IMPROVING SUPPLY-DEMAND-BASED OPTIMIZATION ALGORITHM WITH FDB METHOD: A COMPREHENSIVE RESEARCH ON ENGINEERING DESIGN PROBLEMS

Öz

In this study, an improved version of the supply-demand-based optimization (SDO) algorithm, a recently developed meta-heuristic search method, was developed. In order to model the supply-demand processes more effectively in SDO, solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. In order to test and verify the performance of the developed FDB-based SDO algorithm, CEC 2017, a modern benchmark suite, was used. This suite has four different types and thirty unconstrained test problems. These problems are designed in 30/50/100 dimensions to test and verify the performance of the proposed algorithm in search spaces of different types and dimensions. In addition, twenty engineering design problems were used to test and verify the performance of the proposed FDBSDO variations in constrained engineering design problems. Data from both experimental studies were analyzed using non-parametric statistical test methods. According to the results of the analysis, FDBSDO variations showed superior performance compared to the base algorithm in all experimental studies, with constrained/unconstrained, unimodal/multi-modal/hybrid/composition problem types and different dimensions. The implementation of the FDB selection method eliminated the premature convergence problem of the SDO algorithm. The proposed FDBSDO algorithm has the ability to sensitively search, effectively provide diversity, and build a strong balance between exploitation-exploration. Source code of the proposed FDBSDO: https://www.mathworks.com/matlabcentral/fileexchange/84560-fdbsdo-an-improved-version-of-supply-demand-optimizer

Anahtar Kelimeler

• Supply-Demand-Based Optimization
• Fitness-distance balance
• Exploitation and exploration
• Meta-heuristic optimization
• Engineering design problems

ARZ-TALEP TABANLI OPTİMİZASYON ALGORİTMASININ FDB YÖNTEMİ İLE İYİLEŞTİRİLMESİ: MÜHENDİSLİK TASARIM PROBLEMLERİ ÜZERİNE KAPSAMLI BİR ARAŞTIRMA

Öz

Bu makale çalışmasında son zamanlarda geliştirilmiş güncel bir meta-sezgisel arama (MSA) yöntemi olan arz-talep tabanlı (Supply-Demand-Based Optimization, SDO) algoritmasının iyileştirilmiş bir versiyonu geliştirilmektedir. SDO’da arz-talep süreçlerini daha etkili bir şekilde modelleyebilmek amacıyla arama sürecine rehberlik eden çözüm adayları uzaklık-uygunluk dengesi (fitness-distance balance, FDB) yöntemi kullanılarak belirlenmiştir. Geliştirilen FDB-tabanlı SDO algoritmasının performansını test etmek ve doğrulamak amacıyla güncel bir karşılaştırma problemleri havuzu olan CEC 2017 kullanılmıştır. Bu havuzda dört farklı tipte ve otuz adet kısıtsız test problemi bulunmaktadır. Önerilen algoritmanın farklı tiplerdeki ve farklı boyutlardaki arama uzaylarındaki performansını test etmek ve doğrulamak için test problemleri 3/50/100 boyutta tasarlanmıştır. Ayrıca, önerilen FDB-SDO varyasyonlarının kısıtlı mühendislik problemlerindeki performanslarını test etmek ve doğrulamak için ise 20 adet mühendislik tasarım problemi kullanılmıştır. Her iki deneysel çalışmadan elde edilen veriler parametrik olmayan istatistiksel test yöntemleri kullanılarak analiz edilmiştir. Analiz sonuçlarına göre kısıtlı/kısıtsız, tekmodlu/çokmodlu/melez/komposizyon problem türlerinde ve farklı boyutlarda olmak üzere tüm deneysel çalışmalarda FDB-SDO varyasyonlarının baz algoritmaya kıyasla üstün bir performans sergilemişlerdir. FDB yönteminin tatbik edilmesiyle birlikte SDO algoritmasının prematüre yakınsama problemi çözülmüştür. Önerilen FDBSDO algoritması hassas arama yapabilme, çeşitliliği etkili bir şekilde sağlamaya bilme ve dengeli arama yapabilme yeteneklerine sahiptir. Önerilen FDBSDO’nun kaynak kodu: https://www.mathworks.com/matlabcentral/fileexchange/84560-fdbsdo-an-improved-version-of-supply-demand-optimizer

Anahtar Kelimeler

• Arz-talep tabanlı arama algoritması,
• Uygunluk-mesafe dengesi seçim yöntemi
• Komşuluk araması ve çeşitlilik
• Meta-sezgisel optimizasyon
• Mühendislik tasarım problemleri

