Çift-Girişim Tabanlı İyileştirme Algoritmasının Sayısal İyileştirme Fonksiyonları Üzerinde Performans Analizi

Year 2023, , 545 - 552, 28.07.2023

Mehtap Köse Ulukök

https://doi.org/10.21605/cukurovaumfd.1334219

Abstract

Sayısal iyileştirme, mühendislik alanında en çok uğraşılan problemlerden biridir. Bu çalışmada, son zamanlarda geliştirilen Çift-Girişim Tabanlı İyileştirme Algoritması’nın (Bi-Attempted Based Optimization Algorithm) (ABaOA) arama yakınsama kabiliyeti yirmi iyi bilinen referans fonksiyonu üzerinde test edilmiştir. Elde edilen sonuçlar Genetik Algoritma (GA) ve Temel İyileştirme Algoritması (Based Optimization Algoritması) (BaOA) ile karşılaştırılmıştır. ABaOA, tüm yinelemeler boyunca iki sabit adım boyutlu çoğaltma parametresi ve iki işlem operatörü kullanan nüfus tabanlı bir Evrimsel Algoritma’dır. Evrimsel algoritmalar arama alanı boyunca global optimuma hızlı bir şekilde yaklaşır ve uygulanabilir bir çözümü garanti ederler. Deneysel sonuçlar ABaOA'nın hem BAOA'ya hem de GA'ya göre global optimuma daha hızlı yaklaştığını açıkça göstermiştir.

Keywords

Bilgisayar zekası, Evrimsel algoritmalar, İyileştirme problemleri

Performance Analysis of Bi-Attempted Based Optimization Algorithm on Numerical Optimization Functions

Abstract

Numerical optimization is one of the most challenging problem in engineering field. In this study, a recently developed Bi-Attempted Based Optimization Algorithm (ABaOA) is tested on twenty well-known benchmark functions to find its search convergence capability. Obtained results are compared with the Genetic Algorithm (GA) and the Base Optimization Algorithm (BaOA). ABaOA is a population-based Evolutionary Algorithm that uses two fixed step-size displacement parameter and two arithmetic reproduction operators throughout all the iterations. Evolutionary algorithms converge to the global optimum throughout the search space quickly, and they guarantee a feasible solution. The experimental results clearly showed that the ABaOA reaches the global optimum faster than the BaOA and the GA.

