KAOTİK YILAN OPTİMİZE EDİCİ

Metasezgisel algoritmalar, optimizasyon problemlerine makul bir sürede yaklaşık veya optimal çözümler sunar. Bu özelliği ile metasezgisel algoritmalar zor optimizasyon problemlerini çözmek için etkileyici bir araştırma alanı haline gelmiştir. Yılan Optimize Edici, yılanların çiftleşme davranışlarından esinlenen popülasyon tabanlı bir metasezgisel algoritmadır. Bu çalışmada, Yılan Optimize Edicinin performansını iyileştirmek için rastgele sayı dizileri yerine algoritmanın parametrelerine farklı kaotik haritalar entegre edilmiş ve dört farklı kaotik haritalama kullanılarak Yılan Optimize Edici varyantları önerilmiştir. Önerilen bu varyantların sekiz farklı kaotik harita için performansları klasik ve CEC2019 test fonksiyonları üzerinde incelenmiştir. Sonuçlar, önerilen algoritmaların Yılan Optimize Edici performansının iyileştirilmesine katkıda bulunduğunu ortaya koydu. Literatür ile karşılaştırıldığında önerilen Kaotik Optimize Edici algoritması birçok fonksiyonda en iyi ortalama değerleri bulmuş ve algoritmalar arasında ikinci sırada yer almıştır. Yapılan testler sonucunda, Kaotik Yılan Optimize Edicinin gelecek vadeden, başarılı ve tercih edilebilir bir algoritma olduğu görülmüştür.

Anahtar Kelimeler:

Kaotik haritalar, Yılan optimize edici, Metasezgisel algoritmalar, Sürekli optimizasyon

Chaotic Snake Optimizer

Metaheuristic algorithms provide approximate or optimal solutions for optimization problems in a reasonable time. With this feature, metaheuristic algorithms have become an impressive research area for solving difficult optimization problems. Snake Optimizer is a population-based metaheuristic algorithm inspired by the mating behavior of snakes. In this study, different chaotic maps were integrated into the parameters of the algorithm instead of random number sequences to improve the performance of Snake Optimizer, and Snake Optimizer variants using four different chaotic mappings were proposed. The performances of these proposed variants for eight different chaotic maps were examined on classical and CEC2019 test functions. The results revealed that the proposed algorithms contribute to the improvement of Snake Optimizer performance. In the comparison with the literature, the proposed Chaotic Snake Optimizer algorithm found the best mean values in many functions and took second place among the algorithms. As a result of the tests, Chaotic Snake Optimizer has been shown to be a promising, successful, and preferable algorithm.

Keywords:

Chaotic maps, Snake optimizer, Metaheuristic algorithms, Continuous optimization,

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