Research Article
Year 2021,
Volume: 36 Issue: 2, 925 - 938, 05.03.2021
Abstract
Bu çalışmada, sezgisel ve sayısal
yöntemlerin yapısında var olan eksiklikleri gidermek amacıyla melez bir
optimizasyon yönteminin geliştirilmesi üzerinde durulmuştur. Sezgisel yöntemler
kesin çözümü garanti edemezler. Ancak, sayısal yöntemlerden göreceli olarak
daha hızlı çalıştırılabilirler. Öte yandan, sayısal yöntemler ise güçlü
matematiksel çözümler içerdikleri için kesin çözüme ulaşabilmektedirler.
Uygulamalarımızda, sezgisel optimizasyon yöntemlerinden Parçacık Sürü
Optimizasyonu (PSO) yöntemi ile sayısal optimizasyon yöntemlerinden
Broydon-Fletcher-Goldfarb-Shanno (BFGS) yöntemi birleştirilerek optimum çözüme
daha kesin ve daha hızlı bir şekilde ulaşılması amaçlanmıştır. Geliştirilen
optimizasyon yönteminin algoritmasında, çözüm öncelikle BFGS ile aranmaktadır.
Böylece, amaç fonksiyonu için en küçük veya en büyük noktalar belirlenmektedir.
Daha sonra, PSO ile bu noktalar arasında eleme işlemi gerçekleştirilmektedir.
Nihai sonuca ulaşılana kadar, ara çözüm noktaları BFGS ile PSO arasında sürekli
aktarılmaktadır. Özgün BFGS ve özgün PSO kullanılarak, önerilen önce BFGS sonra
PSO melez yöntemi ve tersi melez yöntem (önce PSO sonra BFGS) olmak üzere iki
farklı yöntem; araştırmacılar arasında sıklıkla kullanılan test fonksiyonları
üzerinde çalıştırılarak uygulanmıştır.
References
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Year 2021,
Volume: 36 Issue: 2, 925 - 938, 05.03.2021
Abstract
References
- D. Karaboğa, Yapay Zeka Optimizasyon Algoritmaları, 2nd ed. Ankara: Nobel Yayın Dağıtım, 2011.J. Nocedal and S. Wright, Numerical Optimization. 1999.Wikipedia, “Broyden–Fletcher–Goldfarb–Shanno algorithm.” [Online]. Available: https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm. [Accessed: 03-Oct-2016].J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948 vol.4, 1995.R. Fletcher, “A new approach to variable metric algorithms,” Oxford Journals, vol. 13, no. 3, pp. 317–322, 1970.M. Lowrie and B. Wah, “Learning Heuristic Function for Numeric Optimization Problems,” IEEE, pp. 443–450, 1988.W. C. Davidon, “Variable metric method for minimization,” SIAM J. Optim., vol. 1, no. 1, pp. 1–17, 1991.J. Semeter and M. Mendillo, “Nonlinear optimization technique for ground-based atmospheric emission tomography,” IEEE Trans. Geosci. Remote Sens., vol. 35, no. 5, pp. 1105–1116, 1997.D. H. Li and M. Fukushima, “A modified BFGS method and its global convergence in nonconvex minimization,” J. Comput. Appl. Math., vol. 129, pp. 15–35, 2001.J. L. Morales, “A numerical study of limited memory BFGS methods,” Appl. Math. Lett., vol. 15, no. 4, pp. 481–487, 2002.Business Standford Software Inc., “SNOPT.” [Online]. Available: http://www.sbsi-sol-optimize.com/asp/sol_product_snopt.htm. [Accessed: 05-Sep-2016].M. Molga and C. Smutnicki, “Test functions for optimization needs,” Test Funct. Optim. needs, pp. 1–43, 2005.T. A. A. Victoire and A. E. Jeyakumar, “Deterministically guided PSO for dynamic dispatch considering valve-point effect,” Electr. Power Syst. Res., vol. 73, no. 3, pp. 313–322, 2005.X. Yan, C. Ka Wing, and L. Mingbo, “Improved BFGS method for optimal power flow calculation with transient stability constraints,” IEEE Power Eng. Soc. Gen. Meet., p. 434–439 Vol. 1, 2005.N. M. Nawi, M. R. Ransing, and R. S. Ransing, “An improved learning algorithm based on the Broyden-Fletcher-GoldfarbShanno (BFGS) method for back propagation neural networks,” Proc. - ISDA 2006 Sixth Int. Conf. Intell. Syst. Des. Appl., vol. 1, pp. 152–157, 2006.N. Andrei, “A scaled BFGS preconditioned conjugate gradient algorithm for unconstrained optimization,” Appl. Math. Lett., vol. 20, no. 6, pp. 645–650, 2007.R. E. Perez and K. Behdinan, “Particle swarm approach for structural design optimization,” Comput. Struct., vol. 85, no. 19–20, pp. 1579–1588, 2007.Y. Xiao, Z. Wei, and Z. Wang, “A limited memory BFGS-type method for large-scale unconstrained optimization,” Comput. Math. with Appl., vol. 56, no. 4, pp. 1001–1009, 2008.G. Yuan and X. Lu, “A new backtracking inexact BFGS method for symmetric nonlinear equations,” Comput. Math. with Appl., vol. 55, no. 1, pp. 116–129, 2008.S. Zhao, J. J. Liang, P. N. Suganthan, and M. F. Tasgetiren, “Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization,” Evol. Comput. 2008. CEC 2008. (IEEE World Congr. Comput. Intell. IEEE Congr., pp. 3845–3852, 2008.Y. Özsağlam and M. Çunkaş, “Optimizasyon Problemlerinin Çözümü için Parçacık Sürü Optimizasyonu Algoritması,” Politek. Derg., vol. 11, no. 4, pp. 299–305, 2008.N. Thomas and M. Reed, “A hybrid algorithm for continuous optimisation,” 2009 IEEE Congr. Evol. Comput., pp. 1–6, 2009.S. Li and M. Tan, “Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm,” Neurocomputing, vol. 73, no. 10–12, pp. 2089–2096, 2010.S. Li, M. Tan, I. W. Tsang, and J. T. Y. Kwok, “A hybrid PSO-BFGS strategy for global optimization of multimodal functions,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 41, no. 4, pp. 1003–1014, 2011.A. M. Nezhad, R. A. Shandiz, and A. E. Jahromi, “A particle swarm–BFGS algorithm for nonlinear programming problems,” Comput. Oper. Res., vol. 40, no. 4, pp. 963–972, 2013.Y. Shi, H. Liu, L. Gao, and G. Zhang, “Cellular particle swarm optimization,” Inf. Sci. (Ny)., vol. 181, no. 20, pp. 4460–4493, 2011.Wikipedia, “Altın Oran.” [Online]. Available: https://tr.wikipedia.org/wiki/Alt%C4%B1n_oran. [Accessed: 09-Sep-2016].S. İplikçi, “Lineer Olmayan Programlama,” 2010.
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Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler |
| Authors | |
| Publication Date | March 5, 2021 |
| Submission Date | September 20, 2019 |
| Acceptance Date | November 15, 2020 |
| Published in Issue | Year 2021 Volume: 36 Issue: 2 |