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BIN_MRFOA: İkili Optimizasyon İçin Yeni Bir Manta Vatozu Beslenme Optimizasyonu Algoritması

Optimizasyon problemleri üç farklı yapıda ortaya çıkar: sürekli, ayrık ve hibrit. Günümüzde optimizasyon problemlerinin çözümünde sıklıkla tercih edilen metasezgisel algoritmalar daha çok sürekli problemler için önerilmiş ve sonraki modifikasyonlarla ayrıklaştırılmıştır. Bu çalışmada, daha önce sürekli optimizasyon problemlerinin çözümünde sıklıkla kullanılan manta vatozu beslenme optimizasyon algoritmasının yeni bir ikili versiyonu (Bin_MRFOA), ikili optimizasyon problemlerinin çözümünde kullanılmak üzere önerilmiştir. Bin_MRFOA ilk olarak on tane klasik kıyaslama fonksiyon üzerinde test edilmiş ve sekiz farklı transfer fonksiyonu kullanılarak elde edilen varyantlar karşılaştırılarak transfer fonksiyonunun performans üzerindeki etkisi incelenmiştir. Ardından en başarılı Bin_MRFOA varyantı, on sekiz CEC2005 kıyaslama fonksiyonu üzerinde çalıştırılmıştır. Sonuçlar literatürdeki algoritmalar ile karşılaştırılmış ve parametrik olmayan Friedman testi ile yorumlanmıştır. Sonuçlar, Bin_MRFOA'nın literatüre kıyasla başarılı, rekabetçi ve tercih edilebilir bir algoritma olduğunu ortaya koymuştur.

Anahtar Kelimeler:

İkili optimizasyon, Manta vatozu beslenme optimizasyonu, S-şekilli ve V-şekilli transfer fonksiyonları

Bin_MRFOA: A NOVEL MANTA RAY FORAGING OPTIMIZATION ALGORITHM FOR BINARY OPTIMIZATION

Optimization problems occur in three different structures: continuous, discrete, and hybrid. Metaheuristic algorithms, which are frequently preferred in the solution of optimization problems today, are mostly proposed for continuous problems and are discretized with subsequent modifications. In this study, a novel binary version (Bin_MRFOA) of the manta ray foraging optimization algorithm, which was frequently used in the solution of continuous optimization problems before, was proposed to be used in the solution of binary optimization problems. The Bin_MRFOA was first tested on ten classical benchmark functions, and the effect of the transfer function on performance was examined by comparing the variants obtained using eight different transfer functions. Then the most successful Bin_MRFOA variant was run on the eighteen CEC2005 benchmark functions. The results were compared with the algorithms in the literature and interpreted with Wilcoxon signed-rank and Friedman tests, which are nonparametric tests. The results revealed that Bin_MRFOA is a successful, competitive, and preferable algorithm compared to the literature.

Keywords:

Binary Optimization, Manta Ray Foraging Optimization, S-shaped and V-shaped Transfer Functions,

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