Abstract
There is a growing interest in designing lightweight and low-cost vehicles. In this research, the Harris hawks optimization algorithm (the HHO), the salp swarm algorithm (SSA), the grasshopper optimization algorithm(GOA), and the dragonfly algorithm (DA) are introduced to solve shape optimization problems in the automotive industry. This research is the first application of the HHO, the SSA, the GOA, and the DA to shape design optimization problems in the literature. In this paper, the HHO, the SSA, and the DA algorithms are used for shape optimization of a vehicle brake pedal to prove how the HHO, the SSA, the GOA, and the DA can be used for solving shape optimization problems. The results show the ability of the HHO, the SSA, the GOA, and the DA to design better optimal components.
References
1 B. S. Yildiz , A. R.Yildiz: Comparison of the grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod, Materials Testing60 (2018), No. 3, pp. 311–31510.3139/120.111153Search in Google Scholar
2 T. Kunakote , S.Bureerat: Multi-objective topology optimization using evolutionary algorithms, Engineering Optimization43 (2011), No. 5, pp. 541–55710.1080/0305215X.2010.502935Search in Google Scholar
3 B. S. Yildiz , A. R.Yildiz: Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes, Materials Testing59 (2017), No. 5, pp. 425–42910.3139/120.111024Search in Google Scholar
4 A. R. Yildiz : A comparative study of population-based optimization algorithms for turning operations, Information Sciences210 (2012), pp. 81–8810.1016/j.ins.2012.03.005Search in Google Scholar
5 A. R. Yildiz : An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry, Journal of Materials Processing Technology209 (2009), No. 6, pp. 2773–278010.1016/j.jmatprotec.2008.06.028Search in Google Scholar
6 A. R. Yildiz : A new hybrid bee colony optimization approach for robust optimal design and manufacturing, Applied Soft Computing13 (2013), No. 5, pp. 2906–291210.1016/j.asoc.2012.04.013Search in Google Scholar
7 A. R. Yildiz : A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations, Applied Soft Computing13 (2013), No. 3, pp. 1561–156610.1016/j.asoc.2011.12.016Search in Google Scholar
8 A. R. Yildiz : Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations, Applied Soft Computing13 (2013), No. 3, pp. 1433–143910.1016/j.asoc.2012.01.012Search in Google Scholar
9 A. R. Yildiz , F.Ozturk: Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation, Proc. Instn. Mech. Engrs, Part B, Journal of Engineering Manufacture220 (2006), No. 12, pp. 2041–205310.1243/09544054JEM570Search in Google Scholar
10 A. R. Yildiz , K.Solanki: Multi-objective optimization of vehicle crashworthiness using new particle swarm based approach, International Journal of Advanced Manufacturing Technology59 (2012), No. 1–4, pp. 367–37610.1007/s00170-011-3496-ySearch in Google Scholar
11 A. R. Yildiz : Hybrid Taguchi-Harmony Search Algorithm for Solving Engineering Optimization Problems, International Journal of Industrial Engineering Theory, Applications and Practice15 (2008), No. 3, pp. 286–293Search in Google Scholar
12 A. R. Yildiz : A novel particle swarm optimization approach for product design and manufacturing, International Journal of Advanced Manufacturing Technology40 (2009), No. 5–6, pp. 617–62810.1007/s00170-008-1453-1Search in Google Scholar
13 A. R. Yildiz : Hybrid immune-simulated annealing algorithm for optimal design and manufacturing, International Journal of Materials and Product Technology34 (2009), No. 3, pp. 217–22610.1504/IJMPT.2009.024655Search in Google Scholar
14 A. R. Yıldız : A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing, Applied Soft Computing13 (2013), pp. 2906–291210.1016/j.asoc.2012.04.013Search in Google Scholar
15 A. R. Yildiz : Cuckoo search algorithm for the selection of optimal machining parameters in milling operations, International Journal of Advanced Manufacturing Technology64 (2013), No. 1–4, pp. 55–6110.1007/s00170-012-4013-7Search in Google Scholar
16 T. Güler , E.Demirci, A. R.Yildiz, U.Yavuz: Lightweight design of an automobile hinge component using glass fiber polyamide composites, Materials Testing, 60 (2018), pp. 306–31010.3139/120.111152Search in Google Scholar
