Comprehensive Comparative Analysis of GWO and Its Variants for Solving Mechanical Optimization Problems

Year 2025, Volume: 6 Issue: 1, 150 - 169, 19.06.2025

Nermin Ozcan

https://doi.org/10.55546/jmm.1660142

Abstract

The intricacy of decision variables, multiple objectives, and nonlinear restrictions make it difficult to find suitable solutions for mechanical design problems. An alternative approach to these difficult challenges, the Grey Wolf Optimizer (GWO) is recognized for its ease of use, flexibility, scalability, and unique balance between exploration and exploitation. Like every stochastic approach, GWO has drawbacks, though, and numerous enhanced variants have been put up to overcome them. The GWO algorithm and its variants are examined in this investigation. It conducts an experimental comparison of the original approach and its two variations. It examines how the approaches behave with various combinations of parameters. Five mechanical design problems are used to test the algorithms' effectiveness utilizing statistical analysis and search performance. In the literature, the performance of alternative approaches is also contrasted with the ideal outcomes.

Keywords

Grey wolf optimizer , Engineering problem , Mechanical design , Meta-heuristic algorithm , Optimization

Thanks

This study did not benefit from any support.

Mekanik Optimizasyon Problemlerinin Çözümü için GWO ve Varyantlarının Kapsamlı Karşılaştırmalı Analizi

Abstract

Karar değişkenlerinin karmaşıklığı, çoklu hedefler ve doğrusal olmayan kısıtlamalar, mekanik tasarım problemleri için uygun çözümler bulmayı zorlaştırmaktadır. Bu zorlu problemlere alternatif bir yaklaşım olan Gri Kurt Optimizasyonu (GKO), kullanım kolaylığı, esnekliği, ölçeklenebilirliği ve keşif ile sömürü arasındaki benzersiz dengesiyle tanınmaktadır. Her stokastik yaklaşım gibi, GKO'nun da dezavantajları vardır ve bunların üstesinden gelmek için çok sayıda geliştirilmiş varyant ortaya konmuştur. Bu araştırmada GKO algoritması ve varyantları incelenmiştir. Orijinal yaklaşım ve iki varyasyonunun deneysel bir karşılaştırması yapılmaktadır. Yaklaşımların çeşitli parametre kombinasyonlarının performans üzerindeki etkisi incelenmektedir. İstatistiksel analiz ve arama performansı kullanılarak algoritmaların etkinliğini test etmek için beş mekanik tasarım problemi kullanılmıştır. Literatürde, alternatif yaklaşımların performansı da ideal sonuçlarla karşılaştırılmaktadır.

Keywords

Gri kurt optimizasyonu , Mühendislik problemi , Mekanik tasarım , Meta-sezgisel algoritma

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