Improved Tuna Swarm Optimization Algorithm for Engineering Design Problems

Abstract

Tuna Swarm Optimization (TSO) which is developed by being inspired by the hunting strategies of the tuna fish is a metaheuristic optimization algorithm (MHA). TSO is able to solve some optimization problems successfully. However, TSO has the handicap of having premature convergence and being caught by local minimum trap. This study proposes a mathematical model aiming to eliminate these disadvantages and to increase the performance of TSO. The basic philosophy of the proposed method is not to focus on the best solution but on the best ones. The Proposed algorithm has been compared to six current and popular MHAs in the literature. Using classical test functions to have a preliminary evaluation is a frequently preferred method in the field of optimization. Therefore, first, all the algorithms were applied to ten classical test functions and the results were interpreted through the Wilcoxon statistical test. The results indicate that the proposed algorithm is successful. Following that, all the algorithms were applied to three engineering design problems, which is the main purpose of this article. The original TSO has a weak performance on design problems. With optimal costs like 1.74 in welded beam design problem, 1581.47 in speed reducer design problem, and 38.455 in I-beam design problem, the proposed algorithm has been the most successful one. Such a case leads us to the idea that the proposed method of this article is successful for improving the performance of TSO.

Keywords

• Tuna Swarm Optimization
• Swarm-Based Metaheuristic Algorithm
• Engineering Design Problems

Mühendislik Tasarım Problemleri için Geliştirilmiş Tuna Sürü Optimizasyonu

Öz

Tuna swarm optimization (TSO) Tuna balıklarının avlanma stratejilerinden esinlenilerek geliştirilmiş bir meta-heuristic optimizasyon algoritmasıdır (MHA). TSO, bazı optimizasyon problemini başarıyla çözebilmektedir. Ancak TSO, premature convergence ve local minimum trap yakalanma gibi problemlere sahiptir. Bu çalışmada, TSO’nun bu dezavantajlarını gidermek ve performansını artırmak için geliştirilmiş bir matematiksel model önerilmektedir. Önerilen yöntemin temel felsefesi en iyi çözüme değil en iyi çözümlere odaklanmaktır. Proposed algorithm, literatürdeki güncel ve popüler 6 adet MHA ile karşılaştırılmıştır. Optimizasyon alanında klasik test fonksiyonlarının bir ön değerlendirme yapmak için kullanılması sıklıkla tercih edilen bir yöntemdir. Bu nedenle tüm algoritmalar ilk önce 10 adet klasik test fonksiyonlarına uygulanmış ve sonuçlar wilcoxen istatistik testi ile yorumlanmıştır. Elde edilen sonuçlar önerilen algoritmanın başarılı olduğunu göstermektedir. Daha sonra tüm algoritmalar, bu makalenin asıl amacı olan 3 adet mühendislik tasarım problemine uygulanmıştır. Orijinal TSO, tasarım problemlerinde kötü bir performansa sahiptir. Buna karşın proposed algorithm oldukça rekabetçi sonuçlar elde etmektedir. Bu durum, TSO’nun performansını artırmak için bu makalede önerilen yöntemin başarılı olduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Tuna sürü optimizasyonu
• Sürü tabanlı meta sezgisel algoritmalar
• Mühendislik tasarım problemleri

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