Başarıyı Kıyaslama: Modern Metasezgiseller Karmaşık Mühendislik Problemlerini Nasıl Çözüyor?

Abstract

Metaheuristik optimizasyon algoritmalarındaki son gelişmeler, önemli ve dikkate değer sonuçlar doğurmuştur.Bu metasezgisel yöntemler, ayrıca mühendislik tasarım zorluklarını değerlendirmek için de kullanılabilir. Bu çalışmada son yıllarda geliştirilen 5 metasezgiselin (Yapay Tavşan OptimizasyonuARO, Kara Dul Optimizasyonu-BWO, Çayır Köpeği Optimizasyonu-PDO, Dağ Ceylanı OptimizasyonuMGO ve Kerevit Optimizasyon Algoritması-COA) mühendislik tasarım problemlerindeki başarıları karşılaştırılmıştır. Bildiğimiz kadarıyla, bu çalışma, gerilim/basınç yayı, basınçlı kap, kaynaklı kiriş, hız düşürücü, dişli seti ve üç çubuklu kafes gibi altı tanınmış mühendislik tasarım optimizasyon problemi üzerinde bu beş metaheuristik algoritmanın ilk kapsamlı değerlendirmesini temsil etmektedir. Deneysel sonuçlar ve yakınsama hızları değerlendirildiğinde, bu araştırmada kullanılan metasezgisel tekniklerin sunulan zorluklara karşı etkili bir etkinlik gösterdiği ortaya çıkmaktadır. Elde edilen sonuçlara göre en başarılı algoritma ARO olurken onu sırasıyla BWO, MGO, COA ve PDO takip etmektedir. Gelecekteki araştırmalarda, çeşitli mühendislik zorluklarını ele almak için özellikle ARO olmak üzere farklı metasezgisel tekniklerin kullanılması hedeflenmektedir.

Keywords

• Güncel Metasezgisel Algoritmalar
• Mühendislik Tasarım Problemleri
• Yapay Tavşan Optimizasyonu
• Kara Dul Optimizasyonu

BENCHMARKING SUCCESS: HOW MODERN METAHEURISTICS SOLVE COMPLEX ENGINEERING PROBLEMS?

Abstract

Recent developments in metaheuristic optimization algorithms have yielded significant and noteworthy results. These metaheuristics can additionally be utilized to evaluate engineering design challenges. In this study, 5 metaheuristics developed in recent years (Artificial Rabbit Optimization-ARO, Black Widow Optimization-BWO, Prairie Dog Optimization-PDO, Mountain Gazelle OptimizationMGO and Crayfish Optimization Algorithm -COA) success in engineering design problems was compared. To the best of our knowledge, this work represents the first comprehensive evaluation of these five metaheuristic algorithms on six well-known engineering design optimization problems: Tension/Compression Spring, Pressure Vessel, Welded Beam, Speed Reducer, Gear Set, and Three-Bar Truss. Upon assessing the experimental outcomes and convergence speeds, it becomes evident that the metaheuristic techniques employed in this research demonstrate effective efficacy against the challenges presented. Based on the obtained results, ARO achieved the highest performance, followed sequentially by BWO, MGO, COA, and PDO. In upcoming research, the goal is to employ additional metaheuristic techniques, particularly ARO, to address various engineering challenges.

Keywords

• Metaheuristic Algorithms
• Engineering Design Problems
• Artificial Rabbit Optimization
• Black Widow Optimization

