Investigating the Ability of Grey Wolf Optimizer to Solve Pressure Vessel Optimization Design Problem
DOI:
https://doi.org/10.55544/jrasb.4.2.21Keywords:
Grey Wolf Optimizer, Metaheuristic Algorithm, Pressure Vessel Design, Constrained Optimization, Engineering DesignAbstract
This study investigates the effectiveness of the Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic algorithm, in solving the complex and constrained pressure vessel design optimization problem. The primary objective is to minimize the cost and weight of a pressure vessel while satisfying multiple nonlinear constraints related to material thickness, radius, and length. The GWO algorithm is benchmarked against several other well-known metaheuristic algorithms, including DE, GA, PSO, and CMA-ES, under identical experimental settings. To handle constraints, the death penalty method is applied. Results demonstrate that GWO performs competitively, ranking second overall in solution quality and convergence speed, surpassed only by Differential Evolution (DE). The findings highlight GWO’s potential as a robust and efficient optimization tool for solving real-world engineering design problems with complex search landscapes.
Downloads
References
Bansal, Jagdish Chand, Pramod Kumar Singh, and Nikhil R. Pal, eds. Evolutionary and swarm intelligence algorithms. Vol. 779. Cham: Springer, 2019.
Yang, Xin-She. "Nature-inspired optimization algorithms: Challenges and open problems." Journal of Computational Science 46 (2020): 101104. https://doi.org/10.1016/j.jocs.2020.101104.
Rechenberg, Ingo. "Evolutionsstrategie." Optimierung technischer Systeme nach Prinzipien derbiologischen Evolution (1973).
Holland, John H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.
Storn, Rainer, and Kenneth Price. "Differential evolution–a simple and efficient metaheuristic for global optimization over continuous spaces." Journal of global optimization 11 (1997): 341-359. https://doi.org/10.1023/A:1008202821328
Simon, Dan. "Biogeography-based optimization." IEEE transactions on evolutionary computation 12, no. 6 (2008): 702-713. DOI: 10.1109/TEVC.2008.919004
Koza, John R., and James P. Rice. "Automatic programming of robots using genetic programming." In AAAI, vol. 92, pp. 194-207. 1992.
Hansen, Nikolaus, Andreas Ostermeier, and Andreas Gawelczyk. "On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation." In ICGA, pp. 57-64. 1995.
Kennedy, James, and Russell Eberhart. "Particle swarm optimization." In Proceedings of ICNN'95-international conference on neural networks, vol. 4, pp. 1942-1948. IEEE, 1995. DOI: 10.1109/ICNN.1995.488968
Dorigo, M., and T. Stützle. "Ant Colony Optimization, Bradford Publisher." (2004).
Karaboga, Dervis, and Bahriye Basturk. "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm." Journal of global optimization 39 (2007): 459-471. https://doi.org/10.1007/s10898-007-9149-x
Karaboga, Dervis. An idea based on honey bee swarm for numerical optimization. Vol. 200. Technical report-tr06, Erciyes university, engineering faculty, computer engineering department, 2005.
Bansal, Jagdish Chand, Harish Sharma, Shimpi Singh Jadon, and Maurice Clerc. "Spider monkey optimization algorithm for numerical optimization." Memetic computing 6 (2014): 31-47. https://doi.org/10.1007/s12293-013-0128-0
Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Yang, Xin-She. "Firefly algorithm, stochastic test functions and design optimisation." International journal of bio-inspired computation 2, no. 2 (2010): 78-84. https://doi.org/10.1504/IJBIC.2010.032124
Yang, Xin-She, and Suash Deb. "Cuckoo search via Lévy flights." In 2009 World congress on nature & biologically inspired computing (NaBIC), pp. 210-214. Ieee, 2009. DOI: 10.1109/NABIC.2009.5393690
Yang, Xin-She, and Suash Deb. "Engineering optimisation by cuckoo search." International Journal of Mathematical Modelling and Numerical Optimisation 1, no. 4 (2010): 330-343. https://doi.org/10.1504/IJMMNO.2010.03543
Rajabioun, Ramin. "Cuckoo optimization algorithm." Applied soft computing 11, no. 8 (2011): 5508-5518. https://doi.org/10.1016/j.asoc.2011.05.008
