TY  - JOUR
T1  - Recent Reviews on Topology Optimization
TT  - Topoloji Optimizasyonunda Güncel Yaklaşımlar
AU  - Şen, Fatma Nur
AU  - Can, Mithat Kutay
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.29109/gujsc.1761253
JF  - Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji
JO  - GUJS Part C
PB  - Gazi Üniversitesi
WT  - DergiPark
SN  - 2147-9526
SP  - 1268
EP  - 1285
VL  - 13
IS  - 3
LA  - en
AB  - Gradient-based methods are utilized in traditional topology optimization studies. This approach is based on a point-by-point technique, which depends on the gradient information of the objective functions regarded as independent variables. Despite the fast response, this approach can lead them to find local optimal solutions, but it faces problems in defining the global optimal solutions in large-scale problems or high-degree functions. For this reason, non-gradient methods that give better solutions have been developed to solve these struggles. This study examines the nature-inspired methods developed over the last 25 years, which are called Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, and Artificial Neural Networks, and presents flowcharts to illustrate their principles. These methods are clarified via a cantilever beam. According to the results, methods are compared, and Particle Swarm Optimization can provide a reasonable solution to determine the optimal solution. These comparisons are tabulated and may be a guide for researchers.
KW  - Topology Optimization
KW  - Optimization Algorithms
KW  - Meta-heuristic Algorithms
KW  - Continuum structural topology
N2  - Geleneksel topoloji optimizasyon çalışmalarında eğimli yöntemler olarak belirtilen metotlar kullanılmaktadır. Bu yaklaşım, bir dizi bağımsız değişkene bağlı olarak amaç fonksiyonun türevine dayanan hesaplamalı nokta tekniğine bağlıdır. Bu metotlar, hızlı çözümler vermesine rağmen, kapsamlı işlemler, yüksek mertebeli fonksiyon gibi durumlarda yerel optimum çözümleri bulmakta; küresel sonuçlarda problemlerle karşılaşılmaktadır. Karşılaşılan sorunları çözmek amaçlı daha iyi sonuçlar elde edilen eğimsiz yöntemler geliştirilmiştir. Bu çalışmada, son 25 yılda yapılan eğimsiz yöntemler olarak belirtilen ve doğadan ilham alan Genetik Algoritmalar, Karınca Kolonisi Optimizasyonu, Parçacık Sürüsü Optimizasyonu ve Yapay Sinir Ağları adlı yöntemler hakkında araştırılmalar yapılmış ve çalışma prensipleri akış şeması ile belirtilmiştir. Bu yöntemler, özellikle kiriş örneği ile detaylandırılmıştır. Sonuçlara bağlı olarak, yöntemler karşılaştırılmakta ve Parçacık Sürü Optimizasyonu optimum çözümü belirlemek için uygun bir sonuç sunabilmektedir.Bu karşılaştırmalara dayanarak, araştırmacılara rehber olabilecek kıyaslama tablosu oluşturulmuştur.
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UR  - https://doi.org/10.29109/gujsc.1761253
L1  - https://dergipark.org.tr/tr/download/article-file/5137024
ER  -