References

1. Abbassi, R., Saidi, S., Urooj, S., Alhasnawi, B. N., Alawad, M. A., & Premkumar, M. (2023). An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single-and Double-Diode Photovoltaic Cell Models. Mathematics, 11(22), 4565.
2. Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408
3. Abdollahzadeh, B., Gharehchopogh, F. S., Khodadadi, N., & Mirjalili, S. (2022). Mountain gazelle optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Advances in Engineering Software, 174, 103282.
4. Alamir, N., Kamel, S., Hassan, M. H., & Abdelkader, S. M. (2023). An effective quantum artificial rabbits optimizer for energy management in microgrid considering demand response. Soft Computing, 27(21), 15741-15768.
5. Alomoush, W., Houssein, E. H., Alrosan, A., Abd-Alrazaq, A., Alweshah, M., & Alshinwan, M. (2024). Joint opposite selection enhanced Mountain Gazelle Optimizer for brain stroke classification. Evolutionary Intelligence, 1-19.
6. Alsaiari, A. O., Moustafa, E. B., Alhumade, H., Abulkhair, H., & Elsheikh, A. (2023). A coupled artificial neural network with artificial rabbits optimizer for predicting water productivity of different designs of solar stills. Advances in Engineering Software, 175, 103315.
7. Altay, E. V. (2022). Gerçek dünya mühendislik tasarım problemlerinin çözümünde kullanılan metasezgisel optimizasyon algoritmalarının performanslarının incelenmesi. International Journal of Innovative Engineering Applications, 6(1), 65-74.
8. Arora, J. S. (2004). Introduction to optimum design. Elsevier.