17 A. R. Yildiz : A New Design Optimization Framework based on immune Algorithm and Taguchi Method, Computers in Industry60 (2009), pp. 613–62010.1016/j.compind.2009.05.016Search in Google Scholar
18 B. S. Yildiz , H.Lekesiz: Fatigue-based structural optimisation of vehicle components, International Journal of Vehicle Design73 (2017), pp. 54–6210.1504/IJVD.2017.10003398Search in Google Scholar
19 A. R. Yildiz : A novel hybrid immune algorithm for global optimization in design and manufacturing, Robotics and Computer-Integrated Manufacturing25 (2009), No. 2, pp. 261–27010.1016/j.rcim.2007.08.002Search in Google Scholar
20 A. R. Yildiz : Designing of optimum vehicle components using new generation optimization methods, Journal of Polytechnic20 (2017), No. 2, pp. 319–32310.2339/2017.20.2325-332Search in Google Scholar
21 H. Gokdağ , A. R.Yildiz: Structural damage detection using modal parameters and particle swarm optimization Materials Testing54 (2012), No. 6, pp. 416–42010.3139/120.110346Search in Google Scholar
22 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 Technology99 (2018), No. 5–8, pp. 1261–128210.1007/s00170-018-2543-3Search in Google Scholar
23 N. Pholdee , S.Bureerat, A. R.Yildiz: Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame, International Journal of Vehicle Design73 (2017), No. 1–3, pp. 20–5310.1504/IJVD.2017.082578Search in Google Scholar
24 S. Karagöz , A. R.Yildiz: A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects, International Journal of Vehicle Design73 (2017), No. 1–3, pp. 179–18810.1504/IJVD.2017.082593Search in Google Scholar
25 A. R. Yildiz , E.Kurtuluş, E.Demirci, B. S.Yildiz, S.Karagöz: Optimization of thin-wall structures using hybrid gravitational search and Nelder-Mead algorithm, Materials Testing58 (2016), No. 1, pp. 75–7810.3139/120.110823Search in Google Scholar
26 B. S. Yildiz : A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems, International Journal of Vehicle Design73 (2017), No. 1–3, pp. 208–21810.1504/IJVD.2017.082603Search in Google Scholar
27 M. Kiani , A. R.Yildiz: A comparative study of non-traditional methods for vehicle crashworthiness and NVH optimization, Archives of Computational Methods in Engineering23 (2016), No. 4, pp. 723–73410.1007/s11831-015-9155-ySearch in Google Scholar
28 B. S. Yildiz , H.Lekesiz, A. R.Yildiz: Structural design of vehicle components using gravitational search and charged system search algorithms, Materials Testing58 (2016), No. 1, pp. 79–8110.3139/120.110819Search in Google Scholar
29 A. R. Yildiz : Comparison of evolutionary based optimization algorithms for structural design optimization, Engineering Applications of Artificial Intelligence26 (2013), No. 1, pp. 327–33310.1016/j.engappai.2012.05.014Search in Google Scholar
30 A. R. Yildiz : Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach, Information Sciences, 220 (2013), pp. 399–40710.1016/j.ins.2012.07.012Search in Google Scholar
31 A. R. Yildiz , K.Saitou: Topology Synthesis of Multi-Component Structural Assemblies in Continuum Domains, Transactions of ASME, Journal of Mechanical Design133 (2011), No. 1, 011008-9 10.1115/1.4003038Search in Google Scholar
32 A. R. Yildiz , U. A.Kılıçarpa, E.Demirci, M.Dogan: Topography and topology optimization of diesel engine components for light-weight design in the automotive industry, Materials Testing61 (2019), pp. 27–3410.3139/120.111277Search in Google Scholar
33 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 Testing60 (2018), pp. 553–56110.3139/120.111187Search in Google Scholar
34 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 Testing60 (2018), pp. 661–66810.3139/120.111201Search in Google Scholar
35 A. R. Yildiz : A new hybrid particle swarm optimization approach for structural design optimization in automotive industry, Journal of Automobile Engineering226 (2012), No. 10, pp. 1340–135110.1177/0954407012443636Search in Google Scholar
36 B. S. Yildiz : Natural frequency optimization of vehicle components using the interior search algorithm, Materials Testing59 (2017), No. 5, pp. 456–45810.3139/120.111018Search in Google Scholar