Rashedi, Esmat, Hossein Nezamabadi-Pour, and Saeid Saryazdi. "GSA: a gravitational search algorithm." Information sciences 179, no. 13 (2009): 2232-2248. https://doi.org/10.1016/j.ins.2009.03.004
Yang, Xin-She. "Harmony search as a metametaheuristic algorithm." Music-inspired harmony search algorithm: theory and applications (2009): 1-14. https://doi.org/10.1007/978-3-642-00185-7_1
Kirkpatrick, Scott, C. Daniel Gelatt Jr, and Mario P. Vecchi. "Optimization by simulated annealing." science 220, no. 4598 (1983): 671-680. DOI: 10.1126/science.220.4598.671
Erol, Osman K., and Ibrahim Eksin. "A new optimization method: big bang–big crunch." Advances in engineering software 37, no. 2 (2006): 106-111. https://doi.org/10.1016/j.advengsoft.2005.04.005
Kaveh, A., and Siamak Talatahari. "A novel metaheuristic optimization method: charged system search." Acta mechanica 213, no. 3-4 (2010): 267-289. DOI: https://doi.org/10.1007/s00707-009-0270-4
Formato, Richard. "Central force optimization: a new metametaheuristic with applications in applied electromagnetics." Progress in electromagnetics research 77 (2007): 425-491. doi:10.2528/PIER07082403
Rao, R. Venkata, Vimal J. Savsani, and D. P. Vakharia. "Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems." Information sciences 183, no. 1 (2012): 1-15. https://doi.org/10.1016/j.ins.2011.08.006
Zong Woo Geem, Joong Hoon Kim, Loganathan GV. A New Metaheuristic Optimization Algorithm: Harmony Search. SIMULATION. 2001;76(2):60-68. doi:10.1177/003754970107600201
S. He, Q. H. Wu and J. R. Saunders, "Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior," in IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 973-990, Oct. 2009, doi: 10.1109/TEVC.2009.2011992.
Fred Glover, (1989) Tabu Search—Part I. ORSA Journal on Computing 1(3):190-206. https://doi.org/10.1287/ijoc.1.3.190
Mirjalili, Seyedali. "SCA: a sine cosine algorithm for solving optimization problems." Knowledge-based systems 96 (2016): 120-133. https://doi.org/10.1016/j.knosys.2015.12.022
Zhao, J., Tang, D., Liu, Z. et al. Spherical search optimizer: a simple yet efficient meta-metaheuristic approach. Neural Comput & Applic 32, 9777–9808 (2020). https://doi.org/10.1007/s00521-019-04510-4
Abualigah, Laith, Ali Diabat, Seyedali Mirjalili, Mohamed Abd Elaziz, and Amir H. Gandomi. "The arithmetic optimization algorithm." Computer methods in applied mechanics and engineering 376 (2021): 113609. https://doi.org/10.1016/j.cma.2020.113609
Salimi, Hamid. "Stochastic fractal search: a powerful metametaheuristic algorithm." Knowledge-based systems 75 (2015): 1-18. https://doi.org/10.1016/j.knosys.2014.07.025
Layeb, A. Tangent search algorithm for solving optimization problems. Neural Comput & Applic 34, 8853–8884 (2022). https://doi.org/10.1007/s00521-022-06908-z
Rao, Singiresu S. Engineering optimization: theory and practice. John Wiley & Sons, 2019.
Gandomi, A.H., Yang, XS., Alavi, A.H. et al. Bat algorithm for constrained optimization tasks. Neural Comput & Applic 22, 1239–1255 (2013). https://doi.org/10.1007/s00521-012-1028-9
Corne, David, Marco Dorigo, Fred Glover, Dipankar Dasgupta, Pablo Moscato, Riccardo Poli, and Kenneth V. Price, eds. New ideas in optimization. McGraw-Hill Ltd., UK, 1999.
Varaee, Hesam, Naser Safaeian Hamzehkolaei, and Mahsa Safari. "A hybrid generalized reduced gradient-based particle swarm optimizer for constrained engineering optimization problems." Journal of Soft Computing in Civil Engineering 5, no. 2 (2021): 86-119.
URL:https://scholar.google.com/citations?view_op=view_citation&hl=en&user=TJHmrREAAAAJ&citation_for_view=TJHmrREAAAAJ:k_IJM867U9cC (Accessed, December 4, 2023).
URL: https://forgedcomponents.com/what-are-some-of-the-uses-for-pressure-vessels/ (Accessed, December 4, 2023
Sabery, G. A., Danishyar, G. H., & Osmani, M. A. (2024). Evaluation the performance of sin e cosine algorithm in solving pressure vessel engineering de sign problem. J. Res. Appl. Sci. Biotechnol., 3(3), 38-46.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ismaiel Nasrat, Dawood Rahmani
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
10.55544/jrasb
editor@jrasb.com or mailtojrasb@gmail.com | 
+91-8874396939
This work is licensed under a 