Keywords

Computational intelligence, Evolutionary algorithms, Optimization problems

References

• 1. Campbell S.D., Sell, D., Jenkins, R.P., Whiting, E.B., Fan, J.A., Werner D.H., 2019. Review of Numerical Optimization Techniques for Meta-Device Design. Optical Materials Express, 9(4), 1842-1863.
• 2. Afshar, M.H., Mariño, M.A., 2007. A Parameter-Free Self-Adapting Boundary Genetic Search for Pipe Network Optimization. Comp. Optim Appl, 37(1), 83-102.
• 3. Melo, V.V.de., Banzhaf, W., 2018. Drone Squadron Optimization: A Novel Self-Adaptive Algorithm for Global Numerical Optimization. Neural Comp. Appl, 30(10), 3117-3144.
• 4. Xiang, Y., Peng, Y., Zhong, Y., Chen, Z., Lu, X., Zhong, X., 2014. A Particle Swarm Inspired Multi-Elitist Artificial Bee Colony Algorithm for Real-Parameter Optimization. Comp. Optim Appl, 57, 493-516.
• 5. Holland, J.H., 1962. Outline for a Logical Theory of Adaptive Systems. Journal of the ACM (JACM), 9(3), 297-314.
• 6. Deb, K., Padhye, N., 2014. Enhancing Performance of Particle Swarm Optimization Through an Algorithmic Link with Genetic Algorithms, Comp Optim Appl, 57(3) 761-794.
• 7. Dorigo, M., Blum, C., 2005. Ant Colony Optimization Theory: A Survey Theoretical, Computer Science, 344, 243-278.
• 8. Karaboga, D., Akay, B., 2009. A Comparative Study of Artificial Bee Colony Algorithm. Appl Math Comp., 214(1), 108-132.
• 9. Tuncel, O., Aydın, H., 2023. Optimization of Nd:YAG Laser Welding Factors of Cold Rolled Strenx 700 CR Steel by Taguchi Method. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi. 38(1), 85-92.
• 10. Gençal, M.C., 2022. Bipolar Particle Swarm Optimization Algorithm, Çukurova Üniversitesi Mühendislik Fakültesi Dergisi. 37(3), 617-625.
• 11. Alhamad, A., Günal, A.Y., 2022. Optimization of Water Distribution System within Tented Camps. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi. 37(1), 23-31.
• 12. Madenli, Ö., Deveci, E. Ü., 2021. Alkaline Pre-Treatment Optimization of Agro-Industrial Waste Apple Pulp with Box-Behnken Response Surface Methodology. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi. 36(3) 769-780.
• 13. Das, K.N., Mishra, R., 2013. Chemo-Inspired Genetic Algorithm for Function Optimization, Appl Math Comp, 220, 394-404.
• 14. Khuat, T.T., Le, M.H., 2017. A Genetic Algorithm with Multi-Parent Crossover Using Quaternion Representation for Numerical Function Optimization. Applied Intelligence, 46(4), 810-826.
• 15. Pan, X., Jiao, L., Liu, F., 2010. An Improved Multi-Agent Genetic Algorithm for Numerical Optimization, Natural Computing, 10(1), 487-506.
• 16. Zang, W., Ren, L., Lui, X., 2018. A Cloud Model Based DNA Genetic Algorithm for Numerical Optimization Problems, Future Generation Comp Sys, 81, 465-477.
• 17. Alatas, B., 2010. Chaotic Bee Colony Algorithms for Global Numerical Optimization, Expert Sys Appl., 37(8), 5682-5687.
• 18. Karaboga, D., Gorkemli, B., 2014. A Quick Artificial Bee Colony (qABC) Algorithm and its Performance On Optimization Problems. Applied Soft Computing, 23, 227-238.
• 19. Cao, Y., Lu, Y., Pan, X., Sun, N., 2019. An Improved Global Best Guided Artificial Bee Colony Algorithm for Continuous Optimization Problems. Cluster Computing, 22(2), 3011-3019.
• 20. Badem, H., Basturk, A., Caliskan, A., Yuksel, M.E, 2018. A New Hybrid Optimization Method Combining Artificial Bee Colony and Limited-Memory BFGS Algorithms For Efficient Numerical Optimization. Applied Soft Computing, 70, 826-844.
• 21. Ghanem, W.A., Jantan, A., 2016. Hybridizing Artificial Bee Colony with Monarch Butterfly Optimization for Numerical Optimization Problems, Neural Comp Appl, 30(1), 163-181.
• 22. Pan, X., Lu, Y., Sun, N., Li, S., 2019. A Hybrid Artificial Bee Colony Algorithm with Modified Search Model for Numerical Optimization, Cluster Computing, 22(2), 2581-2588.
