. Furthermore, the outcomes obtained are compared with those for the other five algorithms. The results reveal that the QSA outperforms the techniques with which it is compared such as the sine-cosine optimization algorithm, the ant lion optimization algorithm, the interior search algorithm, the teaching-learning-based algorithm, and the jaya algorithm in terms of robustness, success rate, and convergence capability.

Prof. Dr. Ali Rıza Yıldız Department of Automotive Engineering Uludağ University Görükle, Bursa, Turkey


The authors would like to express their gratitude to Bursa Uludağ University, Bursa, Turkey, and King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia, for their support of this research.


1 B. S. Yıldız: The spotted hyena optimization algorithm for weight-reduction of automobile brake components, Materials Testing 62 (2020), No. 4, pp. 383-388 DOI:10.3139/120.11149510.3139/120.111495Search in Google Scholar

2 B. S. Yıldız, A. R. Yildiz, E. I. Albak, H. Abderazek, Sadiq M. Sait, S. Bureerat: Butterfly optimization algorithm for optimum shape design of automobile suspension components, Materials Testing, 62 (2020), No. 4, pp. 365-370 DOI:10.3139/120.11149210.3139/120.111492Search in Google Scholar

3 B. S. Yildiz: The mine blast algorithm for the structural optimization of electrical vehicle components, Materials Testing 62 (2020), No. 5, pp. 497-501 DOI:10.3139/120.11151110.3139/120.111511Search in Google Scholar

4 E. Kurtuluş, A. R. Yildiz, S .M. Sait, Bureerat: A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails, Materials Testing 62 (2020), No. 3, pp. 251-260 DOI:10.3139/120.11147810.3139/120.111478Search in Google Scholar

5 B. S. Yildiz, A. R. Yildiz, N. Pholdee, S. Bureerat, S. M. Sait, V. Patel: The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components, Materials Testing 62 (2020), No. 3, pp. 261-264 DOI:10.3139/120.11147910.3139/120.111479Search in Google Scholar

6 Z. Meng, G. Li, X. Wang, S. M. Sait, A. R. Yildiz: A comparative study of metaheuristic algorithms for reliability-based design optimization problems, Archives of Computational Methods in Engineering, 28 (2021), pp. 1853-1869 DOI:10.1007/s11831-020-09443-z10.1007/s11831-020-09443-zSearch in Google Scholar

7 P. Champasak, N.Panagant, N. Pholdee, S. Bureerat, A. R. Yildiz: Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle, Aerospace Science and Technology 100 (2020), pp. 1-11 DOI:10.1016/j.ast.2020.10578310.1016/j.ast.2020.105783Search in Google Scholar

8 B. S Yildiz, N. Pholdee, S. Bureerat, A. R. Yildiz, S. M. Sait: Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm, Expert Systems, 38 (3) 2021, e12666 DOI:10.1111/exsy.1266610.1111/exsy.12666Search in Google Scholar

9 H. Abderazek, A. R. Yildiz, S. Mirjalili: Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism, Knowledge-Based Systems 105 (2020), No. 105237 DOI:10.1016/j.knosys.2019.10523710.1016/j.knosys.2019.105237Search in Google Scholar

10 A. R. Yildiz, H. Abderazek, S. Mirjalili: A comparative study of recent non-traditional methods for mechanical design optimization, Archives of Computational Methods in Engineering 27 (2020), pp. 1031-1048 DOI:10.1007/s11831-019-09343-x10.1007/s11831-019-09343-xSearch in Google Scholar

11 A. R. Yildiz, M. U. Erdaş: A new Hybrid Taguchisalp swarm optimization algorithm for the robust design of real-world engineering problems, Materials Testing 63(2021), pp.157-162 DOI:10.1515/mt-2020-002210.1515/mt-2020-0022Search in Google Scholar

12 B. S Yildiz, N. Pholdee, S. Bureerat, A. R. Yildiz, S. M. Sait: Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry, Materials Testing 63 (2021) 4, pp. 356-359 DOI 10.1515/mt-2020-005310.1515/mt-2020-0053Search in Google Scholar

13 B. S. Yıldız: Optimal design of automobile structures using moth-flame optimization algorithm and response surface methodology, Materials Testing 62 (2020), No. 4, pp. 372-377 DOI:10.3139/120.11149410.3139/120.111494Search in Google Scholar

14 F. Hamza, H. Abderazek, S. Lakhdar, D. Ferhat, A. R. Yildiz: Optimum design of cam-roller follower mechanism using a new evolutionary algorithm, The International Journal of Advanced Manufacturing Technology 99 (2018), No. 5-8, pp. 1261-1282 DOI:10.1007/s00170-018-2543-310.1007/s00170-018-2543-3Search in Google Scholar

15 B. S Yildiz, V. Patel, N. Pholdee, S. M. Sait, S. Bureerat, A. R. Yildiz: Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design, Materials Testing 63 (2021) 4, pp. 336-340 DOI:10.1515/mt-2020-004910.1515/mt-2020-0049Search in Google Scholar