9. B. Gülmez, “Stock price prediction with optimized deep lstm network ¨with artificial rabbits optimization algorithm,” Expert Systems with Applications, vol. 227, p. 120346, 2023.
10. Bacanin, N., Petrovic, A., Jovanovic, L., Zivkovic, M., Zivkovic, T., & Sarac, M. (2024, January). Parkinson’s disease induced gain freezing detection using gated recurrent units optimized by modified crayfish optimization algorithm. In 2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) (pp. 1-8). IEEE.
11. Baş, E., & Güner, L. B. (2025). Weight Optimization of Oil Type Transformer with Crayfish Optimization Algorithm. Sinop Üniversitesi Fen Bilimleri Dergisi, 10(1), 1-28.
12. Bektaş, Y. & Serteller, N. F. O (2023). Gerçek Parametre Optimizasyonu İçin Kara Dul Örümceği Optimizasyon Algoritması. International Journal of Advanced Natural Sciences and Engineering Researches, 7(11), 78-86.
13. D. Datta, A. R. Amaral, and J. R. Figueira, “Single row facility layout problem using a permutation-based genetic algorithm,” European Journal of Operational Research, vol. 213, no. 2, pp. 388–394, 2011.
14. Daulat, H., Varma, T., & Chauhan, K. (2024, April). Augmenting the Crayfish Optimization with Gaussian Distribution Parameter for Improved 111 Optimization Efficiency. In 2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS) (pp. 462-470). IEEE.
15. Dhiman, G. (2021). ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems. Engineering with Computers, 37, 323- 353.
16. E. Eyup and E. Tanyıldızı, “Güncel metasezgisel optimizasyon algoritmalarının performans karşılaştırılması,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018, pp. 1–16
17. E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, “Gsa: a gravitational search algorithm,” Information sciences, vol. 179, no. 13, pp. 2232– 2248, 2009.
18. Ekinci, S., & Izci, D. (2023). Enhancing IIR system identification: Harnessing the synergy of gazelle optimization and simulated annealing algorithms. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 5, 100225.
19. Ezugwu, A. E., Agushaka, J. O., Abualigah, L., Mirjalili, S., & Gandomi, A. H. (2022). Prairie dog optimization algorithm. Neural Computing and Applications, 34(22), 20017-20065.
20. G. G. Emel and Ç. Taşkın, “Genetik algoritmalar ve uygulama alanları,” Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 21, no. 1, pp. 129–152, 2002.
21. G. Hu, B. Du, X. Wang, and G. Wei, “An enhanced black widow optimization algorithm for feature selection,” Knowledge-Based Systems, vol. 235, p. 107638, 2022.
22. H. Bakır, “Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem,” Expert Systems with Applications, vol. 240, p. 122460, 2024.
23. He, X., & Zhou, Y. (2018). Enhancing the performance of differential evolution with covariance matrix self-adaptation. Applied Soft Computing, 64, 227-243.
24. Hussein, A. H. A., Sunil, G., Kotha, M., Alzubaidi, L. H., & Arunasree, B. (2023, November). Prairie Dog Optimization Based Efficient Task Scheduling in the Cloud Computing. In 2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-5). IEEE.
25. Izci, D., Ekinci, S., & Hussien, A. G. (2024). Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm. Scientific Reports, 14(1), 7945.
26. Jia, H., Rao, H., Wen, C., & Mirjalili, S. (2023). Crayfish optimization algorithm. Artificial Intelligence Review, 56(Suppl 2), 1919-1979.
27. Kalyon, M., & Arslan, S. (2024). Comparison of Black Widow Optimization and Aquila Optimizer with Current Metaheuristics. International Journal of Multidisciplinary Studies and Innovative Technologies, 8(1), 17-25.
28. Khalil, A. E., Boghdady, T. A., Alham, M. H., & Ibrahim, D. K. (2023). Enhancing the conventional controllers for load frequency control of isolated microgrids using proposed multi-objective formulation via artificial rabbits optimization algorithm. IEEE Access, 11, 3472-3493.
29. Khodadadi, N., El-Kenawy, E. S. M., De Caso, F., Alharbi, A. H., Khafaga, D. S., & Nanni, A. (2023). The Mountain Gazelle Optimizer for truss structures optimization. Applied Computing and Intelligence, 3(2), 116-144.
30. Kuyu, Y. Ç. (2023). Optimizasyon Problemleri Için Yeni Metasezgisel Yaklaşımlar (Doctoral dissertation, Bursa Uludag University (Turkey)).
31. L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, and W. Zhao, “Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 114, p. 105082, 2022
32. M. A. Albadr, S. Tiun, M. Ayob, and F. Al-Dhief, “Genetic algorithm based on natural selection theory for optimization problems,” Symmetry, vol. 12, no. 11, p. 1758, 2020
33. M. Sharma and P. Kaur, “A comprehensive analysis of nature-inspired meta-heuristic techniques for feature selection problem,” Archives of Computational Methods in Engineering, vol. 28, pp. 1103–1127, 2021
34. Özbay, F. A., & Özbay, E. Martı Optimizasyon Algoritmasının Kısıtlı Mühendislik Tasarım Problemleri İçin Performans Analizi. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 8(15), 469-485
35. Öznur and S. Korukoğlu, “Genetik algoritma yaklaşımı ve yöneylem ¨ arastırmasında bir uygulama,” Yonetim ve Ekonomi Dergisi ¨, vol. 10, no. 2, pp. 191–208, 2003.
36. S. S. Chouhan, A. Kaul, and U. P. Singh, “Soft computing approaches for image segmentation: a survey,” Multimedia Tools and Applications, vol. 77, no. 21, pp. 28 483–28 537, 2018
37. Sahoo, G. K., Choudhury, S., Rathore, R. S., & Bajaj, M. (2023). A novel prairie dog-based meta-heuristic optimization algorithm for improved control, better transient response, and power quality enhancement of hybrid microgrids. Sensors, 23(13), 5973.
38. Shikoun, N. H., Al-Eraqi, A. S., & Fathi, I. S. (2024). BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection. IEEE Access, 12, 28621-28635.
39. V. Hayyolalam and A. A. P. Kazem, “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 87, p. 103249, 2020.
40. Xiao, B., Wang, R., Deng, Y., Yang, Y., & Lu, D. (2024, March). Simplified Crayfish Optimization Algorithm. In 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (Vol. 7, pp. 392-396). IEEE.
41. Y. Wang, Y. Xiao, Y. Guo, and J. Li, “Dynamic chaotic oppositionbased learning-driven hybrid aquila optimizer and artificial rabbits optimization algorithm: framework and applications,” Processes, vol. 10, no. 12, p.2703, 2022.
42. Zhao, W., Wang, L., & Mirjalili, S. (2022). Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 388, 114194.
43. Zhong, C., Li, G., Meng, Z., Li, H., Yildiz, A. R., & Mirjalili, S. (2025). Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers. Neural Computing and Applications, 37(5), 3641-3683.