KAOTİK YILAN OPTİMİZE EDİCİ

Year 2023, , 1122 - 1141, 30.10.2023

Gülnur Yıldızdan

https://doi.org/10.35414/akufemubid.1263731

Cited By: 1

Abstract

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.

Keywords

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

Chaotic Snake Optimizer

Abstract

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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