16 H. Abderazek, D. Ferhat, A. Ivana: Adaptive mixed differential evolution algorithm for biobjective tooth profile spur gear optimization, The International Journal of Advanced Manufacturing Technology 90 (2017), No. 5-8, pp. 2063-2073 DOI:10.1007/s00170-016-9523-210.1007/s00170-016-9523-2Search in Google Scholar

17 H. Abderazek, F. Hamza, A. R. Yildiz, S. M. Sait: Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design, Materials Testing 63 (2021) 3, pp. 266-271 DOI: 10.1515/mt-2020-003910.1515/mt-2020-0039Search in Google Scholar

18 E. Demirci, A. R. Yıldız: An investigation of the crash performance of magnesium, aluminum and advanced high strength steels and different cross-sections for vehicle thin-walled energy absorbers, Materials Testing 60 (2018), No. 7-8, pp. 661-668 DOI:10.3139/120.11120110.3139/120.111201Search in Google Scholar

19 H. Ozkaya, M. Yildiz, A. R. Yildiz, S. Bureerat, B. S. Yildiz, Sadiq M. Sait: The equilibrium optimizationalgorithm and the response surface based metamodel for optimal structural design of vehicle components, Materials Testing 62 (2020), pp. 492-496 DOI:10.3139/120.11150910.3139/120.111509Search in Google Scholar

20 A. R. Yildiz, N. Kaya, N. Öztürk, F. Öztürk: Hybrid approach for genetic algorithm and Taguchi’s method based design optimization in the automotive industry, International Journal of Production Research 44 (2006), pp. 4897-4914 DOI:10.1080/0020754060061993210.1080/00207540600619932Search in Google Scholar

21 A. R. Yildiz: A novel hybrid whale nelder mead algorithm for optimization of design and manufacturing problems, International Journal of Advanced Manufacturing Technology 105 (2019), pp. 5091-5104 DOI:10.1007/s00170-019-04532-110.1007/s00170-019-04532-1Search in Google Scholar

22 T. Güler, A. Demirci, A. R. Yıldız, U. Yavuz: Lightweight design of an automobile hinge component using glass fiber polyamide composites, Materials Testing 60 (2018), No. 3, pp. 306-310 DOI:10.3139/120.11115210.3139/120.111152Search in Google Scholar

23 B. S. Yıldız, N. Pholdee, S. Bureerat, A. R. Yildiz, Sadiq M. Sait: Sine-cosine optimization algorithm for the conceptual design of automobile components, Materials Testing 62 (2020), pp. 744-748 DOI:10.3139/120.11154110.3139/120.111541Search in Google Scholar

24 H. Abderazek, D. Ferhat, I. Atanasovska, K. Boualem: A differential evolution algorithm for tooth profile optimization with respect to balancing specific sliding coefficients of involute cylindrical spur and helical gears, Advances in Mechanical Engineering 7 (2015), No. 9, pp. 1-11 DOI:10.1177/168781401560500810.1177/1687814015605008Search in Google Scholar

25 A. Karaduman, B. S. Yıldız, A. R. Yıldız: Experimental and numerical fatigue-based design optimisation of clutch diaphragm spring in the automotive industry, International Journal of Vehicle Design 80 (2020), No. 2-4, pp. 330-345 DOI:10.1504/IJVD.2019.10987510.1504/IJVD.2019.109875Search in Google Scholar

26 A. R. Yildiz, F. Ozturk: Hybrid Taguchi harmony search approach for shape optimization, Recent Advances in Harmony Search Algorithm 270 (2010), pp. 89-98 DOI:10.1007/978-3-642-04317-8_810.1007/978-3-642-04317-8_8Search in Google Scholar

27 E. Demirci, A. R. Yıldız: An experimental and numerical investigation of the effects of geometry and spot welds on the crashworthiness of vehicle thin-walled structures, Materials Testing 60 (2018), No. 6, pp. 553-561 DOI:10.3139/120.11118710.3139/120.111187Search in Google Scholar

28 B. Aslan, A. R. Yildiz: Optimum design of automobile components using lattice structures for additive manufacturing, Materials Testing 62 (2020), pp. 633-639 DOI:10.3139/120.11152710.3139/120.111527Search in Google Scholar

29 A. R. Yıldız, U. A. Kılıçarpa, E. Demirci: Topography and topology optimization of diesel engine components for light-weight design in the automotive industry, Materials Testing 61 (2019), No. 1, pp. 27-34 DOI:10.3139/120.11127710.3139/120.111277Search in Google Scholar

30 N. Panagant, N. Pholdee, S. Bureerat, A. R. Yildiz, Sadiq M. Sait: Seagull optimization algorithm for solving real-world design optimization problems, Materials Testing 62 (2020), pp. 640-644 DOI:10.3139/120.11152910.3139/120.111529Search in Google Scholar

31 A. R. Yildiz: Designing of optimum vehicle components using new generation optimization methods, Journal of Polytechnic 20 (2017), No. 2, pp. 319-323 DOI:10.2339/2017.20.2 325-33210.2339/2017.20.2325-332Search in Google Scholar

