Optimizasyon problemleri için Gelişmiş Salp Sürüsü Algoritması

Salp Sürüsü Algoritması (SSA), Salp sürülerinin biyolojik özelliklerinden ve koloni stratejilerinden ilham alarak geliştirilmiş metasezgisel bir optimizasyon algoritmasıdır. Literatürde SSA ile yapılmış çok çeşitli çalışmalar vardır. Bu çalışmalarda SSA'nın temel dezavantajlarının olduğu vurgulanmıştır. Bunlardan en önemlisi keşif ve sömürü dengesizliğidir. Bu çalışmada Ikeda haritası kullanılarak bir denge operatörü geliştirilmiştir. Bu geliştirme sayesinde SSA algoritmasının performansı artırılarak erken yakınsama ve lokal minimumlara takılma sorunu giderilmeye çalışılmıştır. Önerilen yöntemin başarısını değerlendirmek için on sabit boyutlu benchmark seti ve üç iyi bilinen mühendislik optimizasyon problemi çözülmüştür. Geliştirilen yöntemin güvenilirliği dört iyi bilinen metasezgisel yaklaşımla ve orijinal SSA ile kıyaslanarak doğrulanmıştır. Deneysel çalışma sonuçları, önerilen yöntemin kıyaslanan yöntemlerden daha performanslı olduğunu göstermiştir.

Anahtar Kelimeler:

Salp Sürüsü Algoritması, Mühendislik Tasarım Problemleri, Global Optimizasyon

An advanced Salp Swarm Algorithm for optimization problems

The Salp Swarm Algorithm (SSA) is a metaheuristic optimization algorithm inspired by Salp swarms' biological characteristics and colony strategies. There is a wide variety of studies conducted with SSA in the literature. These studies have revealed some significant disadvantages of SSA, the most critical being the imbalance of exploration and exploitation. In this study, an equilibrium operator has been developed using the Ikeda map. Thanks to this enhancement, the performance of the SSA algorithm has increased, and issues such as premature convergence and local optima have been overcome. To evaluate the proposed method, ten fixed-dimension benchmark problems and three engineering design optimization problems were solved. The proposed method is validated by comparing four well-known metaheuristic approaches and the original SSA. Experimental results demonstrated that the proposed method outperforms the compared methods.

Keywords:

Salp Swarm Algorithm, Engineering Design Problem, Global Optimization,

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