Comparison of classical and heuristic methods for solving engineering design problems

Year 2024, Volume: 8 Issue: 4, 267 - 277, 20.12.2024

Kürşat Tanrıver , Mustafa Ay

https://doi.org/10.26701/ems.1509881

Abstract

This paper presents an innovative application of the Ant Colony Optimization (ACO) algorithm to optimize engineering problems, specifically on welded beams and pressure vessels. A simulation study was conducted to evaluate the performance of the new ACO algorithm, comparing it with classical optimization techniques and other heuristic algorithms previously discussed in the literature. The algorithm was executed 20 times to obtain the most efficient results. The best performance outcome in the welded beam simulation was 1.7288, achieved after 540 iterations using 1000 ants, with a computation time of 6.27 seconds. Similarly, the best performance result in the pressure vessel simulation was 5947.1735, obtained after 735 iterations using 1000 ants and completed in 6.97 seconds. Compared to similar results reported in the literature, the new ACO algorithm demonstrated superior performance, offering an outstanding solution. Additionally, users can utilize this new ACO algorithm to quickly acquire information about welded beam design and prefabrication through simulation.

Keywords

ant colony, engineering design, MATLAB, optimization, welded beam design, pressure vessel

References

• Akcay, O. (2023). Structural optimization of the brake pedal using artificial intelligence. International Journal of Automotive Science and Technology, 7(3), 187–195. https://doi.org/10.30939/ijastech..1330096
• Tanriver, K., & Ay, M. (2023). Experimental software and topological optimization study of unpredictable forces in bolted connections. Tehnički Vjesnik - Technical Gazette, 30(4). https://doi.org/10.17559/TV-20221113121639
• Zhao, W., Liu, Y., Li, Y., Hu, C., & Rui, S. (2023). Multi-robot coverage path planning for dimensional inspection of large free-form surfaces based on hierarchical optimization. The International Journal of Advanced Manufacturing Technology, 127(11–12), 5471–5486. https://doi.org/10.1007/s00170-023-11788-1
• Harikrishnan, V. K., Sivakumar, A. I., Sampath, S., & Paramasivam, S. (2023). A time-performance improvement model with optimal ergonomic risk level using genetic algorithm. Transactions of FAMENA, 47(4), 109–128. https://doi.org/10.21278/TOF.474049022
• Avci, I., & Yildirim, M. N. (2023). Solving weapon-target assignment problem with Salp swarm algorithm. Tehnički Vjesnik - Technical Gazette, 30(1), 17–23. https://doi.org/10.17559/TV-20220113192727
• Katiyar, S., & Dutta, A. (2022). Comparative analysis on path planning of ATR using RRT*, PSO and modified APF in CG-Space. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 236(10), 5663–5677. https://doi.org/10.1177/09544062211062435
• Essiz, E. S., Kilic, V. N., & Oturakci, M. (2023). Firefly-based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey. Turkish Journal of Engineering, 7(1), 17–24. https://doi.org/10.31127/tuje.1005514
• Irmak, B., & Gülcü, Ş. (2021). Training of the feed-forward artificial neural networks using butterfly optimization algorithm. MANAS Journal of Engineering, 9(2), 160–168. https://doi.org/10.51354/mjen.91783
• Tian, T., Liang, Z., Wei, Y., Luo, Q., & Zhou, Y. (2024). Hybrid whale optimization with a firefly algorithm for function optimization and mobile robot path planning. Biomimetics, 9(1), 39. https://doi.org/10.3390/biomimetics9010039
• Tuncer, A. (2023). Path planning of autonomous mobile robots based on Voronoi diagram and ant colony optimization. Journal of Innovative Engineering and Natural Science, 4(1), 138–146. https://doi.org/10.61112/jiens.1365282
• Zhang, D., Luo, R., Yin, Y.-B., & Zou, S. (2023). Multi-objective path planning for mobile robot in nuclear accident environment based on improved ant colony optimization with modified A*. Nuclear Engineering and Technology, 55(5), 1838–1854. https://doi.org/10.1016/j.net.2023.02.005
• Sun, L., Chen, Y. S., Ding, W., & Xu, J. (2023). LEFSA: Label enhancement-based feature selection with adaptive neighborhood via ant colony optimization for multilabel learning. International Journal of Machine Learning and Cybernetics, 15, 533–558. https://doi.org/10.1007/s13042-023-01924-4
