Accelerating Multi-Objective Optimization of Composite Structures Using Multi-Fidelity Surrogate Models and Curriculum Learning

The optimization of multilayer composite structures requires balancing mechanical performance, economic efficiency, and computational feasibility. This study introduces an innovative approach that integrates Curriculum Learning (CL) with a multi-fidelity surrogate model to enhance computational efficiency in engineering design. A multi-fidelity strategy is introduced to generate training data efficiently, leveraging a high-fidelity finite element model for accurate simulations and a low-fidelity model to provide a larger dataset at reduced computational cost. Unlike conventional surrogate modeling approaches, the proposed method applies CL to iteratively refine the surrogate model, enabling step-by-step learning of complex structural patterns and improving prediction accuracy. Genetic algorithms (GAs) are then applied to optimize structural parameters while minimizing computational expense. The integration of CL and multi-fidelity modeling allows for a reduction in computational burden while preserving accuracy, demonstrating practical applicability in real-world structural design problems. The effectiveness of this methodology is validated by evaluating Pareto front quality using selected performance indicators. Results demonstrate that the proposed approach reduces optimization burden while achieving accurate predictions, highlighting the benefits of integrating surrogate modeling, multi-fidelity analysis, CL, and GAs for efficient composite structure optimization. This work contributes to the advancement of optimization methodologies by providing a scalable framework applicable to complex engineering problems requiring high computational efficiency.