Optimization of Welded Beam Design Problem Using Water Evaporation Optimization Algorithm
DOI:
https://doi.org/10.25007/ajnu.v12n3a1753الملخص
This paper introduces a novel approach to tackle the Welded Beam Design Problem through the application of the Water Evaporation Optimization Algorithm (WEOA), a nature-inspired metaheuristic. The problem involves finding the optimal dimensions of a welded beam that can support a given load while minimizing its weight. The Water Evaporation Optimization Algorithm draws inspiration from the evaporation process and water droplet movement in nature. The design is formulated as an optimization challenge with beam dimensions as variables and incorporate constraints such as allowable stress and geometric limitations. The fitness function is tailored to evaluate each candidate solution based on load-bearing capacity and weight. To demonstrate the efficacy of the proposed method, extensive experimental evaluations are conducted. Comparisons with traditional optimization techniques highlight the WEOA's superior convergence and global search capabilities. Real-world case studies further illustrate the practical applicability of the optimized welded beam designs, showcasing their cost-effectiveness and high-performance characteristics. The results underscore the potential of the Water Evaporation Optimization Algorithm as a robust and efficient tool for tackling the welded beam design problem. The approach provides engineers with valuable support in achieving optimized beam designs, leading to improved structural performance and material utilization.
التنزيلات
المراجع
Ali, R. R., Mohamad, K. M., Mostafa, S. A., et al. (2023). A Meta-Heuristic Method for Reassemble Bifragmented Intertwined JPEG Image Files in Digital Forensic Investigation. IEEE Access.
Deb, K. (1991a). Optimal design of a welded beam via genetic algorithms. AIAA Journal, 29(11), 2013–2015. https://doi.org/10.2514/3.10834
Aighuraibawi, A. H. B., Manickam, S., Abdullah, R., et al. (2023). Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm. Computer Systems Science & Engineering, 47(1).
Mirjalili, S., Jangir, P., & Saremi, S. (2017). Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Applied Intelligence, 46(1), 79–95. https://doi.org/10.1007/s10489-016-0825-8
Yang, X.-S. (2013). Metaheuristic Optimization: Nature-Inspired Algorithms and Applications (pp. 405–420). https://doi.org/10.1007/978-3-642-29694-9_16
Domagalski, Ł., & Kowalczyk, I. (2023). Genetic Algorithm Optimization of Beams in Terms of Maximizing Gaps between Adjacent Frequencies. Materials, 16(14), 4963. https://doi.org/10.3390/ma16144963
Rao, R. V., & Pawar, R. B. (2020). Constrained design optimization of selected mechanical system components using Rao algorithms. Applied Soft Computing, 89, 106141. https://doi.org/10.1016/j.asoc.2020.106141
Almufti, S. M. (2022a). Artificial Bee Colony Algorithm performances in solving Welded Beam Design problem. Computer Integrated Manufacturing Systems, 28(12).
Kaveh, A., & Bakhshpoori, T. (2019). Metaheuristics: Outlines, MATLAB Codes and Examples. Springer International Publishing. https://doi.org/10.1007/978-3-030-04067-3
S. S., V. C., & H. S., A. (2022). Nature inspired meta heuristic algorithms for optimization problems. Computing, 104(2), 251–269. https://doi.org/10.1007/s00607-021-00955-5
Almufti, S. M. (2022c). Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem. Academic Journal of Nawroz University, 11(3), 231–242. https://doi.org/10.25007/ajnu.v11n3a1499
Almufti, S. M., Alkurdi, A. A. H., & Khoursheed, I. A. (2022). Artificial Bee Colony Algorithm Performances in Solving Constraint-Based Optimization Problem. Temematique, 21(1).
Almufti, S. M. (2022b). Artificial Bee Colony Algorithm performances in solving Welded Beam Design problem. Computer Integrated Manufacturing Systems, 28(12). https://doi.org/10.24297/j.cims.2022.12.17
Deb, K. (1991b). Optimal design of a welded beam via genetic algorithms. AIAA Journal, 29(11), 2013–2015. https://doi.org/10.2514/3.10834
Eesa, A. S., Hassan, M. M., & Arabo, W. K. (2023). Letter: Application of optimization algorithms to engineering design problems and discrepancies in mathematical formulas. Applied Soft Computing, 140, 110252. https://doi.org/10.1016/j.asoc.2023.110252
Dalirinia, E., Jalali, M., Yaghoobi, M., & Tabatabaee, H. (2023). Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization. The Journal of Supercomputing. https://doi.org/10.1007/s11227-023-05513-8
Kaveh, A., & Bakhshpoori, T. (2016). Water Evaporation Optimization: A novel physically inspired optimization algorithm. Computers & Structures, 167, 69–85. https://doi.org/10.1016/j.compstruc.2016.01.008
Rao, S. S. (2019). Engineering optimization: theory and practice (5th ed.). Jogn Wiley & Sons.
