A comparison performance analysis of eight meta-heuristic algorithms for optimal design of truss structures with static constraints

Nima Khodadadi, Aybike Özyüksel Çiftçioğlu, Seyedali Mirjalili, Antonio Nanni

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number100266
JournalDecision Analytics Journal
Volume8
DOIs
Publication statusPublished - Sept 2023

Keywords

  • Meta-heuristic algorithms
  • Static constraints
  • Stochastic Paint Optimizer
  • Structural optimization
  • Truss structures

Fingerprint

Dive into the research topics of 'A comparison performance analysis of eight meta-heuristic algorithms for optimal design of truss structures with static constraints'. Together they form a unique fingerprint.

Cite this