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DİFERANSİYEL GELİŞİM ALGORİTMASI

DIFFERENTIAL EVOLUTION ALGORITHM

Journal Name:

  • İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi

Publication Year:

  • 2006

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
There are several techniques developed for solving nonlinear optimization problems. These problems become more difficult related to the number of variables and types of parameters. Solution of these problems with deterministic methods may include difficulties in both modeling and solving depending on the type of the problem. Heuristics are developed in order to overcome these difficulties. Differential evolutionary algorithm (DEA) related to genetic algorithm concerning process and operators, is an efficient population based heuristic optimization technique especially for problems of continuous variables. In this paper, DEA is presented and its operators are detailed. DEA is applied to a problem obtained from literature and results are compared with genetic algorithm.
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Abstract (Original Language): 
Doğrusal olmayan problemlerin çözümüne yönelik olarak geliştirilmişbir çok teknik söz konusudur. Özellikle değişken sayısına ve veri tiplerine bağlı olarak problemlerin zorluk dereceleri de artabilmektedir. Bu tip problemlerin deterministik yöntemlerle çözümü, problemin yapısına bağlı olarak hem modellemede hem de çözüm sürecinde zorluklar içerebilmektedir. Bunların üstesinden gelebilmek için sezgisel yöntemler geliştirilmiştir. Diferansiyel gelişim algoritması (DGA), özellikle sürekli verilerin söz konusu olduğu problemlerde etkin sonuçlar verebilen, işleyişve operatörleri itibariyle genetik algoritmaya dayanan populasyon temelli sezgisel optimizasyon tekniklerinden biridir. Bu çalışmada, diferansiyel gelişim algoritması tanıtılmışve aşamaları anlatılmıştır. Çalışmanın sonunda, DGA literatürden alınmışbir probleme uygulanmış, sonuçlar genetik algoritma sonuçları ile karşılaştırılmıştır.
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  • 9
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  • Turkish

REFERENCES

References: 

Ali, M. M., Törn, A., (2004), “Population Set-BasedGlobal Optimization
Algorithms: Some Modifications and Numerical Studies”, Computer & Operations
Research, 31, 1703-1725.
Bazaraa, M.S., Shetty, L.M., (1985), “Non-Linear Programming: Theory and
Algorithms”, A.B.D., John Wiley & Sons.
Becerra, R.L., Coello, C.A.C., (2005), “Cultured Differential Evolution for
Constrained Optimization”, Comput. Methods Appl. Mech. Engnrg., Baskıda.
Timur KESKİNTÜRK
98
Bergey, P.K., Ragsdale, C., (2005), “Modified Differential Evolution: A Greedy
Random Strategy for Genetic Recombination”, Omega, 33, 255-265.
Goldberg, D.E., (1989), “Genetic Algoritms in Search Optimization and Machine
Learning”, A.B.D., Addison Wesley Publishing Company.
Holland, J. H., (1975), “Adaptation in Natural and Artificial Systems: An
Introductory Analysis with Applications to Biology,Control and Artificial
Intelligence”, University of Michigan Press.
Hrstka, O., Kucerova, A., (2004), “Improvemenets ofReal Coded Genetic
Algorithms Based on Differential Operators Preventing Premature Convergence”,
Advances in Engineering Software, 35, 237-246.
Storn, (2001), http://www.icsi.berkeley.edu/~storn/code.html, [02.02.2006].
Karaboğa, D, (2004), “Yapay Zeka Optimizasyonu Algoritmaları”, İstanbul, Atlas
Yayın Dağıtım.
Lin, Y.C., Hwang, K.S., Wang, F.S., (2004), “A Mixed-Coding Scheme of
Evolutionary Algorithms to Solve Mixed-Integer Nonlinear Programming
Problems”, Computers and Mathematics with Applications, 47, 1295-1307.
Mayer, D.G., Kinghorn, B.P., Archer, A.A., (2005), “Differential Evolution – An
Easy and Efficient Evolutionary Algorithm for ModelOptimisation”, Agricultural
Systems, 83, 315-328.
Michalewicz, Z., (1992), “Genetic Algorithms + DataStructure = Evolution
Programs”, A.B.D., Springer & Verlag.
Schmidt, H., Thierauf, G., (2005), “A Combined Heuristic Optimization
Technique”, Advances in Engineering Software, 36, 11-19.
Shiakolas, P. S., Koladiye, D., Kebrle, J., (2005),“On The Optimum Synthesis of
Six-Bar Linkages Using Differential Evolution and The Geometric Centroid of
Precision Positions Technique”, Mechanism and Machine Theory, 40, 319-335.
Storn, R., Price, K., (1995), “Differential Evolution: A Simple and Efficient
Adaptive Scheme for Global Optimization over Continuous Spaces”, Technical
Report TR-95-012, International Computer Science Institute, Berkeley.
Sun, J., Zhang, Q., P.K. Tsang, E., (2005), “DE/EDA: A New Evolutionary
Algorithm for Global Optimization”, Information Sciences, 169, 249-262.
Tang, J.F., Wang, D., (1998), “A Hybrid Genetic Algorithm for A Type of
Nonlinear Programming Problem”, Computers Math. Applic., 36, 11–21.
İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi Bahar2006/1
99
Zaharie, D., (2002), “Critical Values for Control Parameters of Differential
Evolution Algorithms”, Proceedings of Mendel 2002, 8th International Conference
on Soft Computing.

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