TY - JOUR
T1 - A comparison performance analysis of eight meta-heuristic algorithms for optimal design of truss structures with static constraints
AU - Khodadadi, Nima
AU - Çiftçioğlu, Aybike Özyüksel
AU - Mirjalili, Seyedali
AU - Nanni, Antonio
N1 - Funding Information:
The authors gratefully acknowledge the financial support from the National Science Foundation I/U-CRC Center for Integration of Composites into Infrastructure (CICI) under grant #1916342 .
Publisher Copyright:
© 2023
PY - 2023/9
Y1 - 2023/9
N2 - Metaheuristics have been successfully used for solving complex structural optimization problems. Many algorithms are proposed for truss structure size and shape optimization under some constraints. This study considers eight population-based meta-heuristic methods: African Vultures Optimization Algorithm (AVOA), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), Generalized Normal Distribution Optimization (GNDO), Stochastic Paint Optimizer (SPO), Chaos Game Optimizer (CGO), Crystal Structure Algorithm (CRY) and Material Generation Algorithm (MGO). These meta-heuristics methods are used to optimize the size of three aluminum truss structures. Optimization aims to reduce the weight of the truss members while meeting a set of displacement and stress constraints. The performance of these methods is assessed by solving and optimizing three well-known truss structure benchmarks under some constraints. The results show that the Stochastic Paint Optimizer (SPO) outperforms the other algorithms in terms of accuracy and convergence rate.
AB - Metaheuristics have been successfully used for solving complex structural optimization problems. Many algorithms are proposed for truss structure size and shape optimization under some constraints. This study considers eight population-based meta-heuristic methods: African Vultures Optimization Algorithm (AVOA), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), Generalized Normal Distribution Optimization (GNDO), Stochastic Paint Optimizer (SPO), Chaos Game Optimizer (CGO), Crystal Structure Algorithm (CRY) and Material Generation Algorithm (MGO). These meta-heuristics methods are used to optimize the size of three aluminum truss structures. Optimization aims to reduce the weight of the truss members while meeting a set of displacement and stress constraints. The performance of these methods is assessed by solving and optimizing three well-known truss structure benchmarks under some constraints. The results show that the Stochastic Paint Optimizer (SPO) outperforms the other algorithms in terms of accuracy and convergence rate.
KW - Meta-heuristic algorithms
KW - Static constraints
KW - Stochastic Paint Optimizer
KW - Structural optimization
KW - Truss structures
UR - http://www.scopus.com/inward/record.url?scp=85163178823&partnerID=8YFLogxK
U2 - 10.1016/j.dajour.2023.100266
DO - 10.1016/j.dajour.2023.100266
M3 - Article
AN - SCOPUS:85163178823
SN - 2772-6622
VL - 8
JO - Decision Analytics Journal
JF - Decision Analytics Journal
M1 - 100266
ER -