Multi-Objective Optimization of Truss Structures Using NSGA-II and SHAMODE Algorithms

İbrahim Behram Uğur

Article Details

Multi-Objective Optimization of Truss Structures Using NSGA-II and SHAMODE Algorithms

Authors:

İbrahim Behram Uğur

Publisher:

Erciyes University

Main Topics:

Çelik Yapılar | İnşaat Mühendisliğinde Sayısal Modelleme

Journal:

Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi Vol.41 Issue 2, pp.432-446

Document Type:

Article

Publication Date:

8/2025

Document Language:

English

Document ID (Org No.):

2001354060

pISSN:

1012-2354

Keywords:

Abstract:

 This study investigates the performance of Success-History Adaptive Multi-Objective Differential Evolution SHAMODE and Non-dominated Sorting Genetic Algorithm II NSGA-II, methods in solving a large-scale, multi-objective truss optimization problem. The objective is to minimize the structural weight while maximizing displacement performance, subject to stress and displacement constraints. Four widely used performance metrics including Hypervolume, Generational Distance GD, Inverted Generational Distance IGD, and Spacing-to-Extent STE are employed to evaluate the quality and distribution of the Pareto fronts obtained. Results from independent runs show that SHAMODE consistently produces superior Pareto fronts, as evidenced by higher HV values and significantly lower GD and IGD scores compared to NSGA-II. Furthermore, SHAMODE achieves a more uniform distribution of solutions, indicated by its lower STE values. These findings demonstrate SHAMODEs effectiveness and robustness in handling complex structural optimization problems.

 This study investigates the performance of Success-History Adaptive Multi-Objective Differential Evolution SHAMODE and Non-dominated Sorting Genetic Algorithm II NSGA-II, methods in solving a large-scale, multi-objective truss optimization problem. The objective is to minimize the structural weight while maximizing displacement performance, subject to stress and displacement constraints. Four widely used performance metrics including Hypervolume, Generational Distance GD, Inverted Generational Distance IGD, and Spacing-to-Extent STE are employed to evaluate the quality and distribution of the Pareto fronts obtained. Results from independent runs show that SHAMODE consistently produces superior Pareto fronts, as evidenced by higher HV values and significantly lower GD and IGD scores compared to NSGA-II. Furthermore, SHAMODE achieves a more uniform distribution of solutions, indicated by its lower STE values. These findings demonstrate SHAMODEs effectiveness and robustness in handling complex structural optimization problems.

 This study investigates the performance of Success-History Adaptive Multi-Objective Differential Evolution SHAMODE and Non-dominated Sorting Genetic Algorithm II NSGA-II, methods in solving a large-scale, multi-objective truss optimization problem. The objective is to minimize the structural weight while maximizing displacement performance, subject to stress and displacement constraints. Four widely used performance metrics including Hypervolume, Generational Distance GD, Inverted Generational Distance IGD, and Spacing-to-Extent STE are employed to evaluate the quality and distribution of the Pareto fronts obtained. Results from independent runs show that SHAMODE consistently produces superior Pareto fronts, as evidenced by higher HV values and significantly lower GD and IGD scores compared to NSGA-II. Furthermore, SHAMODE achieves a more uniform distribution of solutions, indicated by its lower STE values. These findings demonstrate SHAMODEs effectiveness and robustness in handling complex structural optimization problems.

 Bu çalışmada, büyük ölçekli çok-amaçlı kafes yapı optimizasyonunda Başarı Geçmişine Dayalı Uyarlamalı Çok Amaçlı Diferansiyel Evrim SHAMODE ve İkinci Nesil Sıralamalı Genetik Algoritma NSGA-II yöntemlerinin performansları incelenmiştir. Amaç, kafes sistemin yapısal ağırlığını ve maksimum düğüm noktası deplasmanlarını minimize ederken, gerilme ve deplasman sınırlayıcılarını da sağlamaktır. Bu iki yöntem ile yapılan optimizasyon sonucunda elde edilen Pareto çözümlerin kalitesi ve dağılımı, Hiperhacim HV, Nesilsel Uzaklık GD, Ters Nesilsel Uzaklık IGD ve Aralık-Uzunluk Oranı STE performans ölçütleri kullanılarak değerlendirilmiştir. Farklı çalıştırmalar sonucu elde edilen en iyi ve ortalama değerler incelendiğinde, SHAMODEun, NSGA-IIye kıyasla daha yüksek HV ve daha düşük GD ve IGD değerleri ürettiği görülmüştür. Ayrıca, SHAMODE daha düşük STE değeri ile daha dengeli bir çözüm dağılımı sağlamıştır. Bu sonuçlar, SHAMODEun karmaşık yapısal optimizasyon problemleri için etkili ve sağlam bir yöntem olduğunu ortaya koymaktadır.

Article Details

Multi-Objective Optimization of Truss Structures Using NSGA-II and SHAMODE Algorithms

Authors:

İbrahim Behram Uğur

Publisher:

Erciyes University

Main Topics:

Çelik Yapılar | İnşaat Mühendisliğinde Sayısal Modelleme

Journal:

Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi Vol.41 Issue 2, pp.432-446

Document Type:

Article

Publication Date:

8/2025

Document Language:

English

Document ID (Org No.):

2001354060

pISSN:

1012-2354

SHAMODE

NSGA-II

Multi-objective optimization

TrussSHAMODE

NSGA-II

Çok amaçlı optimizasyon

Kafes yapı

Issues

Article Details

Authors: İbrahim Behram Uğur

Publisher: Erciyes University

Main Topics: Çelik Yapılar | İnşaat Mühendisliğinde Sayısal Modelleme

Journal: Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi Vol.41 Issue 2, pp.432-446

Document Type: Article

Publication Date: 8/2025

Document Language: English

Document ID (Org No.): 2001354060

pISSN: 1012-2354

Issues

Authors:

İbrahim Behram Uğur

Publisher:

Erciyes University

Main Topics:

Çelik Yapılar | İnşaat Mühendisliğinde Sayısal Modelleme

Journal:

Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi Vol.41 Issue 2, pp.432-446

Document Type:

Article

Publication Date:

8/2025

Document Language:

English

Document ID (Org No.):

2001354060

pISSN:

1012-2354