Graph Neural Network Assisted Genetic Algorithm for Structural Dynamic Response and Parameter Optimization

TL;DR

This paper introduces a hybrid framework combining Graph Neural Networks and Genetic Algorithms to efficiently optimize structural parameters, significantly reducing computational costs while maintaining high accuracy in dynamic response predictions.

Contribution

It presents a novel integration of GNN surrogate models with GA for structural optimization, improving efficiency and accuracy over traditional simulation-based methods.

Findings

Achieves rapid predictions of structural responses with GNN surrogate.

Demonstrates strong convergence and generalization in optimization.

Reduces computational cost compared to conventional FEM and CFD simulations.

Abstract

The optimization of structural parameters, such as mass(m), stiffness(k), and damping coefficient(c), is critical for designing efficient, resilient, and stable structures. Conventional numerical approaches, including Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) simulations, provide high-fidelity results but are computationally expensive for iterative optimization tasks, as each evaluation requires solving the governing equations for every parameter combination. This study proposes a hybrid data-driven framework that integrates a Graph Neural Network (GNN) surrogate model with a Genetic Algorithm (GA) optimizer to overcome these challenges. The GNN is trained to accurately learn the nonlinear mapping between structural parameters and dynamic displacement responses, enabling rapid predictions without repeatedly solving the system equations. A dataset of single-degree-of-freedom (SDOF) system responses is generated using the Newmark Beta method across diverse mass, stiffness, and damping configurations. The GA then searches for globally optimal parameter sets by minimizing predicted displacements and enhancing dynamic stability. Results demonstrate that the GNN and GA framework achieves strong convergence, robust generalization, and significantly reduced computational cost compared to conventional simulations. This approach highlights the effectiveness of combining machine learning surrogates with evolutionary optimization for automated and intelligent structural design.