YARASA ALGORİTMASI VE KLONAL SEÇİM ALGORİTMASININ OPTİMİZASYON PROBLEMLERİ İLE PERFORMANS ANALİZİ

Yıl 2017, , 85 - 92, 29.08.2017

Ezgi Deniz Ülker

https://doi.org/10.17482/uumfd.336407

Öz

 Evrimsel algoritmalar, özellikle
optimizasyon alanında çalışan bir çok farklı araştırmacı tarafından tercih
edilmektedir. Evrimsel algoritmaların verilen problemleri optimize etmenin yanı
sıra, bu problemleri az sayıda iterasyon kullanarak çözmeleri bu algoritmalar
için önemli bir ayırt edici özelliktir. Bu çalışmada, optimizasyon alanında
verimliliği kanıtlanmış iki evrimsel algoritma; yarasa algoritması ve klonal seçim
algoritması test fonksiyonları kullanılarak kıyaslanmıştır. Kıyaslama yapılan
test fonksiyonlarından elde edilen sonuçlara göre, yarasa algoritması klonal
seçim algoritmasına göre daha iyi bir performans göstermiştir. Ayrıca, yarasa
algoritması optimizasyonun ilk safhalarında dahi yüksek çözüm kalitesine
ulaşmıştır. Bu analiz, gelecek çalışmalar için evrimsel algoritmaların
performans kıyaslamaları açısından 
rehber olarak kullanılabilir niteliktedir.

Anahtar Kelimeler

Klonal seçim algoritması, Yarasa Algoritması, Evrimsel programlama, Optimizasyon

Performance Study of Bat Algorithm and Clonal Selection Algorithm for Optimization Tasks

Öz

Evolutionary algorithms are preferred by many
researchers in different areas for optimization tasks. It is quite important to
find optimum points of problems with less number of iterations. In this paper, performance
analysis of two powerful optimization algorithms; bat algorithm and clonal
selection algorithm are studied using well-known benchmark functions. The
experimental results show that bat algorithm outperforms clonal selection
algorithm on most of the selected problems. It is also seen that bat algorithm
can produce high quality results even at the first stages of iterations. This
paper can be used as guidance of performance comparisons for future studies.

Anahtar Kelimeler

Clonal selection algorithm, Bat algorithm, Evolutionary computation, Optimization

Kaynakça

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