Kaynakça

1. Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22.
2. Amir M.: Towards An Approach For Effectively Using Intuition In Large-Scale Decision-Making Problems, PhD Thesis, University of Debrecen (2013).
3. Aras, S., Gedikli, E., & Kahraman, H. T. (2020). A novel Stochastic Fractal Search Algorithm with Fitness-Distance Balance for Global Numerical Optimization. Swarm and Evolutionary Computation, 100821.
4. Awad, N. H., Ali, M. Z., Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2016). Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Tech. Rep.
5. Barshandeh, S., & Haghzadeh, M. (2020). A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with Computers, 1-44.
6. Carrasco, J., García, S., Rueda, M. M., Das, S., & Herrera, F. (2020). Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm and Evolutionary Computation, 54, 100665.
7. Chen, H., Xu, Y., Wang, M., & Zhao, X. (2019). A balanced whale optimization algorithm for constrained engineering design problems. Applied Mathematical Modelling, , 71, 45-59.
8. Cheng, Min-Yuan, and Doddy Prayogo. "Symbiotic organisms search: a new metaheuristic optimization algorithm." Computers & Structures 139 (2014): 98-112.

9. Del Ser, J., Osaba, E., Molina, D., Yang, X. S., Salcedo-Sanz, S., Camacho, D., ... & Herrera, F. (2019). Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation, 48, 220-250.
10. Demir, F.B., Tuncer, T. & Kocamaz, A.F. A chaotic optimization method based on logistic-sine map for numerical function optimization. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-04815-9
11. Dong, M., Wang, N., Cheng, X., Jiang, C.: Composite differential evolution with modified oracle penalty method for constrained optimization problems. Mathematical problems in engineering, 1-15 (2014), http://dx.doi.org/10.1155/2014/617905.
12. Dorigo, M., & Di Caro, G. (1999, July). Ant colony optimization: a new meta-heuristic. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470-1477). IEEE.
13. Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
14. Eftimov, T., Korošec, P., & Seljak, B. K. (2017). A novel approach to statistical comparison of meta-heuristic stochastic optimization algorithms using deep statistics. Information Sciences, 417, 186-215.
15. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. simulation, 76(2), 60-68.
16. Glover, F., & Hao, J. K. (2019). Diversification-based learning in computing and optimization. Journal of Heuristics, 25(4-5), 521-537.
17. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
18. Holland, J.H., 1975. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Q. Rev. Biol. 1, 211. http://dx.doi.org/10.1086/418447.
19. Ibrahim, R. A., Elaziz, M. A., Oliva, D., Cuevas, E., & Lu, S. (2019). An opposition-based social spider optimization for feature selection. Soft Computing, 23(24), 13547-13567.
20. Kahraman, H. T., Aras, S., & Gedikli, E. (2020). Fitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms. Knowledge-Based Systems, 190, 105169.
21. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.
22. Kumar, A., Wu, G., Ali, M. Z., Mallipeddi, R., Suganthan, P. N., & Das, S. (2020). A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm and Evolutionary Computation, 100693.
23. Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2013). Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore.
24. Lin, X., Zhang, F., Xu, L.: Design of Gear Reducer Based on FOA Optimization Algorithm. In International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, pp. 240-247. Springer, Cham (2017).
25. Long, W., Wu, T., Liang, X., Xu, S.: Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Expert systems with applications 123, 108-126 (2019).
26. Mirjalili, S. (2015). The ant lion optimizer. Advances in engineering software, 83, 80-98.
27. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
28. Mozaffari, A., Emami, M. & Fathi, A. A comprehensive investigation into the performance, robustness, scalability and convergence of chaos-enhanced evolutionary algorithms with boundary constraints. Artif Intell Rev 52, 2319–2380 (2019). https://doi.org/10.1007/s10462-018-9616-4
29. P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger and S. Tiwari, "Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization", Technical Report, Nanyang Technological University, Singapore, May 2005 AND KanGAL Report #2005005, IIT Kanpur, India.
30. Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
31. Piotrowski, A. P., Napiorkowski, J. J., & Rowinski, P. M. (2014). How novel is the “novel” black hole optimization approach?. Information Sciences, 267, 191-200.
32. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
33. Ravindran, A., Reklaitis, G. V., & Ragsdell, K. M. (2006). Engineering optimization: methods and applications. John Wiley & Sons.
34. Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47.
35. Sayed, G.I., Tharwat, A. & Hassanien, A.E. Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Appl Intell 49, 188–205 (2019). https://doi.org/10.1007/s10489-018-1261-8
36. Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
37. Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing. In Simulated annealing: Theory and applications (pp. 7-15). Springer, Dordrecht.
38. Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 210-214). IEEE.
39. Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304.
40. Zhao, W., Wang, L., & Zhang, Z. (2019). Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access, 7, 73182-73206.
41. Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300.