Solving of CEC 2019 Problem Set and Engineering Problems with the Snake Optimization Algorithm

Öz

Meta-heuristic algorithms are commonly used in solving optimization problems. In this study, the Snake Optimization (SO) Algorithm is employed to solve the CEC-2019 problem set. The obtained results are compared with other optimization algorithms that are frequently used in the literature and have achieved recent successful outcomes, including the Coati Optimization Algorithm (COA), Bernstein-Search Differential Evolution (BSDE), Oppositional-Mutual Learning Differential Evolution (OMLDE), Weighted Differential Evolution (WDE), Particle Swarm Optimization (PSO), Success-History Adaptation Differential Evolution (SHADE), Adaptive Differential Evolution with Optional External Archive (JADE), Covariance Matrix Adaptation Evolution Strategy (CMAES), and Bernstein Operator and Refracted Oppositional Mutual Learning Differential Evolution (BROMLDE). In the initialization phase, the Latin hypercube technique is used to explore the solution space more effectively and accelerate convergence to the optimal solution. The performance of the developed Snake Algorithm is evaluated in solving the CEC 2019 problem set. The Friedman test is used to analyze the results. According to the results, the Snake Algorithm outperforms its competitors in 6 out of 10 problems. Additionally, two engineering problems are solved using the Snake Optimization algorithm. The results of the engineering problem solutions are compared with the results of other optimization algorithms in the literature. In conclusion, the Snake Algorithm provides competitive results when compared to other optimization algorithms.

Anahtar Kelimeler

• Snake Optimization Algorithm
• Latin Hypercube Sampling
• CEC 2019
• Gear Train Design Problem
• Tubular Column Design Problem

Yılan Optimizasyon Algoritması ile CEC 2019 Problem Seti ve Mühendislik Problemlerinin Çözümü

Öz

Optimizasyon problemlerinin çözümünde Meta-sezgisel algoritmaların kullanımı yaygındır. Bu çalışmada, CEC-2019 problem setinin çözümünde Yılan Optimizasyonu (Snake Optimization, SO) Algoritması kullanılmaktadır. Elde edilen sonuçlar literatürde sık kullanılan, güncel ve başarılı sonuçlar elde edilmiş diğer optimizasyon algoritmalarından Koati Optimizasyon Algoritması (Coati Optimization Algorithm, COA), Bernstein Arama DE Algoritması (Bernstein-Search Differential Evolution, BSDE), Karşıt-Karşılıklı Öğrenme DE (Oppositional-Mutual Learning Differential Evolution, OMLDE), Ağırlıklı Diferansiyel Evrim (Weighted Differential Evolution, WDE), Parçacık Sürü Optimizasyonu (Particle Swarm Optimization, PSO), Başarı Geçmişi Uyarlaması DE (Success-History Adaptation Differential Evolution, SHADE), İsteğe Bağlı Harici Arşivle Uyarlanabilir DE (Adaptive Differential Evolution with Optional External Archive, JADE), Kovaryans Matrisi Uyarlama Evrim Stratejisi (Covariance Matrix Adaptation Evolution Strategy, CMAES) ve Bernstein Operatörü ve Kırılmış Karşıt Karşılıklı Öğrenme Diferansiyel Evrim (Bernstein Operator and Refracted Oppositional Mutual Learning Differential Evolution, BROMLDE) ile karşılaştırılmaktadır. İlklendirme aşamasında, çözüm uzayını daha iyi keşfetmesi adına ve optimum çözüme yakınsamasının hızlandırılması açısından Latin hiperküp tekniği kullanılmaktadır. Geliştirilen yılan algoritmasının performansı CEC 2019 problem çözümünde değerlendirilmektedir. Sonuçları analiz etmek için Friedman testi kullanılmaktadır. Elde edilen sonuçlara göre Yılan algoritmasının 10 problemden 6’sında rakiplerinden daha iyi sonuçlar verdiği görülmektedir. Ek olarak Yılan Optimizasyonu ile iki adet mühendislik problemi çözümü yapılmaktadır. Mühendislik problemi çözüm sonuçları literatürde bulunan diğer optimizasyon algoritmaları sonuçları ile kıyaslanmaktadır. Sonuç olarak Yılan Algoritması diğer optimizasyon algoritmaları ile karşılaştırıldığında rekabetçi sonuçlar vermektedir.

Anahtar Kelimeler

• Yılan Optimizasyon Algoritması
• Latin Hiperküp Örnekleme
• CEC 2019
• Dişli Sistemi Tasarımı Problemi
• Borulu Kolon Tasarımı

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