• Zhou, X., Gui, W., Heidari, A. A., Cai, Z., Liang, G., & Chen, H. (2023). Random following ant colony optimization: Continuous and binary variants for global optimization and feature selection. Applied Soft Computing, 144, 110513. https://doi.org/10.1016/j.asoc.2023.110513
• Hizaroğlu, O., & Akkurt, A. (2023). Simulating gait profile in MATLAB Simulink environment. Gazi Journal of Engineering Sciences, 9(3), 622–633. https://doi.org/10.30855/gmbd.0705093
• Hashemi, M., Joodaki, N. Z., & Dowlatshahi, M. B. (2022). Ant colony optimization equipped with an ensemble of heuristics through multi-criteria decision making: A case study in ensemble feature selection. Applied Soft Computing, 124, 109046. https://doi.org/10.1016/j.asoc.2022.109046
• Maniezzo, V., Gambardella, L. M., & De Luigi, F. (2004). Ant colony optimization. In: New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, 141, 101–121. https://doi.org/10.1007/978-3-540-39930-8_5
• Zheng, X., Wang, Z., Liu, D., & Wang, H. (2021). A path planning algorithm for PCB surface quality automatic inspection. Journal of Intelligent Manufacturing, 33(6), 1829–1841. https://doi.org/10.1007/s10845-021-01766-3
• Das, M., Roy, A., Maity, S., & Kar, S. (2023). A quantum-inspired ant colony optimization for solving a sustainable four-dimensional traveling salesman problem under type-2 fuzzy variable. Advanced Engineering Informatics, 55, 101816. https://doi.org/10.1016/j.aei.2022.101816
• Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2022). A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science, 940, 123–145. https://doi.org/10.1016/j.tcs.2022.10.019
• Rao, S. S. (1996). Engineering optimization: Theory and practice. John Wiley & Sons. https://doi.org/10.1002/9781119454816
• Ray, T., & Liew, K. M. (2003). Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation, 7(4), 386–396. https://doi.org/10.1109/TEVC.2003.814902
• Grković, V., & Bulatović, R. (2012). Modified ant colony algorithm for solving engineering optimization problems. IMK-14 - Research & Development, 18(4), EN115-122.
• Cagnina, L., Esquivel, S. C., & Coello, C. A. C. (2008). Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica, 32(3), 319–326.
• Renato, A. K., & Leandro, D. S. C. (2006). Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36, 1407–1416. https://doi.org/10.1109/tsmcb.2006.873185
• Zahara, E., & Kao, Y. T. (2009). Hybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Systems with Applications, 36(2), 3880–3886. https://doi.org/10.1016/j.eswa.2008.02.039
• He, Q., & Wang, L. (2007). A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation, 186, 1407–1422. https://doi.org/10.1016/j.amc.2006.07.134
• Huang, F. Z., Wang, L., & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation, 186(1), 340–356. https://doi.org/10.1016/j.amc.2006.07.105
• Coelho, L. D. S., & Mariani, V. C. (2008). Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Systems with Applications, 34(3), 1905–1913. https://doi.org/10.1016/j.eswa.2007.02.002
• Tanriver, K., & Ay, M. (2024). Simulation of speed reducer design with the modified ant colony optimization algorithm. The Journal of Engineering Sciences and Research, 6(1), 53–64. https://doi.org/10.46387/bjesr.1435356
• Hasan, M. S., Chowdhury, M. M.-U.-T., & Kamalasadan, S. (2024). Sequential quadratic programming (SQP) based optimal power flow methodologies for electric distribution system with high penetration of DERs. IEEE Transactions on Industry Applications, 60(3), 4810–4820. https://doi.org/10.1109/TIA.2024.3371428
• Wang, J., Hu, H., Zhang, W., & Hu, Z. (2023). Optimization-based transient control of turbofan engines: A sequential quadratic programming approach. International Journal of Turbo & Jet-Engines, 40(s1), s119–s128. https://doi.org/10.1515/tjj-2021-0072
• Alhaji, H. S., Celik, Y., & Goel, S. (2024). An approach to deepfake video detection based on ACO-PSO features and deep learning. Electronics, 13(2), 2398. https://doi.org/10.3390/electronics13122398