• 23. Ulukök, M.K., 2017. Impact of Genetic Algorithm’s Parameters on Solution of Numerical Optimization Benchmark Problems. IEEE 9th International Conference on Computational Intelligence and Communication Networks (CICN2017), 16-17 September 2017, Final Üniversitesi, Girne, KKTC.
• 24. Khalilpourazari, S., Khalilpourazary, S., 2019. An Efficient Hybrid Algorithm Based on Water Cycle and Moth-Flame Optimization Algorithms for Solving Numerical and Constrained Engineering Optimization Problems. Soft Computing, 23(5), 1699-1722.
• 25. Wang, G.G., Deb, S., Cui, Z., 2014. Monarch Butterfly Optimization, Neural Comp Appl, 31(7), 1995-2014.
• 26. Wang, G.G., Gandomi, A.H., Zhao, X. Chu, H.C.E., 2016. Hybridizing Harmony Search Algorithm with Cuckoo Search for Global Numerical Optimization, Soft Computing, 20(1), 273-285.
• 27. Shehab, M., Khader A.T., Louchedi, M., Alomari, O.A., 2019. Hybridizing Cuckoo Search Algorithm with Bat Algorithm for Global Numerical Optimization. The Journal of Supercomputing, 75(5), 2395-2422.
• 28. Zhang, Y., Jin, Z., Chen, Y., 2020. Hybridizing Grey Wolf Optimization with Neural Network Algorithm for Global Numerical Optimization Problems. Neural Comp Appl, 32, 10451-10470.
• 29. Abualigah, L.M., Khader, A.T., Hanandeh, E.S., 2019. Modified Krill Herd Algorithm for Global Numerical Optimization Problems. Advances in Nature-Inspired Comp Appl, Springer, 205-221.
• 30. Ahandani, M.A., Vakil-Baghmisheh, M.T., Talebi, M., 2014. Hybridizing Local Search Algorithms for Global Optimization. Comp Optim Appl, 59(3), 725-748.
• 31. Ye, X., Wang, P., 2019. Impact of Migration Strategies and İndividual Stabilization on Multi-Scale Quantum Harmonic Oscillator Algorithm for Global Numerical Optimization Problems. Applied Soft Computing, 85, 105800.
• 32. Arora, S., Anand, P., 2019. Chaotic Grasshopper Optimization Algorithm for Global Optimization. Neural Comp Appl, 31(8), 4385-4405.
• 33. Cassioli, A., Locatelli, M., Schoen, F., 2010. Dissimilarity Measures for Population-Based Global Optimization Algorithms. Comp Optim Appl, 45(2), 257-281.
• 34. Chen, B., Lei, H., Shen, H., Liu, Y., Lu, Y., 2019. A Hybrid Quantum-Based PIO Algorithm for Global Numerical Optimization. Science China Information Sciences, 62(7), 70203.
• 35. Civicioglu, P., 2013. Backtracking Search Optimization Algorithm for Numerical Optimization Problems. Applied Maths Comp, 219(15), 8121-8144.
• 36. Gaviano, M., Lera, D., 2002. A Complexity Analysis of Local Search Algorithms in Global Optimization. Optim Meth Software, 17(1), 113-127.
• 37. Ghetas, M., Chan, H.Y., 2018. Integrating Mutation Scheme İnto Monarch Butterfly Algorithm for Global Numerical Optimization. Neural Comp Appl, 32, 2165-2181.
• 38. Dai, J. He. H., Song, X., 2014. The Combination Stretching Function Technique with Simulated Annealing Algorithm for Global Optimization. Optim Methods and Software, 29(3), 629-645.
• 39. Hedar, A.R., Fukushima, M., 2002. Hybrid Simulated Annealing and Direct Search Method for Nonlinear Unconstrained Global Optimization. Optim Methods and Software, 17(5), 891-912.
• 40. Chetty, S., Adewumi, A.O., 2013. Three New Stochastic Local Search Algorithms for Continuous Optimization Problems. Comp Optim Appl, 56(3), 675-721.
• 41. Narayanam, G., Ranjan, K., Kumar S., 2019. Comparison of Optimization Strategies for Numerical Optimization, Software Engineering. Springer, Singapore, 709-716.
• 42. Ulukök, M.K., 2021. Bi-Attempted Based Optimization Algorithm for Numerical Optimization Problems. European Journal of Science and Technology, Special Issue(26), 466-471.
• 43. Yıldız, B., Ulukök, M.K., Bashiry, V., 2023. Bi Attempted Base Optimization Algorithm on Optimization of Hydrosystems. Water Resources Management, 1-13.