32 E. Demirci, A. R. Yıldız: A new hybrid approach for reliability-based design optimization of structural components, Materials Testing 61 (2019), pp. 111-119 DOI:10.3139/120.11129110.3139/120.111291Search in Google Scholar

33 M. Dorigo, M. Birattari, T. Stutzle : Ant colony optimization – artificial ants as a computational intelligence technique, IEEE Computational Intelligence Magazine, 1 (2006), pp. 28-3910.1109/MCI.2006.329691Search in Google Scholar

34 X. S. Yang, S. Deb: Cuckoo search via levy flights, Proc. of the World Congress on Nature and Biologically Inspired Computing (NaBIC-2009), Coimbatore, India (2009), pp. 210-214Search in Google Scholar

35 S. Mirjalili: The ant lion optimizer, Advances in Engineering Software 83 (2015), pp. 80-98 DOI:10.1016/j.advengsoft.2015.01.01010.1016/j.advengsoft.2015.01.010Search in Google Scholar

36 S. Mirjalili: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications 27 (2016), No. 4, pp. 1053-1073 DOI:10.1007/s00521-015-1920-110.1007/s00521-015-1920-1Search in Google Scholar

37 A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. L. Chen: Harris hawks optimization: Algorithm and applications, Future Generation Computer Systems 97 (2019), pp. 849-872 DOI:10.1016/j.future.2019.02.02810.1016/j.future.2019.02.028Search in Google Scholar

38 R. V. Rao, V. J. Savsani, D. P. Vakharia: Teaching– learning-based optimization: A novel method for constrained mechanical design optimization problems, Computer-Aided Design 43 (2011), No. 3, pp. 303-315 DOI: 10.1016/j.cad.2010.12.01510.1016/j.cad.2010.12.015Search in Google Scholar

39 F. Glover: Tabu search-uncharted domains, Annals of Operations Research 149 (2007). No. 1, pp. 89-98 DOI: 10.1007/s10479-006-0113-910.1007/s10479-006-0113-9Search in Google Scholar

40 D. Manjarres, I. Landa-Torres, S. Gil-Lopez, J. Del Ser, M. N. Bilbao, S. Salcedo-Sanz, Z. W. Geem: A survey on applications of the harmony search algorithm, Engineering Applications of Artificial Intelligence 26 (2013), No. 8, pp. 1818-1831 DOI: 10.1016/j.engappai.2013.05.00810.1016/j.engappai.2013.05.008Search in Google Scholar

41 M. Kumar, A. J. Kulkarni, S. C. Satapathy: Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology, Future Generation Computer Systems 81 (2018), pp. 252-272 DOI: 10.1016/j.future.2017.10.05210.1016/j.future.2017.10.052Search in Google Scholar

42 J. Zhang, M. Xiao, L. Gao, Q. Pan: Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems, Applied Mathematical Modelling 63 (2018), pp. 464-490 DOI: 10.1016/j.apm.2018.06.03610.1016/j.apm.2018.06.036Search in Google Scholar

43 V. Savsani, R. V. Rao, D. P. Vakharia: Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms, Mechanism and machine theory 45 (2010), No. 3, pp. 531-541 DOI: 10.1016/j.mechmachtheory.2009.10.01010.1016/j.mechmachtheory.2009.10.010Search in Google Scholar

44 A. H. Gandomi: Interior search algorithm (isa): a novel approach for global optimization, ISA transactions 53 (2014), No. 4, pp. 1168-1183 DOI: 10.1016/j.isatra.2014.03.01810.1016/j.isatra.2014.03.018Search in Google Scholar

45 B. S. Yildiz: Natural frequency optimization of vehicle components using the interior search algorithm, Materials Testing 59 (2017), No. 5, pp. 456-458 DOI:10.3139/120.11101810.3139/120.111018Search in Google Scholar

46 R. Rao: Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems, International Journal of Industrial Engineering Computations 7 (2016), No. 1, pp. 19-34 DOI:10.5267/j.ijiec.2015.8.00410.5267/j.ijiec.2015.8.004Search in Google Scholar

47 S. Mirjalili: SCA: a sine cosine algorithm for solving optimization problems, Knowledge-based systems 96 (2016), pp. 120-133 DOI:10.1016/j.knosys.2015.12.02210.1016/j.knosys.2015.12.022Search in Google Scholar

48 E. Mezura-Montes, C. A. C. Coello: Constraint-handling in nature-inspired numerical optimization: Past, present and future, Swarm and Evolutionary Computation 1 (2011), No. 4, pp. 173-194 DOI:10.1016/j.swevo.2011.10.00110.1016/j.swevo.2011.10.001Search in Google Scholar

49 T. Yokota, T. Taguchi, M. Gen: A solution method for optimal weight design problem of the gear using Genetic Algorithms, Computers and Industrial Engineering 35 (1998), No. 3-4, pp. 523-526 DOI:10.1016/S0360-8352(98)00149-110.1016/S0360-8352(98)00149-1Search in Google Scholar

Published Online: 2021-05-23
Published in Print: 2021-05-26

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