Evaluation of The Performance of Butterfly Optimization Algorithm in Solving High-Dimensional Numerical Optimization Problems

Yıl 2022, Cilt: 4 Sayı: 2, 296 - 303, 26.10.2022

Ceren Baştemur Kaya , Ebubekir Kaya

https://doi.org/10.46387/bjesr.1170933

Cited By: 1

Öz

The butterfly optimization algorithm is one of the metaheuristic algorithms that models the foraging behavior of butterflies. It appears in the literature that it has been used successfully in the solution of many problems. Control parameters and dimension of problem directly affect the performance of a metaheuristic algorithm. When possible problems are evaluated, some may be low-dimensional, while others may be high-dimensional. At the same time, it is expected that metaheuristic algorithms are successful on high-dimensional problems as well. In this study, the performance of the butterfly optimization algorithm is evaluated in solving high-dimensional numerical optimization problems. The performance of butterfly optimization algorithm is compared with artificial bee colony algorithm, harmony search and flower pollination algorithm. Wilcoxon signed-rank test is used to analyze the significance of the results. The results show that the butterfly optimization algorithm is generally more successful than other algorithms in solving high-dimensional numerical optimization problems.

Anahtar Kelimeler

Butterfly Optimization Algorithm, High-Dimensional Problems, Global Optimization, Metaheuristic Algorithms

Yüksek Boyutlu Nümerik Optimizasyon Problemlerinin Çözümünde Kelebek Optimizasyon Algoritmasının Performansının Değerlendirilmesi

Öz

Kelebek optimizasyon algoritması, kelebeklerin yiyecek arama davranışını modelleyen güncel meta-sezgisel algoritmalardan biridir. Pek çok problemin çözümünde başarılı bir şekilde kullanıldığı literatürde görülmektedir. Bir meta sezgisel algoritmanın başarısını, kontrol parametreleri ve problem boyutu doğrudan etkilemektedir. Muhtemel problemler değerlendirildiğinde, bazıları düşük boyutlu iken, bazıları yüksek boyutlu olabilmektedir. Bununla birlikte, meta-sezgisel algoritmaların yüksek boyutlu problemler üzerinde de başarılı olması beklenmektedir. Bu çalışmada, yüksek boyutlu nümerik optimizasyon problemlerinin çözümünde kelebek optimizasyon algoritmasının performansı değerlendirilmiştir. Kelebek optimizasyon algoritması; yapay arı koloni algoritması, armoni arama algoritması ve çiçek tozlaşma algoritması ile karşılaştırılmıştır. Sonuçların anlamlılığının analizi için Wilcoxon işaretli sıralar testi uygulanmıştır. Sonuçlar, çok boyutlu nümerik optimizasyon problemlerinin çözümünde, kelebek optimizasyon algoritmasının diğer algoritmalardan genel olarak daha başarılı olduğunu göstermiştir.

Anahtar Kelimeler

Kelebek Optimizasyon Algoritması, Yüksek Boyutlu Problemler, Global Optimizasyon, Meta-Sezgisel Algoritmalar

Kaynakça

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