YARASA ALGORİTMASI VE KLONAL SEÇİM ALGORİTMASININ OPTİMİZASYON PROBLEMLERİ İLE PERFORMANS ANALİZİ

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

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.

Keywords:

Clonal selection algorithm, Bat algorithm, Evolutionary computation, Optimization,

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