Wang, S., Tu, Y., Wan, R., & Fang, H. (2012). Evaporation of Tiny Water Aggregation on Solid Surfaces with Different Wetting Properties. The Journal of Physical Chemistry B, 116(47), 13863–13867. https://doi.org/10.1021/jp302142s
Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188(2), 1567–1579. https://doi.org/10.1016/j.amc.2006.11.033
Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., & Alizadeh, Y. (2008). Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 197(33–40), 3080–3091. https://doi.org/10.1016/j.cma.2008.02.006
Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2011). Mixed variable structural optimization using Firefly Algorithm. Computers & Structures, 89(23–24), 2325–2336. https://doi.org/10.1016/j.compstruc.2011.08.002
Coello Coello, C. A. (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
Liu, J. (2005). Novel orthogonal simulated annealing with fractional factorial analysis to solve global optimization problems. Engineering Optimization, 37(5), 499–519. https://doi.org/10.1080/03052150500066646
Betania Hernández-Ocaña, & Efrén Mezura-Montes. (2009). Modified Bacterial Foraging Optimization for Engineering Design. In Intelligent Engineering Systems through Artificial Neural Networks (pp. 357–364). ASME Press. https://doi.org/10.1115/1.802953.paper45
Hedar, A.-R., & Fukushima, M. (2006). Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization. Journal of Global Optimization, 35(4), 521–549. https://doi.org/10.1007/s10898-005-3693-z
Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194(36–38), 3902–3933.
Zhang, J., Liang, C., Huang, Y., Wu, J., & Yang, S. (2009). An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization. Applied Mathematics and Computation, 211(2), 392–416. https://doi.org/10.1016/j.amc.2009.01.048
Hwang, S.-F., & He, R.-S. (2006). A hybrid real-parameter genetic algorithm for function optimization. Advanced Engineering Informatics, 20(1), 7–21.
Bernardino, H. S., Barbosa, H. J. C., & Lemonge, A. C. C. (2007). A hybrid genetic algorithm for constrained optimization problems in mechanical engineering. 2007 IEEE Congress on Evolutionary Computation, 646–653.
Lemonge, A. C. C., & Barbosa, H. J. C. (2004). An adaptive penalty scheme for genetic algorithms in structural optimization. International Journal for Numerical Methods in Engineering, 59(5), 703–736.
Zhang, M., Luo, W., & Wang, X. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences, 178(15), 3043–3074.
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.
Leite, J. P. B., & Topping, B. H. V. (1998). Improved genetic operators for structural engineering optimization. Advances in Engineering Software, 29(7–9), 529–562.
ATIQULLAH, M. I. R. M., & RAO, S. S. (2000). SIMULATED ANNEALING AND PARALLEL PROCESSING: AN IMPLEMENTATION FOR CONSTRAINED GLOBAL DESIGN OPTIMIZATION. Engineering Optimization, 32(5), 659–685.
Deb, K. (1991c). Optimal design of a welded beam via genetic algorithms. AIAA Journal, 29(11), 2013–2015. https://doi.org/10.2514/3.10834
Akhtar, S., Tai, K., & Ray, T. (2002). A socio-behavioural simulation model for engineering design optimization. Engineering Optimization, 34(4), 341–354. https://doi.org/10.1080/03052150212723
التنزيلات
منشور
كيفية الاقتباس
إصدار
القسم
الرخصة
الحقوق الفكرية (c) 2023 المجلة الأكاديمية لجامعة نوروز
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
هذا العمل مرخص بموجب Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
بيان الحقوق الفكرية
حقوق التأليف
يوافق المؤلفون الذين ينشرون في هذه المجلة على المصطلحات التالية:
١. يحتفظ المؤلفون بحقوق الطبع والنشر ومنح حق المجلة في النشر الأول مع العمل المرخص له في نفس الوقت بموجب ترخيص المشاع الإبداعي [سيسي بي-نك-ند 4.0] الذي يسمح للآخرين بمشاركة العمل مع الإقرار بحقوق التأليف والنشر الأولي في هذه المجلة.
٢. يمكن للمؤلفين الدخول في ترتيبات تعاقدية إضافية منفصلة للتوزيع غير الحصري للنسخة المنشورة من المجلة من العمل (على سبيل المثال، نشرها في مستودع مؤسسي أو نشرها في كتاب) مع الإقرار بنسخة أولية نشر في هذه المجلة.
٣. يسمح للمؤلفين وتشجيعهم على نشر عملهم عبر الإنترنت (على سبيل المثال، في المستودعات المؤسسية أو على موقعهم على الويب) قبل وأثناء عملية التقديم، حيث يمكن أن يؤدي إلى التبادلات الإنتاجية، فضلا عن الاستشهاد المبكر والأكبر للعمل المنشورة ( انظر تأثير النفاذ المفتوح).
نقل حقوق الطبع والنشر
بيان الخصوصية
المجلة الأكاديمية لجامعة نوروز ملتزمة بحماية خصوصية مستخدمي موقع المجلة هذا. سيتم استخدام الأسماء والتفاصيل الشخصية وعناوين البريد الإلكتروني التي تم إدخالها في هذا الموقع الإلكتروني فقط للأغراض المعلنة لهذه المجلة ولن يتم إتاحتها لأطراف ثالثة بدون إذن المستخدم أو الإجراءات القانونية الواجبة. موافقة المستخدمين مطلوبة لتلقي الاتصالات من المجلة الأكاديمية لجامعة نوروز للأغراض المعلنة للمجلة. ويمكن توجيه الاستفسارات المتعلقة بالخصوص إلى [email protected]