37 A. R. Yildiz : Optimal structural design of vehicle components using topology design and optimization, Materials Testing50 (2008), No. 4, pp. 224–22810.3139/120.100880Search in Google Scholar
38 A. R. Yildiz , N.Öztürk, N.Kaya, F.Öztürk: Hybrid multi-objective shape design optimization using Taguchi's method and genetic algorithm, Structural and Multidisciplinary Optimization34 (2007), pp. 317–33210.1007/s00158-006-0079-xSearch in Google Scholar
39 E. Demirci , A. R.Yıldız: A new hybrid approach for reliability-based design optimization of structural components, Materials Testing61 (2019), pp. 111–11910.3139/120.111291Search in Google Scholar
40 A. R. Yildiz , N.Öztürk, N.Kaya, F.Öztürk: Integrated optimal topology design and shape optimization using neural networks, Structural and Multidisciplinary Optimization25 (2003), No. 4, pp. 251–26010.1007/s00158-003-0300-0Search in Google Scholar
41 A. R. Yildiz , E.Kurtulus, E.Demirci, B. S.Yildiz, S.Karagoz: Optimization of thinwall structures using hybrid gravitational search and Nelder-Mead algorithm, Materials Testing58 (2016), No. 1, pp. 75–7810.3139/120.110823Search in Google Scholar
42 A. R. Yildiz : A New Design Optimization Framework based on immune Algorithm and Taguchi Method, Computers in Industry60 (2009), No. 8, pp. 613–62010.1016/j.compind.2009.05.016Search in Google Scholar
43 İ. Durgun , A. R.Yildiz: Structural design optimization of vehicle components using Cuckoo search algorithm, Materials Testing54 (2012), No. 3, pp. 185–18810.3139/120.110317Search in Google Scholar
44 N. Öztürk , A. R.Yildiz, N.Kaya, F.Öztürk: Neuro-genetic design optimization framework to support the integrated robust design optimization process in CE, Concurrent Engineering Research And Applications14 (2006), No. 1, pp. 5–1610.1177/1063293X06063314Search in Google Scholar
45 A. Askarzadeh : A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm, Computers & Structure169 (2016), pp. 1–1210.1016/j.compstruc.2016.03.001Search in Google Scholar
46 A. Heidari , S.Mirjalili, H.Farris, I.Aljarah, M.Mafarja, H.Chen: Harris hawks optimization: Algorithm and applications, Future Generation Computer Systems, 97 (2019), pp. 849–87210.1016/j.future.2019.02.028Search in Google Scholar
47 S. Mirjalili , A. H.Gandomi, S. Z.Mirjalili, S.Saremi, H.Faris, S. M.Mirjalili: Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software114 (2017), pp. 163–19110.1016/j.advengsoft.2017.07.002Search in Google Scholar
48 S. Saremi , S.Mirjalili, A.Lewis: Grasshopper optimisation algorithm: theory and application, Advances in Engineering Software105 (2017), pp. 30–4710.1016/j.advengsoft.2017.01.004Search in Google Scholar
49 S. Mirjalili : Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications27 (2016), No. 4, pp. 1053–107310.1007/s00521-015-1920-1Search in Google Scholar
50 A. Sadollah , H.Eskandar, A.Bahreininejad, J. H.Kim: Water cycle algorithm for solving multi-objective optimization problems, Soft Computing19 (2015), No. 9, pp. 2587–260310.1007/s00500-014-1424-4Search in Google Scholar
51 I. Rajendran , S.Vijayarangan: Optimal design of a composite leaf spring using genetic algorithms, Computers and Structures79 (2001), No. 11, pp. 1121–112910.1016/S0045-7949(00)00174-7Search in Google Scholar
52 F. Kang , J. J.Li, J. H.Dai, Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms, Advances in Engineering Software, 131 (2019), pp. 60–7610.1016/j.advengsoft.2019.03.003Search in Google Scholar
53 G. I. Sayed , A.Tharwat, A. E.Hassanien, Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection, Applied Intelligence, 49 (2019), pp. 188–20510.1007/s10489-018-1261-8.Search in Google Scholar
54 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 Research44 (2006), pp. 4897–491410.1080/00207540600619932Search in Google Scholar
55 A. R. Yildiz : Optimization of multi-pass turning operations using hybrid teaching learning-based approach, The International Journal of Advanced Manufacturing Technology66 (2013), pp. 1319–132610.1007/s00170-012-4410-ySearch in Google Scholar
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