Başarıyı Kıyaslama: Modern Metasezgiseller Karmaşık Mühendislik Problemlerini Nasıl Çözüyor?

Year 2026, Volume: 31 Issue: 1 , 225 - 244 , 10.04.2026

Metin Kalyon , Sibel Arslan

https://doi.org/10.17482/uumfd.1801249

https://izlik.org/JA73WB77UN

Abstract

Metaheuristik optimizasyon algoritmalarındaki son gelişmeler, önemli ve dikkate değer sonuçlar doğurmuştur.Bu metasezgisel yöntemler, ayrıca mühendislik tasarım zorluklarını değerlendirmek için de kullanılabilir. Bu çalışmada son yıllarda geliştirilen 5 metasezgiselin (Yapay Tavşan OptimizasyonuARO, Kara Dul Optimizasyonu-BWO, Çayır Köpeği Optimizasyonu-PDO, Dağ Ceylanı OptimizasyonuMGO ve Kerevit Optimizasyon Algoritması-COA) mühendislik tasarım problemlerindeki başarıları karşılaştırılmıştır. Bildiğimiz kadarıyla, bu çalışma, gerilim/basınç yayı, basınçlı kap, kaynaklı kiriş, hız düşürücü, dişli seti ve üç çubuklu kafes gibi altı tanınmış mühendislik tasarım optimizasyon problemi üzerinde bu beş metaheuristik algoritmanın ilk kapsamlı değerlendirmesini temsil etmektedir. Deneysel sonuçlar ve yakınsama hızları değerlendirildiğinde, bu araştırmada kullanılan metasezgisel tekniklerin sunulan zorluklara karşı etkili bir etkinlik gösterdiği ortaya çıkmaktadır. Elde edilen sonuçlara göre en başarılı algoritma ARO olurken onu sırasıyla BWO, MGO, COA ve PDO takip etmektedir. Gelecekteki araştırmalarda, çeşitli mühendislik zorluklarını ele almak için özellikle ARO olmak üzere farklı metasezgisel tekniklerin kullanılması hedeflenmektedir.

Keywords

Güncel Metasezgisel Algoritmalar , Mühendislik Tasarım Problemleri , Yapay Tavşan Optimizasyonu , Kara Dul Optimizasyonu

BENCHMARKING SUCCESS: HOW MODERN METAHEURISTICS SOLVE COMPLEX ENGINEERING PROBLEMS?

https://izlik.org/JA73WB77UN

Abstract

Recent developments in metaheuristic optimization algorithms have yielded significant and noteworthy results. These metaheuristics can additionally be utilized to evaluate engineering design challenges. In this study, 5 metaheuristics developed in recent years (Artificial Rabbit Optimization-ARO, Black Widow Optimization-BWO, Prairie Dog Optimization-PDO, Mountain Gazelle OptimizationMGO and Crayfish Optimization Algorithm -COA) success in engineering design problems was compared. To the best of our knowledge, this work represents the first comprehensive evaluation of these five metaheuristic algorithms on six well-known engineering design optimization problems: Tension/Compression Spring, Pressure Vessel, Welded Beam, Speed Reducer, Gear Set, and Three-Bar Truss. Upon assessing the experimental outcomes and convergence speeds, it becomes evident that the metaheuristic techniques employed in this research demonstrate effective efficacy against the challenges presented. Based on the obtained results, ARO achieved the highest performance, followed sequentially by BWO, MGO, COA, and PDO. In upcoming research, the goal is to employ additional metaheuristic techniques, particularly ARO, to address various engineering challenges.

Keywords

Metaheuristic Algorithms , Engineering Design Problems , Artificial Rabbit Optimization , Black Widow Optimization

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