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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
85-99
  • Turkish

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