• Sabir, Z., Raja, M. A. Z., Wahab, H. A., Shoaib, M., & Aguilar, J. F. G. (2024). Integrated neuro-evolution heuristic with sequential quadratic programming for second-order prediction differential models. Numerical Methods for Partial Differential Equations, 40, e22692. https://doi.org/10.1002/num.22692
• Ji, W., Li, G., Wei, L., & Yi, Z. (2024). An improved sequential quadratic programming method for identifying the total heat exchange factor of reheating furnace. International Journal of Thermal Sciences, 204, 109238. https://doi.org/10.1016/j.ijthermalsci.2024.109238
• Wilson, E. O., & Hölldobler, B. (1988). Dense hierarchies and mass communication as the basis of organization in ant colonies. Trends in Ecology & Evolution, 3(3), 65–68. https://doi.org/10.1016/0169-5347(88)90018-3
• Dorigo, M., Maniezzo, V., & Colorni, A. (1996). The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(1), 29–41. https://doi.org/10.1109/3477.484436
• Hashemi, A., & Dowlatshahi, M. B. (2024). Exploring ant colony optimization for feature selection: A comprehensive review. In N. Dey (Ed.), Applications of ant colony optimization and its variants (pp. 101–121). Springer. https://doi.org/10.1007/978-981-99-7227-2_3
• Benhala, B., Ahaitouf, A., Mechaqrane, A., Benlahbib, B., Abdi, F., Hossain Abarkan, E. H., & Fakhfakh, M. (2011). Sizing of current conveyors by means of an ant colony optimization technique. In Proceedings of the International Conference on Multimedia Computing and Systems (pp. 1–6). Ouarzazate, Morocco. https://doi.org/10.1109/ICMCS.2011.5945669
• Shen, Q., Jiang, J., Tao, G., Shen, J., & Yu, R. (2005). Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. Journal of Chemical Information and Modeling, 45(4), 1024–1029. https://doi.org/10.1021/ci049610z
• Tanriver, K., & Ay, M. (2020). Topology optimization of a steel construction bolt under boundary conditions. Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences, 7(12), 31–47. https://doi.org/10.38065/euroasiaorg.272
• Ay, M., Altunpak, Y., & Hartomacioglu, S. (2017). The grey-based Taguchi method: Optimization of drilling of hybrid aluminum matrix composites. Acta Physica Polonica A, 131(3), 551–554. https://doi.org/10.12693/APhysPolA.131.551
• Tanriver, K., & Ay, M. (2024). Efficient path planning for drilling processes: The hybrid approach of a genetic algorithm and ant colony optimization. Transactions of FAMENA, 48(3), 125–140. https://doi.org/10.21278/TOF.483062023
• Basmaci, G., Kurt, M., Ay, M., & Barkin, B. (2018). Optimization of the effects of machining parameters in turning on Hastelloy C22 composition through Taguchi response surface methodology. Acta Physica Polonica A, 134(1), 28–31. https://doi.org/10.12693/APhysPolA.134.28
• Yüksel, S., Şirin, T. B., Ay, M., & Kurt, M. (2024). A study on end mill tool geometry parameters for end milling of 316L: Finite element analysis and response surface methodology optimization based on resultant cutting force. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(8), 452. https://doi.org/10.1007/s40430-024-05027-1
• Basmaci, G., Kayacan, M. Y., Ay, M., & Etyemez, A. (2023). Optimization of cutting forces and surface roughness via ANOVA and grey relational analysis in machining of In718. Open Chemistry, 21(1). https://doi.org/10.1515/chem-2022-0273
• Erman, B., & Kalyon, A. (2022). Multi-objective optimization of parameters in EDM of Mirrax steel. Materials and Manufacturing Processes, 38(7), 848–858.
• Tanriver, K., & Ay, M. (2024). Investigation of flue gas temperature effects in natural gas-fueled systems: Experimental thermal performance and structural optimization. International Journal of Heat and Fluid Flow, 107, 109428. https://doi.org/10.1016/j.ijheatfluidflow.2024.109428
• Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm: A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110–111, 151–166. https://doi.org/10.1016/j.compstruc.2012.07.010
• Coello, C. A. C. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113–127. https://doi.org/10.1016/S0166-3615(99)00046-9
• Zhao, J. G., Wang, L., Zeng, P., & Fan, W. H. (2012). An effective hybrid genetic algorithm with flexible allowance technique for constrained engineering design optimization. Expert Systems with Applications, 39(5), 6041–6051. https://doi.org/10.1016/j.eswa.2011.12.012