GRİ KURT OPTİMİZASYONUNDA KURT LİDERİNİN GELİŞTİRİLMESİ

Optimizasyon algoritmalarının geliştirilmesi, çeşitli alanlarda performansı, geliri ve verimliliği artırma, maliyeti düşürme gibi avantajları olduğu için birçok analistin ilgisini çekmektedir. Meta-sezgisel yöntemler arasında yer alan sürü tabanlı optimizasyon algoritmaları genel olarak başarılı oldukları için daha çok tercih edilmektedir. Bu çalışmada Gri Kurt Optimizasyonunda (GWO) kurt lider sınıfı olarak da adlandırılan alfa kurt sınıfı, Balina Optimizasyon Algoritması (WOA) ile iyileştirilmiştir. Bu geliştirilmiş yönteme ILGWO adı verilir. ILGWO'yu değerlendirmek için tek modlu, çok modlu ve sabit boyutlu çok modlu kıyaslama fonksiyonlarından oluşan 23 kıyaslama testi fonksiyonu kullanılmıştır. Önerilen yöntemin 30 kez çalıştırılması sonucunda ortalama uygunluk ve standart sapma değerleri elde edilmiş ve bu sonuçlar literatür ile karşılaştırılmıştır. Literatürde karşılaştırılan algoritmalara göre ILGWO algoritması tek modlu kıyaslama fonksiyonları için 7 fonksiyondan 6'sında, çok modlu kıyaslama fonksiyonları için 6 fonksiyondan 3'ünde ve sabit boyutlu çok modlu kıyaslama fonksiyonları için 10 fonksiyondan 8'inde en uygun sonucu elde etmiştir. Dolayısıyla önerilen algoritma genellikle literatür sonuçlarından daha iyidir. Önerilen ILGWO'nun umut verici olduğu ve çeşitli uygulamalarda uygulanabileceği görülmektedir.

Anahtar Kelimeler:

Gri Kurt Optimizasyonu, Alfa Kurt, Balina Optimizasyon Algoritması, Benchmark Test Fonksiyonları, Grey Wolf Optimization, Alpha Wolf, Whale Optimization Algorithm, Benchmark Test Functions

IMPROVEMENT OF WOLF LEADER IN THE GREY WOLF OPTIMIZATION

The development of optimization algorithms attracts the attention of many analysts as it has advantages such as increasing performance, revenue, and efficiency in various fields, and reducing cost. Swarm-based optimization algorithms, which are among the meta-heuristic methods, are more commonly preferred because they are generally successful. In this study, the alpha wolf class, also called the wolf leader class, in the Grey Wolf Optimization (GWO), has been improved with the Whale Optimization Algorithm (WOA). This improved method is called ILGWO. To evaluate the ILGWO, 23 benchmark test functions, and 10 CEC2019 test functions were used. After running 30 iterations of the suggested algorithm, average fitness and standard deviation values have been acquired; these findings have been compared to the literature. Based on the literature's comparisons of the algorithms, the ILGWO algorithm has achieved the most optimal result in 5 of 7 functions for unimodal benchmark functions, 3 of 6 functions for multimodal benchmark functions, 9 of 10 functions for fixed-dimension multimodal benchmark functions, and 8 of 10 functions for CEC2019 test functions. So the proposed algorithm is generally better than the literature results. It has been found that the suggested ILGWO is encouraging and may be used in a variety of implementations.

Keywords:

Grey Wolf Optimization, Alpha Wolf, Whale Optimization Algorithm, Benchmark Test Functions,

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