Automated Generation and Optimization of Geodesic Domes: A New Benchmark Suite for Structural Design Algorithms

KILINÇ M., Shirwa A. M.

International Journal of Steel Structures, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s13296-026-01084-5
  • Dergi Adı: International Journal of Steel Structures
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: Benchmark problem, Finite element analysis, Geodesic dome, Hybrid optimization, Metaheuristic algorithm, Structural optimization
  • Erciyes Üniversitesi Adresli: Evet

Özet

Benchmark problems are essential for evaluating structural optimization algorithms, yet most studies still rely on the classical 120-bar dome, a simplified, idealized geometry. This over-reliance restricts the generality and practical relevance of optimization research on spatial structures. To address this gap, this study introduces three alternative geodesic benchmark models (tetrahedral, octahedral, and icosahedral domes) developed under a unified modeling protocol and AISC–ASD design constraints. Six metaheuristic algorithms [Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC), and a hybrid GA-PSO] were applied within an equal-budget framework across four design scenarios: stress-only, combined stress and lateral deflection, vertical-deflection-only, and fully constrained cases. The proposed benchmarks consistently produce lighter optimal designs than the 120-bar dome under identical conditions, confirming their structural efficiency and suitability for algorithm testing. Rather than ranking algorithms, this work establishes a reproducible, constraint-consistent benchmark suite that enables fair comparisons among optimization methods and provides a richer foundation for future research in structural and metaheuristic optimization.