Metaheuristic Applications in Mechanical and Structural Design
Main Article Content
Abstract
The paper shows the significance of metaheuristic optimization algorithms through their application to specific engineering problems, especially in mechanical and civil engineering domains, where some significant publications are presented. Moreover, due to their nature, these algorithms are very convenient for application in various engineering examples, both with single-objective or multi-objective optimization problems. Also, they are successfully being applied for tasks with a great number of variables and constraint functions. Finally, the paper presents the comparison of the results of seven chosen metaheuristic optimization algorithms that were applied on the example of the cantilever beam subjected to complex loading. The objective function was the cross-sectional area of the welded I-profile. In contrast, the constraint functions were the permissible stresses in the I-profile and the welded connection supporting a cantilever beam and one welding technology limitation. After comparing obtained optimum results, optimization time and convergence for all seven chosen algorithms, some conclusions and recommendations for an appropriate type choice and application were made.
Article Details
References
Q. Qi, Y. Yu, Q. Dong, G. Xu and Y. Xin, "Lightweight and green design of general bridge crane structure based on multi- specular reflection algorithm", Advances in Mechanical Engineering, Vol. 13(10), pp. 1–15, (2021), https://doi.org/10.1177/16878140211051220
M. Savković, R. Bulatović, M. Gašić, G. Pavlović and A. Stepanović, "Optimization of the box section of the main gird-er of the single-girder bridge crane by applying biologically inspired algorithms", Engineering Structures, Vol. 148, pp. 452-465, (2017), https://doi.org/10.1016/j.engstruct.2017.07.004
G. Pavlović, M. Savković, N. Zdravković and G. Marković, "Comparative analysis and optimization of T and I cross sections of crane hook using by two physics-inspired algorithms," IMK-14 - Istraživanje i razvoj, Vol. 25(3), pp. 87-94, (2019), DOI: 10.5937/IMK1903087P
S. Mirjalili, "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm", Knowledge-Based Systems, Vol. 89, pp. 228-249, (2015), https://doi.org/10.1016/j.knosys.2015.07.006
S. Mirjalili, S.M. Mirjalili and A. Hatamlou, "Multi-Verse Optimizer: a nature-inspired algorithm for global optimiza-tion", Neural Comput & Applic, Vol.27, pp. 495–513, (2016), https://doi.org/10.1007/s00521-015-1870-7
S. Mirjalili, "The Ant Lion Optimizer", Advances in Engineering Software, Vol.83, pp. 80–98, (2015), https://doi.org/10.1016/j.advengsoft.2015.01.010
S. Arora and S. Singh, "Butterfly optimization algorithm: a novel approach for global optimization", Soft Comput, Vol. 23, pp. 715–734, (2019), https://doi.org/10.1007/s00500-018-3102-4
S. Arora, S. Singh and K. Yetilmezsoy, "A modified butterfly optimization algorithm for mechanical design optimiza-tion problems", J Braz. Soc. Mech. Sci. Eng., Vol. 40, Article ID 21, p. 17, (2018), https://doi.org/10.1007/s40430-017-0927-1
S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm", Advances in Engineering Software, Vol. 95, pp. 51–67, (2016), https://doi.org/10.1016/j.advengsoft.2016.01.008
S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris and S.M. Mirjalili, "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems", Advances in Engineering Software, Vol. 114, pp. 163-191, (2017), https://doi.org/10.1016/j.advengsoft.2017.07.002
G. Dhiman and V. Kumar, "Emperor penguin optimizer: A bio-inspired algorithm for engineering", Knowledge-Based Systems, Vol. 159, pp. 20–50, (2018), https://doi.org/10.1016/j.knosys.2018.06.001
G. Dhiman and A. Kaur, "STOA: A bio-inspired based optimization algorithm for industrial engineering problems", Engineering Applications of Artificial Intelligence, Vol. 82, pp. 148–174, (2019), https://doi.org/10.1016/j.engappai.2019.03.021
A.S. Heidari, R.A. Abbaspour and A.R. Jordehi, "An efficient chaotic water cycle algorithm for optimization tasks", Neural Comput & Applic, Vol. 28, pp. 57–85, (2017), https://doi.org/10.1007/s00521-015-2037-2
S. Mirjalili, "SCA: A Sine Cosine Algorithm for solving optimization problems", Knowledge-Based Systems, Vol. 96, pp. 120-133, (2015), https://doi.org/10.1016/j.knosys.2015.12.022
S. Mirjalili, "Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, dis-crete, and multi-objective problems", Neural Comput & Applic, Vol.27, pp. 1053–1073, (2016), https://doi.org/10.1007/s00521-015-1920-1
A. Kaveh and A. Zolghadr, "Cyclical Parthenogenesis Algorithm: A New Metaheuristic Algorithm", Asian Journal of Civil Engineering (BHRC), Vol. 18(5), pp. 673-701, (2017)
A. Kaveh, S.R.H. Vaez and P. Hosseini, "Matlab Code for an Enhanced Vibrating Particles System Algorithm", Int. J. Optim. Civil Eng., Vol. 8(3), pp.401-414, (2018)
A. Kaveh and A.D. Eslamlou, "Metaheuristic Optimization Algorithms in Civil Engineering: New Applications", Springer Nature Switzerland AG, Cham (Switzerland), (2020), https://doi.org/10.1007/978-3-030-45473-9
O. Skoglund, J. Leander and R. Karoumi, "Optimizing the steel girders in a high strength steel composite bridge", Engineering Structures, Vol. 221, Article ID 110981, p. 10, (2020), https://doi.org/10.1016/j.engstruct.2020.110981
H. Abedini, S.R.H. Vaez, and A. Zarrineghbal, "Optimum design of buckling-restrained braced frames", Structures, Vol. 25, pp. 99–112, (2020), https://doi.org/10.1016/j.istruc.2020.03.004
Z. Petković and D. Ostrić, "Metalne konstrukcije u masinogradnji I", University of Belgrade, Faculty of Mechanical Engineering, Belgrade (Serbia), (1996)
E. Cuevas, A. González, F. Fausto, D. Zaldívar and M. Pérez-Cisneros, "Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms", Mathematical Problems in Engineering, Vol. 2015, Article ID 805357, p. 25, (2015), https://doi.org/10.1155/2015/805357